librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-15 06:27:42	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 521 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 06:27:26	card stringlengths 11 1.01M
GoYM/gemma-product-description	GoYM	2025-05-27T19:33:15Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-pt", "base_model:finetune:google/gemma-3-4b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-16T03:32:47Z	--- base_model: google/gemma-3-4b-pt library_name: transformers model_name: gemma-product-description tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-product-description This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="GoYM/gemma-product-description", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
smartmyapp/ladji5_2	smartmyapp	2025-05-27T19:33:13Z	0	0	transformers	[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-27T12:17:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
zidsi/Zlatorog-12B-Instruct-Beta-GGUF	zidsi	2025-05-27T19:33:05Z	9	0	transformers	[ "transformers", "gguf", "full", "generated_from_trainer", "text-generation", "sl", "en", "base_model:zidsi/MistralNemoCPT6", "base_model:quantized:zidsi/MistralNemoCPT6", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-23T15:41:35Z	--- library_name: transformers license: cc-by-nc-nd-4.0 base_model: zidsi/MistralNemoCPT6 tags: - full - generated_from_trainer model-index: - name: zlatorog_12b_sft_v6 results: [] language: - sl - en pipeline_tag: text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Zlatorog-12B-Instruct-Beta This model is a fine-tuned version of [zidsi/MistralNemoCPT6](https://huggingface.co/zidsi/MistralNemoCPT6) on the custom mix of SFT datasets. ## Model description More information needed ## Intended uses & limitations Research explore and have fun with Slovenian LLM :) ## Training and evaluation data Bad standard Slovenian benchmarks results but sometimes impresssive "real world" prompt responses :) Reduced hallucinations rate on "Who is ...?" prompts. Tools use to be evaluated Up to 16k ctx should work OK, for longer contexts training data would be required to improve CPT Long stage More information needed ## GGUF The HF model was coverted to GGUF using llama.cpp
yutakas/llava-v1.6-mistral-7b-hf-test	yutakas	2025-05-27T19:32:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T18:49:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
vermoney/cdc5327d-3aaa-49a2-a63f-78fc847a8490	vermoney	2025-05-27T19:32:22Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-27T18:35:14Z	--- library_name: peft license: apache-2.0 base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - axolotl - generated_from_trainer model-index: - name: cdc5327d-3aaa-49a2-a63f-78fc847a8490 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e9539959e5b475cc_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/cdc5327d-3aaa-49a2-a63f-78fc847a8490 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/e9539959e5b475cc_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 074d0027-87b6-4ea0-a8be-5f7675bf7878 wandb_project: s56-9 wandb_run: your_name wandb_runid: 074d0027-87b6-4ea0-a8be-5f7675bf7878 warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # cdc5327d-3aaa-49a2-a63f-78fc847a8490 This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9817 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7481 \| 0.0132 \| 280 \| 0.9817 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JasperV13/yehia-7b-CoT	JasperV13	2025-05-27T19:31:54Z	0	0	null	[ "yehia_reasoning", "arabic", "reasoning", "cot", "chain-of-thought", "yehia", "custom_code", "ar", "base_model:Navid-AI/Yehia-7B-preview", "base_model:finetune:Navid-AI/Yehia-7B-preview", "license:apache-2.0", "region:us" ]	null	2025-05-27T19:31:53Z	--- language: ar license: apache-2.0 tags: - arabic - reasoning - cot - chain-of-thought - yehia base_model: Navid-AI/Yehia-7B-preview --- # Yehia-7B Chain of Thought Model نموذج يهيا المحسن بتقنية التفكير المتسلسل (Chain of Thought) ## الوصف هذا النموذج يطبق تقنية التفكير المتسلسل تلقائياً عند طرح أي سؤال. يقوم النموذج بتقسيم المشكلة إلى خطوات منطقية متتابعة للوصول إلى الحل الصحيح. ## الاستخدام ```python from transformers import AutoModel, AutoTokenizer # تحميل النموذج model = AutoModel.from_pretrained("JasperV13/yehia-7b-CoT", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("JasperV13/yehia-7b-CoT") # طرح سؤال question = "احسب 25 × 16" inputs = tokenizer(question, return_tensors="pt") outputs = model.generate(inputs) answer = tokenizer.decode(outputs[0], skip_special_tokens=True) print(answer) ``` ## مثال على النتيجة ``` السؤال: احسب 25 × 16 دعني أحل هذا السؤال خطوة بخطوة: الخطوة 1: سأقوم بضرب 25 في 16 الخطوة 2: يمكنني تقسيم هذا إلى (20 + 5) × 16 الخطوة 3: = (20 × 16) + (5 × 16) الخطوة 4: = 320 + 80 الخطوة 5: = 400 الإجابة النهائية: 400 ``` ## المميزات - ✅ تفكير خطوة بخطوة تلقائي - ✅ محسن للغة العربية - ✅ متوافق مع مكتبة transformers - ✅ دقة عالية في الحسابات والمنطق - ✅ سهولة الاستخدام ## متطلبات ```bash pip install transformers torch ``` ## النموذج الأساسي يستخدم هذا النموذج `Navid-AI/Yehia-7B-preview` كنموذج أساسي مع إضافة طبقة التفكير المتسلسل. ## الاستخدام المتقدم ```python # للحصول على تحكم أكبر في المعاملات from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("JasperV13/yehia-7b-CoT", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("JasperV13/yehia-7b-CoT") # يمكنك تخصيص معاملات التوليد question = "كيف أحل معادلة من الدرجة الثانية؟" inputs = tokenizer(question, return_tensors="pt") # مع معاملات مخصصة outputs = model.generate( inputs, max_length=500, temperature=0.7, do_sample=True ) answer = tokenizer.decode(outputs[0], skip_special_tokens=True) print(answer) ``` ## الإصدار - الإصدار: 1.0 - تاريخ الإنشاء: 2025 - المطور: JasperV13 ## الترخيص Apache 2.0
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task-aware/Llama_3.2_3B_Instruct	task-aware	2025-05-27T19:30:12Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T16:38:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
goalaphx/outputs_qcm_then_fitb	goalaphx	2025-05-27T19:26:42Z	0	0	peft	[ "peft", "safetensors", "gguf", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-27T19:20:27Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
shishirahm3d/lawyer	shishirahm3d	2025-05-27T19:26:05Z	4	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-27T03:30:50Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: shishirahm3d - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
Alexhuou/MNLP_M2_document_encoder	Alexhuou	2025-05-27T19:25:39Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "mteb", "sentence-similarity", "Sentence Transformers", "en", "arxiv:2308.03281", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-27T19:22:29Z	--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.62686567164178 - type: ap value: 34.46944126809772 - type: f1 value: 66.23684353950857 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.51805 - type: ap value: 89.49842783330848 - type: f1 value: 92.51112169431808 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.074 - type: f1 value: 48.44785682572955 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.077 - type: map_at_10 value: 48.153 - type: map_at_100 value: 48.963 - type: map_at_1000 value: 48.966 - type: map_at_3 value: 43.184 - type: map_at_5 value: 46.072 - type: mrr_at_1 value: 33.073 - type: mrr_at_10 value: 48.54 - type: mrr_at_100 value: 49.335 - type: mrr_at_1000 value: 49.338 - type: mrr_at_3 value: 43.563 - type: mrr_at_5 value: 46.383 - type: ndcg_at_1 value: 32.077 - type: ndcg_at_10 value: 57.158 - type: ndcg_at_100 value: 60.324999999999996 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 46.934 - type: ndcg_at_5 value: 52.158 - type: precision_at_1 value: 32.077 - type: precision_at_10 value: 8.591999999999999 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.275000000000002 - type: precision_at_5 value: 14.111 - type: recall_at_1 value: 32.077 - type: recall_at_10 value: 85.917 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 57.824 - type: recall_at_5 value: 70.555 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.619246083417295 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.3574067664688 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.06359661829253 - type: mrr value: 76.15596007562766 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 90.25407547368691 - type: cos_sim_spearman value: 88.65081514968477 - type: euclidean_pearson value: 88.14857116664494 - type: euclidean_spearman value: 88.50683596540692 - type: manhattan_pearson value: 87.9654797992225 - type: manhattan_spearman value: 88.21164851646908 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.05844155844157 - type: f1 value: 86.01555597681825 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.10510519739522 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.84689960264385 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.800000000000004 - type: map_at_10 value: 44.857 - type: map_at_100 value: 46.512 - type: map_at_1000 value: 46.635 - type: map_at_3 value: 41.062 - type: map_at_5 value: 43.126 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 50.879 - type: mrr_at_100 value: 51.605000000000004 - type: mrr_at_1000 value: 51.641000000000005 - type: mrr_at_3 value: 48.14 - type: mrr_at_5 value: 49.835 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 51.819 - type: ndcg_at_100 value: 57.318999999999996 - type: ndcg_at_1000 value: 58.955999999999996 - type: ndcg_at_3 value: 46.409 - type: ndcg_at_5 value: 48.825 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 10.072000000000001 - type: precision_at_100 value: 1.625 - type: precision_at_1000 value: 0.21 - type: precision_at_3 value: 22.556 - type: precision_at_5 value: 16.309 - type: recall_at_1 value: 32.800000000000004 - type: recall_at_10 value: 65.078 - type: recall_at_100 value: 87.491 - type: recall_at_1000 value: 97.514 - type: recall_at_3 value: 49.561 - type: recall_at_5 value: 56.135999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.614 - type: map_at_10 value: 43.578 - type: map_at_100 value: 44.897 - type: map_at_1000 value: 45.023 - type: map_at_3 value: 40.282000000000004 - type: map_at_5 value: 42.117 - type: mrr_at_1 value: 40.510000000000005 - type: mrr_at_10 value: 49.428 - type: mrr_at_100 value: 50.068999999999996 - type: mrr_at_1000 value: 50.111000000000004 - type: mrr_at_3 value: 47.176 - type: mrr_at_5 value: 48.583999999999996 - type: ndcg_at_1 value: 40.510000000000005 - type: ndcg_at_10 value: 49.478 - type: ndcg_at_100 value: 53.852 - type: ndcg_at_1000 value: 55.782 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 47.19 - type: precision_at_1 value: 40.510000000000005 - type: precision_at_10 value: 9.363000000000001 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 21.741 - type: precision_at_5 value: 15.465000000000002 - type: recall_at_1 value: 32.614 - type: recall_at_10 value: 59.782000000000004 - type: recall_at_100 value: 78.012 - type: recall_at_1000 value: 90.319 - type: recall_at_3 value: 46.825 - type: recall_at_5 value: 52.688 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.266000000000005 - type: map_at_10 value: 53.756 - type: map_at_100 value: 54.809 - type: map_at_1000 value: 54.855 - type: map_at_3 value: 50.073 - type: map_at_5 value: 52.293 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 57.116 - type: mrr_at_100 value: 57.767 - type: mrr_at_1000 value: 57.791000000000004 - type: mrr_at_3 value: 54.461999999999996 - type: mrr_at_5 value: 56.092 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 60.092 - type: ndcg_at_100 value: 64.034 - type: ndcg_at_1000 value: 64.937 - type: ndcg_at_3 value: 54.071000000000005 - type: ndcg_at_5 value: 57.254000000000005 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.799 - type: precision_at_100 value: 1.278 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.368000000000002 - type: precision_at_5 value: 16.89 - type: recall_at_1 value: 40.266000000000005 - type: recall_at_10 value: 75.41499999999999 - type: recall_at_100 value: 92.01700000000001 - type: recall_at_1000 value: 98.379 - type: recall_at_3 value: 59.476 - type: recall_at_5 value: 67.297 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.589 - type: map_at_10 value: 37.755 - type: map_at_100 value: 38.881 - type: map_at_1000 value: 38.954 - type: map_at_3 value: 34.759 - type: map_at_5 value: 36.544 - type: mrr_at_1 value: 30.734 - type: mrr_at_10 value: 39.742 - type: mrr_at_100 value: 40.774 - type: mrr_at_1000 value: 40.824 - type: mrr_at_3 value: 37.137 - type: mrr_at_5 value: 38.719 - type: ndcg_at_1 value: 30.734 - type: ndcg_at_10 value: 42.978 - type: ndcg_at_100 value: 48.309000000000005 - type: ndcg_at_1000 value: 50.068 - type: ndcg_at_3 value: 37.361 - type: ndcg_at_5 value: 40.268 - type: precision_at_1 value: 30.734 - type: precision_at_10 value: 6.565 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.096 - type: recall_at_1 value: 28.589 - type: recall_at_10 value: 57.126999999999995 - type: recall_at_100 value: 81.051 - type: recall_at_1000 value: 94.027 - type: recall_at_3 value: 42.045 - type: recall_at_5 value: 49.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.5 - type: map_at_10 value: 27.950999999999997 - type: map_at_100 value: 29.186 - type: map_at_1000 value: 29.298000000000002 - type: map_at_3 value: 25.141000000000002 - type: map_at_5 value: 26.848 - type: mrr_at_1 value: 22.637 - type: mrr_at_10 value: 32.572 - type: mrr_at_100 value: 33.472 - type: mrr_at_1000 value: 33.533 - type: mrr_at_3 value: 29.747 - type: mrr_at_5 value: 31.482 - type: ndcg_at_1 value: 22.637 - type: ndcg_at_10 value: 33.73 - type: ndcg_at_100 value: 39.568 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.505999999999997 - type: ndcg_at_5 value: 31.255 - type: precision_at_1 value: 22.637 - type: precision_at_10 value: 6.281000000000001 - type: precision_at_100 value: 1.073 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 13.847000000000001 - type: precision_at_5 value: 10.224 - type: recall_at_1 value: 18.5 - type: recall_at_10 value: 46.744 - type: recall_at_100 value: 72.072 - type: recall_at_1000 value: 91.03999999999999 - type: recall_at_3 value: 32.551 - type: recall_at_5 value: 39.533 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.602 - type: map_at_10 value: 42.18 - type: map_at_100 value: 43.6 - type: map_at_1000 value: 43.704 - type: map_at_3 value: 38.413000000000004 - type: map_at_5 value: 40.626 - type: mrr_at_1 value: 37.344 - type: mrr_at_10 value: 47.638000000000005 - type: mrr_at_100 value: 48.485 - type: mrr_at_1000 value: 48.52 - type: mrr_at_3 value: 44.867000000000004 - type: mrr_at_5 value: 46.566 - type: ndcg_at_1 value: 37.344 - type: ndcg_at_10 value: 48.632 - type: ndcg_at_100 value: 54.215 - type: ndcg_at_1000 value: 55.981 - type: ndcg_at_3 value: 42.681999999999995 - type: ndcg_at_5 value: 45.732 - type: precision_at_1 value: 37.344 - type: precision_at_10 value: 8.932 - type: precision_at_100 value: 1.376 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 20.276 - type: precision_at_5 value: 14.726 - type: recall_at_1 value: 30.602 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 85.12100000000001 - type: recall_at_1000 value: 96.439 - type: recall_at_3 value: 45.848 - type: recall_at_5 value: 53.615 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.952 - type: map_at_10 value: 35.177 - type: map_at_100 value: 36.59 - type: map_at_1000 value: 36.703 - type: map_at_3 value: 31.261 - type: map_at_5 value: 33.222 - type: mrr_at_1 value: 29.337999999999997 - type: mrr_at_10 value: 40.152 - type: mrr_at_100 value: 40.963 - type: mrr_at_1000 value: 41.016999999999996 - type: mrr_at_3 value: 36.91 - type: mrr_at_5 value: 38.685 - type: ndcg_at_1 value: 29.337999999999997 - type: ndcg_at_10 value: 41.994 - type: ndcg_at_100 value: 47.587 - type: ndcg_at_1000 value: 49.791000000000004 - type: ndcg_at_3 value: 35.27 - type: ndcg_at_5 value: 38.042 - type: precision_at_1 value: 29.337999999999997 - type: precision_at_10 value: 8.276 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.161 - type: precision_at_5 value: 12.671 - type: recall_at_1 value: 23.952 - type: recall_at_10 value: 57.267 - type: recall_at_100 value: 80.886 - type: recall_at_1000 value: 95.611 - type: recall_at_3 value: 38.622 - type: recall_at_5 value: 45.811 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.092083333333335 - type: map_at_10 value: 37.2925 - type: map_at_100 value: 38.57041666666666 - type: map_at_1000 value: 38.68141666666667 - type: map_at_3 value: 34.080000000000005 - type: map_at_5 value: 35.89958333333333 - type: mrr_at_1 value: 31.94758333333333 - type: mrr_at_10 value: 41.51049999999999 - type: mrr_at_100 value: 42.36099999999999 - type: mrr_at_1000 value: 42.4125 - type: mrr_at_3 value: 38.849583333333335 - type: mrr_at_5 value: 40.448249999999994 - type: ndcg_at_1 value: 31.94758333333333 - type: ndcg_at_10 value: 43.17633333333333 - type: ndcg_at_100 value: 48.45241666666668 - type: ndcg_at_1000 value: 50.513999999999996 - type: ndcg_at_3 value: 37.75216666666667 - type: ndcg_at_5 value: 40.393833333333326 - type: precision_at_1 value: 31.94758333333333 - type: precision_at_10 value: 7.688916666666666 - type: precision_at_100 value: 1.2250833333333333 - type: precision_at_1000 value: 0.1595 - type: precision_at_3 value: 17.465999999999998 - type: precision_at_5 value: 12.548083333333333 - type: recall_at_1 value: 27.092083333333335 - type: recall_at_10 value: 56.286583333333326 - type: recall_at_100 value: 79.09033333333333 - type: recall_at_1000 value: 93.27483333333335 - type: recall_at_3 value: 41.35325 - type: recall_at_5 value: 48.072750000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.825 - type: map_at_10 value: 33.723 - type: map_at_100 value: 34.74 - type: map_at_1000 value: 34.824 - type: map_at_3 value: 31.369000000000003 - type: map_at_5 value: 32.533 - type: mrr_at_1 value: 29.293999999999997 - type: mrr_at_10 value: 36.84 - type: mrr_at_100 value: 37.681 - type: mrr_at_1000 value: 37.742 - type: mrr_at_3 value: 34.79 - type: mrr_at_5 value: 35.872 - type: ndcg_at_1 value: 29.293999999999997 - type: ndcg_at_10 value: 38.385999999999996 - type: ndcg_at_100 value: 43.327 - type: ndcg_at_1000 value: 45.53 - type: ndcg_at_3 value: 33.985 - type: ndcg_at_5 value: 35.817 - type: precision_at_1 value: 29.293999999999997 - type: precision_at_10 value: 6.12 - type: precision_at_100 value: 0.9329999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 14.621999999999998 - type: precision_at_5 value: 10.030999999999999 - type: recall_at_1 value: 25.825 - type: recall_at_10 value: 49.647000000000006 - type: recall_at_100 value: 72.32300000000001 - type: recall_at_1000 value: 88.62400000000001 - type: recall_at_3 value: 37.366 - type: recall_at_5 value: 41.957 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.139 - type: map_at_10 value: 26.107000000000003 - type: map_at_100 value: 27.406999999999996 - type: map_at_1000 value: 27.535999999999998 - type: map_at_3 value: 23.445 - type: map_at_5 value: 24.916 - type: mrr_at_1 value: 21.817 - type: mrr_at_10 value: 29.99 - type: mrr_at_100 value: 31.052000000000003 - type: mrr_at_1000 value: 31.128 - type: mrr_at_3 value: 27.627000000000002 - type: mrr_at_5 value: 29.005 - type: ndcg_at_1 value: 21.817 - type: ndcg_at_10 value: 31.135 - type: ndcg_at_100 value: 37.108000000000004 - type: ndcg_at_1000 value: 39.965 - type: ndcg_at_3 value: 26.439 - type: ndcg_at_5 value: 28.655 - type: precision_at_1 value: 21.817 - type: precision_at_10 value: 5.757000000000001 - type: precision_at_100 value: 1.036 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 12.537 - type: precision_at_5 value: 9.229 - type: recall_at_1 value: 18.139 - type: recall_at_10 value: 42.272999999999996 - type: recall_at_100 value: 68.657 - type: recall_at_1000 value: 88.93799999999999 - type: recall_at_3 value: 29.266 - type: recall_at_5 value: 34.892 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.755000000000003 - type: map_at_10 value: 37.384 - type: map_at_100 value: 38.56 - type: map_at_1000 value: 38.655 - type: map_at_3 value: 34.214 - type: map_at_5 value: 35.96 - type: mrr_at_1 value: 32.369 - type: mrr_at_10 value: 41.625 - type: mrr_at_100 value: 42.449 - type: mrr_at_1000 value: 42.502 - type: mrr_at_3 value: 38.899 - type: mrr_at_5 value: 40.489999999999995 - type: ndcg_at_1 value: 32.369 - type: ndcg_at_10 value: 43.287 - type: ndcg_at_100 value: 48.504999999999995 - type: ndcg_at_1000 value: 50.552 - type: ndcg_at_3 value: 37.549 - type: ndcg_at_5 value: 40.204 - type: precision_at_1 value: 32.369 - type: precision_at_10 value: 7.425 - type: precision_at_100 value: 1.134 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.102 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 27.755000000000003 - type: recall_at_10 value: 57.071000000000005 - type: recall_at_100 value: 79.456 - type: recall_at_1000 value: 93.54299999999999 - type: recall_at_3 value: 41.298 - type: recall_at_5 value: 48.037 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.855 - type: map_at_10 value: 34.53 - type: map_at_100 value: 36.167 - type: map_at_1000 value: 36.394999999999996 - type: map_at_3 value: 31.037 - type: map_at_5 value: 33.119 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.763999999999996 - type: mrr_at_100 value: 40.77 - type: mrr_at_1000 value: 40.826 - type: mrr_at_3 value: 36.495 - type: mrr_at_5 value: 38.561 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 40.942 - type: ndcg_at_100 value: 47.07 - type: ndcg_at_1000 value: 49.363 - type: ndcg_at_3 value: 35.038000000000004 - type: ndcg_at_5 value: 38.161 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 7.983999999999999 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.206 - type: precision_at_5 value: 12.253 - type: recall_at_1 value: 24.855 - type: recall_at_10 value: 53.291999999999994 - type: recall_at_100 value: 80.283 - type: recall_at_1000 value: 94.309 - type: recall_at_3 value: 37.257 - type: recall_at_5 value: 45.282 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.208 - type: map_at_10 value: 30.512 - type: map_at_100 value: 31.496000000000002 - type: map_at_1000 value: 31.595000000000002 - type: map_at_3 value: 27.904 - type: map_at_5 value: 29.491 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 32.379999999999995 - type: mrr_at_100 value: 33.245000000000005 - type: mrr_at_1000 value: 33.315 - type: mrr_at_3 value: 29.945 - type: mrr_at_5 value: 31.488 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 35.643 - type: ndcg_at_100 value: 40.535 - type: ndcg_at_1000 value: 43.042 - type: ndcg_at_3 value: 30.625000000000004 - type: ndcg_at_5 value: 33.323 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 13.431999999999999 - type: precision_at_5 value: 9.575 - type: recall_at_1 value: 21.208 - type: recall_at_10 value: 49.47 - type: recall_at_100 value: 71.71499999999999 - type: recall_at_1000 value: 90.55499999999999 - type: recall_at_3 value: 36.124 - type: recall_at_5 value: 42.606 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 11.363 - type: map_at_10 value: 20.312 - type: map_at_100 value: 22.225 - type: map_at_1000 value: 22.411 - type: map_at_3 value: 16.68 - type: map_at_5 value: 18.608 - type: mrr_at_1 value: 25.537 - type: mrr_at_10 value: 37.933 - type: mrr_at_100 value: 38.875 - type: mrr_at_1000 value: 38.911 - type: mrr_at_3 value: 34.387 - type: mrr_at_5 value: 36.51 - type: ndcg_at_1 value: 25.537 - type: ndcg_at_10 value: 28.82 - type: ndcg_at_100 value: 36.341 - type: ndcg_at_1000 value: 39.615 - type: ndcg_at_3 value: 23.01 - type: ndcg_at_5 value: 25.269000000000002 - type: precision_at_1 value: 25.537 - type: precision_at_10 value: 9.153 - type: precision_at_100 value: 1.7319999999999998 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 17.22 - type: precision_at_5 value: 13.629 - type: recall_at_1 value: 11.363 - type: recall_at_10 value: 35.382999999999996 - type: recall_at_100 value: 61.367000000000004 - type: recall_at_1000 value: 79.699 - type: recall_at_3 value: 21.495 - type: recall_at_5 value: 27.42 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.65 - type: map_at_10 value: 20.742 - type: map_at_100 value: 29.614 - type: map_at_1000 value: 31.373 - type: map_at_3 value: 14.667 - type: map_at_5 value: 17.186 - type: mrr_at_1 value: 69.75 - type: mrr_at_10 value: 76.762 - type: mrr_at_100 value: 77.171 - type: mrr_at_1000 value: 77.179 - type: mrr_at_3 value: 75.125 - type: mrr_at_5 value: 76.287 - type: ndcg_at_1 value: 57.62500000000001 - type: ndcg_at_10 value: 42.370999999999995 - type: ndcg_at_100 value: 47.897 - type: ndcg_at_1000 value: 55.393 - type: ndcg_at_3 value: 46.317 - type: ndcg_at_5 value: 43.906 - type: precision_at_1 value: 69.75 - type: precision_at_10 value: 33.95 - type: precision_at_100 value: 10.885 - type: precision_at_1000 value: 2.2239999999999998 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.3 - type: recall_at_1 value: 9.65 - type: recall_at_10 value: 26.117 - type: recall_at_100 value: 55.084 - type: recall_at_1000 value: 78.62400000000001 - type: recall_at_3 value: 15.823 - type: recall_at_5 value: 19.652 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.885 - type: f1 value: 42.99567641346983 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 70.97 - type: map_at_10 value: 80.34599999999999 - type: map_at_100 value: 80.571 - type: map_at_1000 value: 80.584 - type: map_at_3 value: 79.279 - type: map_at_5 value: 79.94 - type: mrr_at_1 value: 76.613 - type: mrr_at_10 value: 85.15700000000001 - type: mrr_at_100 value: 85.249 - type: mrr_at_1000 value: 85.252 - type: mrr_at_3 value: 84.33800000000001 - type: mrr_at_5 value: 84.89 - type: ndcg_at_1 value: 76.613 - type: ndcg_at_10 value: 84.53399999999999 - type: ndcg_at_100 value: 85.359 - type: ndcg_at_1000 value: 85.607 - type: ndcg_at_3 value: 82.76599999999999 - type: ndcg_at_5 value: 83.736 - type: precision_at_1 value: 76.613 - type: precision_at_10 value: 10.206 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 31.913000000000004 - type: precision_at_5 value: 19.769000000000002 - type: recall_at_1 value: 70.97 - type: recall_at_10 value: 92.674 - type: recall_at_100 value: 95.985 - type: recall_at_1000 value: 97.57000000000001 - type: recall_at_3 value: 87.742 - type: recall_at_5 value: 90.28 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.494 - type: map_at_10 value: 36.491 - type: map_at_100 value: 38.550000000000004 - type: map_at_1000 value: 38.726 - type: map_at_3 value: 31.807000000000002 - type: map_at_5 value: 34.299 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.146 - type: mrr_at_100 value: 54.013999999999996 - type: mrr_at_1000 value: 54.044000000000004 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.124 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 44.499 - type: ndcg_at_100 value: 51.629000000000005 - type: ndcg_at_1000 value: 54.367 - type: ndcg_at_3 value: 40.900999999999996 - type: ndcg_at_5 value: 41.737 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.346 - type: precision_at_100 value: 1.974 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 27.366 - type: precision_at_5 value: 19.846 - type: recall_at_1 value: 22.494 - type: recall_at_10 value: 51.156 - type: recall_at_100 value: 77.11200000000001 - type: recall_at_1000 value: 93.44 - type: recall_at_3 value: 36.574 - type: recall_at_5 value: 42.361 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.568999999999996 - type: map_at_10 value: 58.485 - type: map_at_100 value: 59.358999999999995 - type: map_at_1000 value: 59.429 - type: map_at_3 value: 55.217000000000006 - type: map_at_5 value: 57.236 - type: mrr_at_1 value: 77.137 - type: mrr_at_10 value: 82.829 - type: mrr_at_100 value: 83.04599999999999 - type: mrr_at_1000 value: 83.05399999999999 - type: mrr_at_3 value: 81.904 - type: mrr_at_5 value: 82.50800000000001 - type: ndcg_at_1 value: 77.137 - type: ndcg_at_10 value: 67.156 - type: ndcg_at_100 value: 70.298 - type: ndcg_at_1000 value: 71.65700000000001 - type: ndcg_at_3 value: 62.535 - type: ndcg_at_5 value: 65.095 - type: precision_at_1 value: 77.137 - type: precision_at_10 value: 13.911999999999999 - type: precision_at_100 value: 1.6389999999999998 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 39.572 - type: precision_at_5 value: 25.766 - type: recall_at_1 value: 38.568999999999996 - type: recall_at_10 value: 69.56099999999999 - type: recall_at_100 value: 81.931 - type: recall_at_1000 value: 90.91799999999999 - type: recall_at_3 value: 59.358999999999995 - type: recall_at_5 value: 64.416 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.45600000000002 - type: ap value: 84.09725115338568 - type: f1 value: 88.41874909080512 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.404999999999998 - type: map_at_10 value: 33.921 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.164 - type: map_at_3 value: 30.043999999999997 - type: map_at_5 value: 32.327 - type: mrr_at_1 value: 21.977 - type: mrr_at_10 value: 34.505 - type: mrr_at_100 value: 35.638999999999996 - type: mrr_at_1000 value: 35.68 - type: mrr_at_3 value: 30.703999999999997 - type: mrr_at_5 value: 32.96 - type: ndcg_at_1 value: 21.963 - type: ndcg_at_10 value: 40.859 - type: ndcg_at_100 value: 46.614 - type: ndcg_at_1000 value: 47.789 - type: ndcg_at_3 value: 33.007999999999996 - type: ndcg_at_5 value: 37.084 - type: precision_at_1 value: 21.963 - type: precision_at_10 value: 6.493 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.155000000000001 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.404999999999998 - type: recall_at_10 value: 62.175000000000004 - type: recall_at_100 value: 88.786 - type: recall_at_1000 value: 97.738 - type: recall_at_3 value: 40.925 - type: recall_at_5 value: 50.722 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.50661194710442 - type: f1 value: 93.30311193153668 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 73.24669402644778 - type: f1 value: 54.23122108002977 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.61936785474109 - type: f1 value: 70.52644941025565 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.76529926025555 - type: f1 value: 77.26872729322514 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.39450293021839 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.757796879839294 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.62512146657428 - type: mrr value: 33.84624322066173 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.462 - type: map_at_10 value: 14.947 - type: map_at_100 value: 19.344 - type: map_at_1000 value: 20.933 - type: map_at_3 value: 10.761999999999999 - type: map_at_5 value: 12.744 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.365 - type: mrr_at_100 value: 57.931 - type: mrr_at_1000 value: 57.96 - type: mrr_at_3 value: 54.85 - type: mrr_at_5 value: 56.569 - type: ndcg_at_1 value: 46.129999999999995 - type: ndcg_at_10 value: 38.173 - type: ndcg_at_100 value: 35.983 - type: ndcg_at_1000 value: 44.507000000000005 - type: ndcg_at_3 value: 42.495 - type: ndcg_at_5 value: 41.019 - type: precision_at_1 value: 47.678 - type: precision_at_10 value: 28.731 - type: precision_at_100 value: 9.232 - type: precision_at_1000 value: 2.202 - type: precision_at_3 value: 39.628 - type: precision_at_5 value: 35.851 - type: recall_at_1 value: 6.462 - type: recall_at_10 value: 18.968 - type: recall_at_100 value: 37.131 - type: recall_at_1000 value: 67.956 - type: recall_at_3 value: 11.905000000000001 - type: recall_at_5 value: 15.097 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 30.335 - type: map_at_10 value: 46.611999999999995 - type: map_at_100 value: 47.632000000000005 - type: map_at_1000 value: 47.661 - type: map_at_3 value: 41.876999999999995 - type: map_at_5 value: 44.799 - type: mrr_at_1 value: 34.125 - type: mrr_at_10 value: 49.01 - type: mrr_at_100 value: 49.75 - type: mrr_at_1000 value: 49.768 - type: mrr_at_3 value: 45.153 - type: mrr_at_5 value: 47.589999999999996 - type: ndcg_at_1 value: 34.125 - type: ndcg_at_10 value: 54.777 - type: ndcg_at_100 value: 58.914 - type: ndcg_at_1000 value: 59.521 - type: ndcg_at_3 value: 46.015 - type: ndcg_at_5 value: 50.861000000000004 - type: precision_at_1 value: 34.125 - type: precision_at_10 value: 9.166 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 21.147 - type: precision_at_5 value: 15.469 - type: recall_at_1 value: 30.335 - type: recall_at_10 value: 77.194 - type: recall_at_100 value: 94.812 - type: recall_at_1000 value: 99.247 - type: recall_at_3 value: 54.681000000000004 - type: recall_at_5 value: 65.86800000000001 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.62 - type: map_at_10 value: 84.536 - type: map_at_100 value: 85.167 - type: map_at_1000 value: 85.184 - type: map_at_3 value: 81.607 - type: map_at_5 value: 83.423 - type: mrr_at_1 value: 81.36 - type: mrr_at_10 value: 87.506 - type: mrr_at_100 value: 87.601 - type: mrr_at_1000 value: 87.601 - type: mrr_at_3 value: 86.503 - type: mrr_at_5 value: 87.179 - type: ndcg_at_1 value: 81.36 - type: ndcg_at_10 value: 88.319 - type: ndcg_at_100 value: 89.517 - type: ndcg_at_1000 value: 89.60900000000001 - type: ndcg_at_3 value: 85.423 - type: ndcg_at_5 value: 86.976 - type: precision_at_1 value: 81.36 - type: precision_at_10 value: 13.415 - type: precision_at_100 value: 1.529 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.342999999999996 - type: precision_at_5 value: 24.534 - type: recall_at_1 value: 70.62 - type: recall_at_10 value: 95.57600000000001 - type: recall_at_100 value: 99.624 - type: recall_at_1000 value: 99.991 - type: recall_at_3 value: 87.22 - type: recall_at_5 value: 91.654 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.826438478212744 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.24027467551447 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.997999999999999 - type: map_at_10 value: 14.267 - type: map_at_100 value: 16.843 - type: map_at_1000 value: 17.229 - type: map_at_3 value: 9.834 - type: map_at_5 value: 11.92 - type: mrr_at_1 value: 24.7 - type: mrr_at_10 value: 37.685 - type: mrr_at_100 value: 38.704 - type: mrr_at_1000 value: 38.747 - type: mrr_at_3 value: 34.150000000000006 - type: mrr_at_5 value: 36.075 - type: ndcg_at_1 value: 24.7 - type: ndcg_at_10 value: 23.44 - type: ndcg_at_100 value: 32.617000000000004 - type: ndcg_at_1000 value: 38.628 - type: ndcg_at_3 value: 21.747 - type: ndcg_at_5 value: 19.076 - type: precision_at_1 value: 24.7 - type: precision_at_10 value: 12.47 - type: precision_at_100 value: 2.564 - type: precision_at_1000 value: 0.4 - type: precision_at_3 value: 20.767 - type: precision_at_5 value: 17.06 - type: recall_at_1 value: 4.997999999999999 - type: recall_at_10 value: 25.3 - type: recall_at_100 value: 52.048 - type: recall_at_1000 value: 81.093 - type: recall_at_3 value: 12.642999999999999 - type: recall_at_5 value: 17.312 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - 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task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 69.91342028796086 - type: cos_sim_spearman value: 69.71495021867864 - type: euclidean_pearson value: 70.65334330405646 - type: euclidean_spearman value: 69.4321253472211 - type: manhattan_pearson value: 70.59743494727465 - type: manhattan_spearman value: 69.11695509297482 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.42451709766952 - type: cos_sim_spearman value: 86.07166710670508 - type: euclidean_pearson value: 86.12711421258899 - type: euclidean_spearman value: 86.05232086925126 - type: manhattan_pearson value: 86.15591089932126 - type: manhattan_spearman value: 86.0890128623439 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - 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type: precision_at_3 value: 27.444000000000003 - type: precision_at_5 value: 18 - type: recall_at_1 value: 59.511 - type: recall_at_10 value: 87.156 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 75.2 - type: recall_at_5 value: 80.661 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81683168316832 - type: cos_sim_ap value: 95.74716566563774 - type: cos_sim_f1 value: 90.64238745574103 - type: cos_sim_precision value: 91.7093142272262 - type: cos_sim_recall value: 89.60000000000001 - type: dot_accuracy value: 99.69405940594059 - type: dot_ap value: 91.09013507754594 - type: dot_f1 value: 84.54227113556779 - type: dot_precision value: 84.58458458458459 - type: dot_recall value: 84.5 - type: euclidean_accuracy value: 99.81782178217821 - type: euclidean_ap value: 95.6324301072609 - type: euclidean_f1 value: 90.58341862845445 - type: euclidean_precision value: 92.76729559748428 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.81980198019802 - type: manhattan_ap value: 95.68510494437183 - type: manhattan_f1 value: 90.58945191313342 - type: manhattan_precision value: 93.79014989293361 - type: manhattan_recall value: 87.6 - type: max_accuracy value: 99.81980198019802 - type: max_ap value: 95.74716566563774 - type: max_f1 value: 90.64238745574103 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 67.63761899427078 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.572473369697235 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.63000245208579 - type: mrr value: 54.504193722943725 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.300791939416545 - type: cos_sim_spearman value: 31.662904057924123 - type: dot_pearson value: 26.21198530758316 - type: dot_spearman value: 27.006921548904263 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.197 - type: map_at_10 value: 1.752 - type: map_at_100 value: 10.795 - type: map_at_1000 value: 27.18 - type: map_at_3 value: 0.5890000000000001 - type: map_at_5 value: 0.938 - type: mrr_at_1 value: 74 - type: mrr_at_10 value: 85.833 - type: mrr_at_100 value: 85.833 - type: mrr_at_1000 value: 85.833 - type: mrr_at_3 value: 85.333 - type: mrr_at_5 value: 85.833 - type: ndcg_at_1 value: 69 - type: ndcg_at_10 value: 70.22 - 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type: mrr_at_3 value: 47.278999999999996 - type: mrr_at_5 value: 49.626 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 25.507 - type: ndcg_at_100 value: 38.296 - type: ndcg_at_1000 value: 49.492000000000004 - type: ndcg_at_3 value: 29.006999999999998 - type: ndcg_at_5 value: 25.979000000000003 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 22.041 - type: precision_at_100 value: 8.02 - type: precision_at_1000 value: 1.567 - type: precision_at_3 value: 28.571 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.803 - type: recall_at_10 value: 16.378 - type: recall_at_100 value: 50.489 - type: recall_at_1000 value: 85.013 - type: recall_at_3 value: 6.505 - type: recall_at_5 value: 9.243 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.55579999999999 - type: ap value: 14.206982753316227 - 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type: max_f1 value: 77.91659078492079 language: - en license: mit --- # gte-large General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). \| Model Name \| Model Size (GB) \| Dimension \| Sequence Length \| Average (56) \| Clustering (11) \| Pair Classification (3) \| Reranking (4) \| Retrieval (15) \| STS (10) \| Summarization (1) \| Classification (12) \| \|:----:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\| \| [gte-large](https://huggingface.co/thenlper/gte-large) \| 0.67 \| 1024 \| 512 \| 63.13 \| 46.84 \| 85.00 \| 59.13 \| 52.22 \| 83.35 \| 31.66 \| 73.33 \| \| [gte-base](https://huggingface.co/thenlper/gte-base) \| 0.22 \| 768 \| 512 \| 62.39 \| 46.2 \| 84.57 \| 58.61 \| 51.14 \| 82.3 \| 31.17 \| 73.01 \| \| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) \| 1.34 \| 1024\| 512 \| 62.25 \| 44.49 \| 86.03 \| 56.61 \| 50.56 \| 82.05 \| 30.19 \| 75.24 \| \| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) \| 0.44 \| 768 \| 512 \| 61.5 \| 43.80 \| 85.73 \| 55.91 \| 50.29 \| 81.05 \| 30.28 \| 73.84 \| \| [gte-small](https://huggingface.co/thenlper/gte-small) \| 0.07 \| 384 \| 512 \| 61.36 \| 44.89 \| 83.54 \| 57.7 \| 49.46 \| 82.07 \| 30.42 \| 72.31 \| \| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) \| - \| 1536 \| 8192 \| 60.99 \| 45.9 \| 84.89 \| 56.32 \| 49.25 \| 80.97 \| 30.8 \| 70.93 \| \| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) \| 0.13 \| 384 \| 512 \| 59.93 \| 39.92 \| 84.67 \| 54.32 \| 49.04 \| 80.39 \| 31.16 \| 72.94 \| \| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) \| 9.73 \| 768 \| 512 \| 59.51 \| 43.72 \| 85.06 \| 56.42 \| 42.24 \| 82.63 \| 30.08 \| 73.42 \| \| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) \| 0.44 \| 768 \| 514 \| 57.78 \| 43.69 \| 83.04 \| 59.36 \| 43.81 \| 80.28 \| 27.49 \| 65.07 \| \| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) \| 28.27 \| 4096 \| 2048 \| 57.59 \| 38.93 \| 81.9 \| 55.65 \| 48.22 \| 77.74 \| 33.6 \| 66.19 \| \| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) \| 0.13 \| 384 \| 512 \| 56.53 \| 41.81 \| 82.41 \| 58.44 \| 42.69 \| 79.8 \| 27.9 \| 63.21 \| \| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) \| 0.09 \| 384 \| 512 \| 56.26 \| 42.35 \| 82.37 \| 58.04 \| 41.95 \| 78.9 \| 30.81 \| 63.05 \| \| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) \| 0.44 \| 768 \| 512 \| 56.00 \| 41.1 \| 82.54 \| 53.14 \| 41.88 \| 76.51 \| 30.36 \| 66.68 \| \| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) \| 0.22 \| 768 \| 512 \| 55.27 \| 40.21 \| 85.18 \| 53.09 \| 33.63 \| 81.14 \| 31.39 \| 69.81 \| ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large") model = AutoModel.from_pretrained("thenlper/gte-large") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(*batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-large') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```
sanekalas/t5-hw3-shumovav	sanekalas	2025-05-27T19:23:51Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-27T19:23:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Moklemok/CodeSpace	Moklemok	2025-05-27T19:21:03Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-27T19:21:03Z	--- license: bigcode-openrail-m ---
Lubna-qureshi-Hd/lubna.qureshi.viral.video.HOT.NEws.Today.Trending.Latest.Video	Lubna-qureshi-Hd	2025-05-27T19:20:58Z	0	0	null	[ "region:us" ]	null	2025-05-27T19:17:26Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Lubna-qureshi) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Lubna-qureshi) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Lubna-qureshi)
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ErikCikalleshi/alpaca_lora_model	ErikCikalleshi	2025-05-27T19:19:51Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-22T19:35:59Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ErikCikalleshi - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs7	aamijar	2025-05-27T19:19:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T19:19:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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milpu02/Akkgsykmix	milpu02	2025-05-27T19:18:07Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2025-05-27T19:17:54Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Akkgsyk output: url: images/pixai-1845374734374697074-2.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: Akkgsyk --- # sdxl <Gallery /> ## Trigger words You should use `Akkgsyk` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/milpu02/Akkgsykmix/tree/main) them in the Files & versions tab.
gradientrouting-spar/medical_task_qwen_3_8b_ft_trainers_seed_42_epoch_1	gradientrouting-spar	2025-05-27T19:17:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T19:15:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Videos-CCTV-wiring-cikgu-viral-clip/Original.Bocor.Video.CCTV.wiring.cikgu.video.nur.fadhilah.binti.zainal.guru.part.2.video	Videos-CCTV-wiring-cikgu-viral-clip	2025-05-27T19:17:22Z	0	0	null	[ "region:us" ]	null	2025-05-27T19:16:57Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
flaviawallen/MNLP_M2_document_encoder	flaviawallen	2025-05-27T19:15:56Z	0	0	sentence-transformers	[ "sentence-transformers", "pytorch", "onnx", "safetensors", "bert", "feature-extraction", "mteb", "sentence-similarity", "en", "arxiv:2402.16829", "arxiv:2212.09741", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-27T19:14:26Z	--- language: - en library_name: sentence-transformers license: mit pipeline_tag: sentence-similarity tags: - feature-extraction - mteb - sentence-similarity - sentence-transformers model-index: - name: GIST-small-Embedding-v0 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.26865671641791 - type: ap value: 38.25623793370476 - type: f1 value: 69.26434651320257 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.232225 - type: ap value: 89.97936072879344 - type: f1 value: 93.22122653806187 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.715999999999994 - type: f1 value: 49.169789920136076 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 34.922 - type: map_at_10 value: 50.524 - type: map_at_100 value: 51.247 - type: map_at_1000 value: 51.249 - type: map_at_3 value: 45.887 - type: map_at_5 value: 48.592999999999996 - type: mrr_at_1 value: 34.922 - type: mrr_at_10 value: 50.382000000000005 - type: mrr_at_100 value: 51.104000000000006 - type: mrr_at_1000 value: 51.105999999999995 - type: mrr_at_3 value: 45.733000000000004 - type: mrr_at_5 value: 48.428 - type: ndcg_at_1 value: 34.922 - type: ndcg_at_10 value: 59.12 - type: ndcg_at_100 value: 62.083999999999996 - type: ndcg_at_1000 value: 62.137 - type: ndcg_at_3 value: 49.616 - type: ndcg_at_5 value: 54.501 - type: precision_at_1 value: 34.922 - type: precision_at_10 value: 8.649 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.152 - type: precision_at_5 value: 14.466999999999999 - type: recall_at_1 value: 34.922 - type: recall_at_10 value: 86.48599999999999 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 60.455000000000005 - type: recall_at_5 value: 72.333 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.623282347623714 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 39.86487843524932 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.3290291318171 - type: mrr value: 75.2379853141626 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.52002953574285 - type: cos_sim_spearman value: 86.98752423842483 - type: euclidean_pearson value: 86.89442688314197 - type: euclidean_spearman value: 86.88631711307471 - type: manhattan_pearson value: 87.03723618507175 - type: manhattan_spearman value: 86.76041062975224 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.64935064935065 - type: f1 value: 86.61903824934998 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.21904455377494 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.43342755570654 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.843 - type: map_at_10 value: 43.379 - type: map_at_100 value: 44.946999999999996 - type: map_at_1000 value: 45.078 - type: map_at_3 value: 39.598 - type: map_at_5 value: 41.746 - type: mrr_at_1 value: 39.199 - type: mrr_at_10 value: 49.672 - type: mrr_at_100 value: 50.321000000000005 - type: mrr_at_1000 value: 50.365 - type: mrr_at_3 value: 46.805 - type: mrr_at_5 value: 48.579 - type: ndcg_at_1 value: 39.199 - type: ndcg_at_10 value: 50.163999999999994 - type: ndcg_at_100 value: 55.418 - type: ndcg_at_1000 value: 57.353 - type: ndcg_at_3 value: 44.716 - type: ndcg_at_5 value: 47.268 - type: precision_at_1 value: 39.199 - type: precision_at_10 value: 9.757 - type: precision_at_100 value: 1.552 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 21.602 - type: precision_at_5 value: 15.479000000000001 - type: recall_at_1 value: 31.843 - type: recall_at_10 value: 62.743 - type: recall_at_100 value: 84.78099999999999 - type: recall_at_1000 value: 96.86099999999999 - type: recall_at_3 value: 46.927 - type: recall_at_5 value: 54.355 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.321 - type: map_at_10 value: 39.062999999999995 - type: map_at_100 value: 40.403 - type: map_at_1000 value: 40.534 - type: map_at_3 value: 36.367 - type: map_at_5 value: 37.756 - type: mrr_at_1 value: 35.987 - type: mrr_at_10 value: 44.708999999999996 - type: mrr_at_100 value: 45.394 - type: mrr_at_1000 value: 45.436 - type: mrr_at_3 value: 42.463 - type: mrr_at_5 value: 43.663000000000004 - type: ndcg_at_1 value: 35.987 - type: ndcg_at_10 value: 44.585 - type: ndcg_at_100 value: 49.297999999999995 - type: ndcg_at_1000 value: 51.315 - type: ndcg_at_3 value: 40.569 - type: ndcg_at_5 value: 42.197 - type: precision_at_1 value: 35.987 - type: precision_at_10 value: 8.369 - type: precision_at_100 value: 1.366 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 19.427 - type: precision_at_5 value: 13.58 - type: recall_at_1 value: 29.321 - type: recall_at_10 value: 54.333 - type: recall_at_100 value: 74.178 - type: recall_at_1000 value: 86.732 - type: recall_at_3 value: 42.46 - type: recall_at_5 value: 47.089999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.811 - type: map_at_10 value: 51.114000000000004 - type: map_at_100 value: 52.22 - type: map_at_1000 value: 52.275000000000006 - type: map_at_3 value: 47.644999999999996 - type: map_at_5 value: 49.675000000000004 - type: mrr_at_1 value: 44.389 - type: mrr_at_10 value: 54.459 - type: mrr_at_100 value: 55.208999999999996 - type: mrr_at_1000 value: 55.239000000000004 - type: mrr_at_3 value: 51.954 - type: mrr_at_5 value: 53.571999999999996 - type: ndcg_at_1 value: 44.389 - type: ndcg_at_10 value: 56.979 - type: ndcg_at_100 value: 61.266 - type: ndcg_at_1000 value: 62.315 - type: ndcg_at_3 value: 51.342 - type: ndcg_at_5 value: 54.33 - type: precision_at_1 value: 44.389 - type: precision_at_10 value: 9.26 - type: precision_at_100 value: 1.226 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 22.926 - type: precision_at_5 value: 15.987000000000002 - type: recall_at_1 value: 38.811 - type: recall_at_10 value: 70.841 - type: recall_at_100 value: 89.218 - type: recall_at_1000 value: 96.482 - type: recall_at_3 value: 56.123999999999995 - type: recall_at_5 value: 63.322 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.378 - type: map_at_10 value: 34.311 - type: map_at_100 value: 35.399 - type: map_at_1000 value: 35.482 - type: map_at_3 value: 31.917 - type: map_at_5 value: 33.275 - type: mrr_at_1 value: 27.683999999999997 - type: mrr_at_10 value: 36.575 - type: mrr_at_100 value: 37.492 - type: mrr_at_1000 value: 37.556 - type: mrr_at_3 value: 34.35 - type: mrr_at_5 value: 35.525 - type: ndcg_at_1 value: 27.683999999999997 - type: ndcg_at_10 value: 39.247 - type: ndcg_at_100 value: 44.424 - type: ndcg_at_1000 value: 46.478 - type: ndcg_at_3 value: 34.684 - type: ndcg_at_5 value: 36.886 - type: precision_at_1 value: 27.683999999999997 - type: precision_at_10 value: 5.989 - type: precision_at_100 value: 0.899 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.84 - type: precision_at_5 value: 10.215 - type: recall_at_1 value: 25.378 - type: recall_at_10 value: 52.195 - type: recall_at_100 value: 75.764 - type: recall_at_1000 value: 91.012 - type: recall_at_3 value: 39.885999999999996 - type: recall_at_5 value: 45.279 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.326 - type: map_at_10 value: 25.247000000000003 - type: map_at_100 value: 26.473000000000003 - type: map_at_1000 value: 26.579000000000004 - type: map_at_3 value: 22.466 - type: map_at_5 value: 24.113 - type: mrr_at_1 value: 21.393 - type: mrr_at_10 value: 30.187 - type: mrr_at_100 value: 31.089 - type: mrr_at_1000 value: 31.15 - type: mrr_at_3 value: 27.279999999999998 - type: mrr_at_5 value: 29.127 - type: ndcg_at_1 value: 21.393 - type: ndcg_at_10 value: 30.668 - type: ndcg_at_100 value: 36.543 - type: ndcg_at_1000 value: 39.181 - type: ndcg_at_3 value: 25.552000000000003 - type: ndcg_at_5 value: 28.176000000000002 - type: precision_at_1 value: 21.393 - type: precision_at_10 value: 5.784000000000001 - type: precision_at_100 value: 1.001 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 12.231 - type: precision_at_5 value: 9.179 - type: recall_at_1 value: 17.326 - type: recall_at_10 value: 42.415000000000006 - type: recall_at_100 value: 68.605 - type: recall_at_1000 value: 87.694 - type: recall_at_3 value: 28.343 - type: recall_at_5 value: 35.086 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.069 - type: map_at_10 value: 40.027 - type: map_at_100 value: 41.308 - type: map_at_1000 value: 41.412 - type: map_at_3 value: 36.864000000000004 - type: map_at_5 value: 38.641999999999996 - type: mrr_at_1 value: 35.707 - type: mrr_at_10 value: 45.527 - type: mrr_at_100 value: 46.348 - type: mrr_at_1000 value: 46.392 - type: mrr_at_3 value: 43.086 - type: mrr_at_5 value: 44.645 - type: ndcg_at_1 value: 35.707 - type: ndcg_at_10 value: 46.117000000000004 - type: ndcg_at_100 value: 51.468 - type: ndcg_at_1000 value: 53.412000000000006 - type: ndcg_at_3 value: 41.224 - type: ndcg_at_5 value: 43.637 - type: precision_at_1 value: 35.707 - type: precision_at_10 value: 8.459999999999999 - type: precision_at_100 value: 1.2970000000000002 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 19.731 - type: precision_at_5 value: 14.013 - type: recall_at_1 value: 29.069 - type: recall_at_10 value: 58.343999999999994 - type: recall_at_100 value: 81.296 - type: recall_at_1000 value: 93.974 - type: recall_at_3 value: 44.7 - type: recall_at_5 value: 50.88700000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.905 - type: map_at_10 value: 33.983000000000004 - type: map_at_100 value: 35.372 - type: map_at_1000 value: 35.487 - type: map_at_3 value: 30.902 - type: map_at_5 value: 32.505 - type: mrr_at_1 value: 29.794999999999998 - type: mrr_at_10 value: 39.28 - type: mrr_at_100 value: 40.215 - type: mrr_at_1000 value: 40.276 - type: mrr_at_3 value: 36.701 - type: mrr_at_5 value: 38.105 - type: ndcg_at_1 value: 29.794999999999998 - type: ndcg_at_10 value: 40.041 - type: ndcg_at_100 value: 45.884 - type: ndcg_at_1000 value: 48.271 - type: ndcg_at_3 value: 34.931 - type: ndcg_at_5 value: 37.044 - type: precision_at_1 value: 29.794999999999998 - type: precision_at_10 value: 7.546 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 16.933 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 23.905 - type: recall_at_10 value: 52.945 - type: recall_at_100 value: 77.551 - type: recall_at_1000 value: 93.793 - type: recall_at_3 value: 38.364 - type: recall_at_5 value: 44.044 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.24441666666667 - type: map_at_10 value: 34.4595 - type: map_at_100 value: 35.699999999999996 - type: map_at_1000 value: 35.8155 - type: map_at_3 value: 31.608333333333338 - type: map_at_5 value: 33.189416666666666 - type: mrr_at_1 value: 29.825250000000004 - type: mrr_at_10 value: 38.60875 - type: mrr_at_100 value: 39.46575 - type: mrr_at_1000 value: 39.52458333333333 - type: mrr_at_3 value: 36.145166666666675 - type: mrr_at_5 value: 37.57625 - type: ndcg_at_1 value: 29.825250000000004 - type: ndcg_at_10 value: 39.88741666666667 - type: ndcg_at_100 value: 45.17966666666667 - type: ndcg_at_1000 value: 47.440583333333336 - type: ndcg_at_3 value: 35.04591666666666 - type: ndcg_at_5 value: 37.32025 - type: precision_at_1 value: 29.825250000000004 - type: precision_at_10 value: 7.07225 - type: precision_at_100 value: 1.1462499999999998 - type: precision_at_1000 value: 0.15325 - type: precision_at_3 value: 16.18375 - type: precision_at_5 value: 11.526833333333334 - type: recall_at_1 value: 25.24441666666667 - type: recall_at_10 value: 51.744916666666676 - type: recall_at_100 value: 75.04574999999998 - type: recall_at_1000 value: 90.65558333333334 - type: recall_at_3 value: 38.28349999999999 - type: recall_at_5 value: 44.16591666666667 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.237000000000002 - type: map_at_10 value: 30.667 - type: map_at_100 value: 31.592 - type: map_at_1000 value: 31.688 - type: map_at_3 value: 28.810999999999996 - type: map_at_5 value: 29.788999999999998 - type: mrr_at_1 value: 26.840000000000003 - type: mrr_at_10 value: 33.305 - type: mrr_at_100 value: 34.089000000000006 - type: mrr_at_1000 value: 34.159 - type: mrr_at_3 value: 31.518 - type: mrr_at_5 value: 32.469 - type: ndcg_at_1 value: 26.840000000000003 - type: ndcg_at_10 value: 34.541 - type: ndcg_at_100 value: 39.206 - type: ndcg_at_1000 value: 41.592 - type: ndcg_at_3 value: 31.005 - type: ndcg_at_5 value: 32.554 - type: precision_at_1 value: 26.840000000000003 - type: precision_at_10 value: 5.3069999999999995 - type: precision_at_100 value: 0.8340000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 13.292000000000002 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 24.237000000000002 - type: recall_at_10 value: 43.862 - type: recall_at_100 value: 65.352 - type: recall_at_1000 value: 82.704 - type: recall_at_3 value: 34.009 - type: recall_at_5 value: 37.878 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.482 - type: map_at_10 value: 23.249 - type: map_at_100 value: 24.388 - type: map_at_1000 value: 24.519 - type: map_at_3 value: 20.971 - type: map_at_5 value: 22.192 - type: mrr_at_1 value: 19.993 - type: mrr_at_10 value: 26.985 - type: mrr_at_100 value: 27.975 - type: mrr_at_1000 value: 28.052 - type: mrr_at_3 value: 24.954 - type: mrr_at_5 value: 26.070999999999998 - type: ndcg_at_1 value: 19.993 - type: ndcg_at_10 value: 27.656 - type: ndcg_at_100 value: 33.256 - type: ndcg_at_1000 value: 36.275 - type: ndcg_at_3 value: 23.644000000000002 - type: ndcg_at_5 value: 25.466 - type: precision_at_1 value: 19.993 - type: precision_at_10 value: 5.093 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.149000000000001 - type: precision_at_5 value: 8.149000000000001 - type: recall_at_1 value: 16.482 - type: recall_at_10 value: 37.141999999999996 - type: recall_at_100 value: 62.696 - type: recall_at_1000 value: 84.333 - type: recall_at_3 value: 26.031 - type: recall_at_5 value: 30.660999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.887999999999998 - type: map_at_10 value: 34.101 - type: map_at_100 value: 35.27 - type: map_at_1000 value: 35.370000000000005 - type: map_at_3 value: 31.283 - type: map_at_5 value: 32.72 - type: mrr_at_1 value: 29.011 - type: mrr_at_10 value: 38.004 - type: mrr_at_100 value: 38.879000000000005 - type: mrr_at_1000 value: 38.938 - type: mrr_at_3 value: 35.571999999999996 - type: mrr_at_5 value: 36.789 - type: ndcg_at_1 value: 29.011 - type: ndcg_at_10 value: 39.586 - type: ndcg_at_100 value: 44.939 - type: ndcg_at_1000 value: 47.236 - type: ndcg_at_3 value: 34.4 - type: ndcg_at_5 value: 36.519 - type: precision_at_1 value: 29.011 - type: precision_at_10 value: 6.763 - type: precision_at_100 value: 1.059 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 15.609 - type: precision_at_5 value: 10.896 - type: recall_at_1 value: 24.887999999999998 - type: recall_at_10 value: 52.42 - type: recall_at_100 value: 75.803 - type: recall_at_1000 value: 91.725 - type: recall_at_3 value: 38.080999999999996 - type: recall_at_5 value: 43.47 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.953 - type: map_at_10 value: 32.649 - type: map_at_100 value: 34.181 - type: map_at_1000 value: 34.398 - type: map_at_3 value: 29.567 - type: map_at_5 value: 31.263 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 37.419999999999995 - type: mrr_at_100 value: 38.396 - type: mrr_at_1000 value: 38.458 - type: mrr_at_3 value: 34.782999999999994 - type: mrr_at_5 value: 36.254999999999995 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 38.595 - type: ndcg_at_100 value: 44.6 - type: ndcg_at_1000 value: 47.158 - type: ndcg_at_3 value: 33.56 - type: ndcg_at_5 value: 35.870000000000005 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.53 - type: precision_at_100 value: 1.538 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.542 - type: recall_at_1 value: 23.953 - type: recall_at_10 value: 50.08200000000001 - type: recall_at_100 value: 77.364 - type: recall_at_1000 value: 93.57799999999999 - type: recall_at_3 value: 35.432 - type: recall_at_5 value: 41.875 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.72 - type: map_at_10 value: 25.724000000000004 - type: map_at_100 value: 26.846999999999998 - type: map_at_1000 value: 26.964 - type: map_at_3 value: 22.909 - type: map_at_5 value: 24.596999999999998 - type: mrr_at_1 value: 18.854000000000003 - type: mrr_at_10 value: 27.182000000000002 - type: mrr_at_100 value: 28.182000000000002 - type: mrr_at_1000 value: 28.274 - type: mrr_at_3 value: 24.276 - type: mrr_at_5 value: 26.115 - type: ndcg_at_1 value: 18.854000000000003 - type: ndcg_at_10 value: 30.470000000000002 - type: ndcg_at_100 value: 35.854 - type: ndcg_at_1000 value: 38.701 - type: ndcg_at_3 value: 24.924 - type: ndcg_at_5 value: 27.895999999999997 - type: precision_at_1 value: 18.854000000000003 - type: precision_at_10 value: 5.009 - type: precision_at_100 value: 0.835 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 10.721 - type: precision_at_5 value: 8.133 - type: recall_at_1 value: 17.72 - type: recall_at_10 value: 43.617 - type: recall_at_100 value: 67.941 - type: recall_at_1000 value: 88.979 - type: recall_at_3 value: 29.044999999999998 - type: recall_at_5 value: 36.044 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 13.427 - type: map_at_10 value: 22.935 - type: map_at_100 value: 24.808 - type: map_at_1000 value: 24.994 - type: map_at_3 value: 19.533 - type: map_at_5 value: 21.261 - type: mrr_at_1 value: 30.945 - type: mrr_at_10 value: 43.242000000000004 - type: mrr_at_100 value: 44.013999999999996 - type: mrr_at_1000 value: 44.048 - type: mrr_at_3 value: 40.109 - type: mrr_at_5 value: 42.059999999999995 - type: ndcg_at_1 value: 30.945 - type: ndcg_at_10 value: 31.828 - type: ndcg_at_100 value: 38.801 - type: ndcg_at_1000 value: 42.126999999999995 - type: ndcg_at_3 value: 26.922 - type: ndcg_at_5 value: 28.483999999999998 - type: precision_at_1 value: 30.945 - type: precision_at_10 value: 9.844 - type: precision_at_100 value: 1.7309999999999999 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 20.477999999999998 - type: precision_at_5 value: 15.27 - type: recall_at_1 value: 13.427 - type: recall_at_10 value: 37.141000000000005 - type: recall_at_100 value: 61.007 - type: recall_at_1000 value: 79.742 - type: recall_at_3 value: 24.431 - type: recall_at_5 value: 29.725 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.122 - type: map_at_10 value: 18.799 - type: map_at_100 value: 25.724999999999998 - type: map_at_1000 value: 27.205000000000002 - type: map_at_3 value: 14.194999999999999 - type: map_at_5 value: 16.225 - type: mrr_at_1 value: 68.0 - type: mrr_at_10 value: 76.035 - type: mrr_at_100 value: 76.292 - type: mrr_at_1000 value: 76.297 - type: mrr_at_3 value: 74.458 - type: mrr_at_5 value: 75.558 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 39.761 - type: ndcg_at_100 value: 43.736999999999995 - type: ndcg_at_1000 value: 51.146 - type: ndcg_at_3 value: 45.921 - type: ndcg_at_5 value: 42.756 - type: precision_at_1 value: 68.0 - type: precision_at_10 value: 30.275000000000002 - type: precision_at_100 value: 9.343 - type: precision_at_1000 value: 1.8270000000000002 - type: precision_at_3 value: 49.167 - type: precision_at_5 value: 40.699999999999996 - type: recall_at_1 value: 9.122 - type: recall_at_10 value: 23.669999999999998 - type: recall_at_100 value: 48.719 - type: recall_at_1000 value: 72.033 - type: recall_at_3 value: 15.498999999999999 - type: recall_at_5 value: 18.657 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 55.885000000000005 - type: f1 value: 50.70726446938571 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 75.709 - type: map_at_10 value: 83.345 - type: map_at_100 value: 83.557 - type: map_at_1000 value: 83.572 - type: map_at_3 value: 82.425 - type: map_at_5 value: 83.013 - type: mrr_at_1 value: 81.593 - type: mrr_at_10 value: 88.331 - type: mrr_at_100 value: 88.408 - type: mrr_at_1000 value: 88.41 - type: mrr_at_3 value: 87.714 - type: mrr_at_5 value: 88.122 - type: ndcg_at_1 value: 81.593 - type: ndcg_at_10 value: 86.925 - type: ndcg_at_100 value: 87.67 - type: ndcg_at_1000 value: 87.924 - type: ndcg_at_3 value: 85.5 - type: ndcg_at_5 value: 86.283 - type: precision_at_1 value: 81.593 - type: precision_at_10 value: 10.264 - type: precision_at_100 value: 1.084 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 32.388 - type: precision_at_5 value: 19.991 - type: recall_at_1 value: 75.709 - type: recall_at_10 value: 93.107 - type: recall_at_100 value: 96.024 - type: recall_at_1000 value: 97.603 - type: recall_at_3 value: 89.08500000000001 - type: recall_at_5 value: 91.15299999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 19.121 - type: map_at_10 value: 31.78 - type: map_at_100 value: 33.497 - type: map_at_1000 value: 33.696 - type: map_at_3 value: 27.893 - type: map_at_5 value: 30.087000000000003 - type: mrr_at_1 value: 38.272 - type: mrr_at_10 value: 47.176 - type: mrr_at_100 value: 48.002 - type: mrr_at_1000 value: 48.044 - type: mrr_at_3 value: 45.086999999999996 - type: mrr_at_5 value: 46.337 - type: ndcg_at_1 value: 38.272 - type: ndcg_at_10 value: 39.145 - type: ndcg_at_100 value: 45.696999999999996 - type: ndcg_at_1000 value: 49.0 - type: ndcg_at_3 value: 36.148 - type: ndcg_at_5 value: 37.023 - type: precision_at_1 value: 38.272 - type: precision_at_10 value: 11.065 - type: precision_at_100 value: 1.7840000000000003 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 24.587999999999997 - type: precision_at_5 value: 18.056 - type: recall_at_1 value: 19.121 - type: recall_at_10 value: 44.857 - type: recall_at_100 value: 69.774 - type: recall_at_1000 value: 89.645 - type: recall_at_3 value: 32.588 - type: recall_at_5 value: 37.939 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.428 - type: map_at_10 value: 56.891999999999996 - type: map_at_100 value: 57.82899999999999 - type: map_at_1000 value: 57.896 - type: map_at_3 value: 53.762 - type: map_at_5 value: 55.718 - type: mrr_at_1 value: 72.856 - type: mrr_at_10 value: 79.245 - type: mrr_at_100 value: 79.515 - type: mrr_at_1000 value: 79.525 - type: mrr_at_3 value: 78.143 - type: mrr_at_5 value: 78.822 - type: ndcg_at_1 value: 72.856 - type: ndcg_at_10 value: 65.204 - type: ndcg_at_100 value: 68.552 - type: ndcg_at_1000 value: 69.902 - type: ndcg_at_3 value: 60.632 - type: ndcg_at_5 value: 63.161 - type: precision_at_1 value: 72.856 - type: precision_at_10 value: 13.65 - type: precision_at_100 value: 1.6260000000000001 - type: precision_at_1000 value: 0.181 - type: precision_at_3 value: 38.753 - type: precision_at_5 value: 25.251 - type: recall_at_1 value: 36.428 - type: recall_at_10 value: 68.25099999999999 - type: recall_at_100 value: 81.317 - type: recall_at_1000 value: 90.27 - type: recall_at_3 value: 58.13 - type: recall_at_5 value: 63.126000000000005 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 89.4868 - type: ap value: 84.88319192880247 - type: f1 value: 89.46144458052846 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.282999999999998 - type: map_at_10 value: 33.045 - type: map_at_100 value: 34.238 - type: map_at_1000 value: 34.29 - type: map_at_3 value: 29.305999999999997 - type: map_at_5 value: 31.391000000000002 - type: mrr_at_1 value: 21.92 - type: mrr_at_10 value: 33.649 - type: mrr_at_100 value: 34.791 - type: mrr_at_1000 value: 34.837 - type: mrr_at_3 value: 30.0 - type: mrr_at_5 value: 32.039 - type: ndcg_at_1 value: 21.92 - type: ndcg_at_10 value: 39.729 - type: ndcg_at_100 value: 45.484 - type: ndcg_at_1000 value: 46.817 - type: ndcg_at_3 value: 32.084 - type: ndcg_at_5 value: 35.789 - type: precision_at_1 value: 21.92 - type: precision_at_10 value: 6.297 - type: precision_at_100 value: 0.918 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 13.639000000000001 - type: precision_at_5 value: 10.054 - type: recall_at_1 value: 21.282999999999998 - type: recall_at_10 value: 60.343999999999994 - type: recall_at_100 value: 86.981 - type: recall_at_1000 value: 97.205 - type: recall_at_3 value: 39.452999999999996 - type: recall_at_5 value: 48.333 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 95.47879616963064 - type: f1 value: 95.21800589958251 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.09256725946192 - type: f1 value: 60.554043889452515 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 75.53463349024882 - type: f1 value: 73.14418495756476 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.22663080026899 - type: f1 value: 79.331456217501 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.50316010430136 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.15612040042282 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.36227552557184 - type: mrr value: 33.57901344209811 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.6610000000000005 - type: map_at_10 value: 12.992 - type: map_at_100 value: 16.756999999999998 - type: map_at_1000 value: 18.25 - type: map_at_3 value: 9.471 - type: map_at_5 value: 11.116 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 53.388999999999996 - type: mrr_at_100 value: 53.982 - type: mrr_at_1000 value: 54.033 - type: mrr_at_3 value: 51.858000000000004 - type: mrr_at_5 value: 53.019000000000005 - type: ndcg_at_1 value: 41.641 - type: ndcg_at_10 value: 34.691 - type: ndcg_at_100 value: 32.305 - type: ndcg_at_1000 value: 41.132999999999996 - type: ndcg_at_3 value: 40.614 - type: ndcg_at_5 value: 38.456 - type: precision_at_1 value: 43.344 - type: precision_at_10 value: 25.881999999999998 - type: precision_at_100 value: 8.483 - type: precision_at_1000 value: 2.131 - type: precision_at_3 value: 38.803 - type: precision_at_5 value: 33.87 - type: recall_at_1 value: 5.6610000000000005 - type: recall_at_10 value: 16.826 - type: recall_at_100 value: 32.939 - type: recall_at_1000 value: 65.161 - type: recall_at_3 value: 10.756 - type: recall_at_5 value: 13.331000000000001 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 26.692 - type: map_at_10 value: 41.065000000000005 - type: map_at_100 value: 42.235 - type: map_at_1000 value: 42.27 - type: map_at_3 value: 36.635 - type: map_at_5 value: 39.219 - type: mrr_at_1 value: 30.214000000000002 - type: mrr_at_10 value: 43.443 - type: mrr_at_100 value: 44.326 - type: mrr_at_1000 value: 44.352000000000004 - type: mrr_at_3 value: 39.623999999999995 - type: mrr_at_5 value: 41.898 - type: ndcg_at_1 value: 30.214000000000002 - type: ndcg_at_10 value: 48.692 - type: ndcg_at_100 value: 53.671 - type: ndcg_at_1000 value: 54.522000000000006 - type: ndcg_at_3 value: 40.245 - type: ndcg_at_5 value: 44.580999999999996 - type: precision_at_1 value: 30.214000000000002 - type: precision_at_10 value: 8.3 - type: precision_at_100 value: 1.1079999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 18.521 - type: precision_at_5 value: 13.627 - type: recall_at_1 value: 26.692 - type: recall_at_10 value: 69.699 - type: recall_at_100 value: 91.425 - type: recall_at_1000 value: 97.78099999999999 - type: recall_at_3 value: 47.711 - type: recall_at_5 value: 57.643 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.962 - type: map_at_10 value: 84.772 - type: map_at_100 value: 85.402 - type: map_at_1000 value: 85.418 - type: map_at_3 value: 81.89 - type: map_at_5 value: 83.685 - type: mrr_at_1 value: 81.67 - type: mrr_at_10 value: 87.681 - type: mrr_at_100 value: 87.792 - type: mrr_at_1000 value: 87.79299999999999 - type: mrr_at_3 value: 86.803 - type: mrr_at_5 value: 87.392 - type: ndcg_at_1 value: 81.69 - type: ndcg_at_10 value: 88.429 - type: ndcg_at_100 value: 89.66 - type: ndcg_at_1000 value: 89.762 - type: ndcg_at_3 value: 85.75 - type: ndcg_at_5 value: 87.20700000000001 - type: precision_at_1 value: 81.69 - type: precision_at_10 value: 13.395000000000001 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.507000000000005 - type: precision_at_5 value: 24.614 - type: recall_at_1 value: 70.962 - type: recall_at_10 value: 95.339 - type: recall_at_100 value: 99.543 - type: recall_at_1000 value: 99.984 - type: recall_at_3 value: 87.54899999999999 - type: recall_at_5 value: 91.726 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.506631779239555 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.63731341848479 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.852 - type: map_at_10 value: 13.175 - type: map_at_100 value: 15.623999999999999 - type: map_at_1000 value: 16.002 - type: map_at_3 value: 9.103 - type: map_at_5 value: 11.068999999999999 - type: mrr_at_1 value: 23.9 - type: mrr_at_10 value: 35.847 - type: mrr_at_100 value: 36.968 - type: mrr_at_1000 value: 37.018 - type: mrr_at_3 value: 32.300000000000004 - type: mrr_at_5 value: 34.14 - type: ndcg_at_1 value: 23.9 - type: ndcg_at_10 value: 21.889 - type: ndcg_at_100 value: 30.903000000000002 - type: ndcg_at_1000 value: 36.992000000000004 - type: ndcg_at_3 value: 20.274 - type: ndcg_at_5 value: 17.773 - type: precision_at_1 value: 23.9 - type: precision_at_10 value: 11.61 - type: precision_at_100 value: 2.4539999999999997 - type: precision_at_1000 value: 0.391 - type: precision_at_3 value: 19.133 - type: precision_at_5 value: 15.740000000000002 - type: recall_at_1 value: 4.852 - type: recall_at_10 value: 23.507 - type: recall_at_100 value: 49.775000000000006 - type: recall_at_1000 value: 79.308 - type: recall_at_3 value: 11.637 - type: recall_at_5 value: 15.947 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 86.03345827446948 - type: cos_sim_spearman value: 80.53174518259549 - type: euclidean_pearson value: 83.44538971660883 - type: euclidean_spearman value: 80.57344324098692 - type: manhattan_pearson value: 83.36528808195459 - type: manhattan_spearman value: 80.48931287157902 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.21363088257881 - type: cos_sim_spearman value: 75.56589127055523 - type: euclidean_pearson value: 82.32868324521908 - type: euclidean_spearman value: 75.31928550664554 - type: manhattan_pearson value: 82.31332875713211 - type: manhattan_spearman value: 75.35376322099196 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 85.09085593258487 - type: cos_sim_spearman value: 86.26355088415221 - type: euclidean_pearson value: 85.49646115361156 - type: euclidean_spearman value: 86.20652472228703 - type: manhattan_pearson value: 85.44084081123815 - type: manhattan_spearman value: 86.1162623448951 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.68250248349368 - type: cos_sim_spearman value: 82.29883673695083 - type: euclidean_pearson value: 84.17633035446019 - type: euclidean_spearman value: 82.19990511264791 - type: manhattan_pearson value: 84.17408410692279 - type: manhattan_spearman value: 82.249873895981 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.31878760045024 - type: cos_sim_spearman value: 88.7364409031183 - type: euclidean_pearson value: 88.230537618603 - type: euclidean_spearman value: 88.76484309646318 - type: manhattan_pearson value: 88.17689071136469 - type: manhattan_spearman value: 88.72809249037928 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.41078559110638 - type: cos_sim_spearman value: 85.27439135411049 - type: euclidean_pearson value: 84.5333571592088 - type: euclidean_spearman value: 85.25645460575957 - type: manhattan_pearson value: 84.38428921610226 - type: manhattan_spearman value: 85.07796040798796 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.82374132382576 - type: cos_sim_spearman value: 89.02101343562433 - type: euclidean_pearson value: 89.50729765458932 - type: euclidean_spearman value: 89.04184772869253 - type: manhattan_pearson value: 89.51737904059856 - type: manhattan_spearman value: 89.12925950440676 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.56051823873482 - type: cos_sim_spearman value: 68.50988748185463 - type: euclidean_pearson value: 69.16524346147456 - type: euclidean_spearman value: 68.61859952449579 - type: manhattan_pearson value: 69.10618915706995 - type: manhattan_spearman value: 68.36401769459522 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.4159693872625 - type: cos_sim_spearman value: 87.07819121764247 - type: euclidean_pearson value: 87.03013260863153 - type: euclidean_spearman value: 87.06547293631309 - type: manhattan_pearson value: 86.8129744446062 - type: manhattan_spearman value: 86.88494096335627 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.47758088996575 - type: mrr value: 96.17891458577733 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 57.538999999999994 - type: map_at_10 value: 66.562 - type: map_at_100 value: 67.254 - type: map_at_1000 value: 67.284 - type: map_at_3 value: 63.722 - type: map_at_5 value: 65.422 - type: mrr_at_1 value: 60.0 - type: mrr_at_10 value: 67.354 - type: mrr_at_100 value: 67.908 - type: mrr_at_1000 value: 67.93299999999999 - type: mrr_at_3 value: 65.056 - type: mrr_at_5 value: 66.43900000000001 - type: ndcg_at_1 value: 60.0 - type: ndcg_at_10 value: 70.858 - type: ndcg_at_100 value: 73.67099999999999 - type: ndcg_at_1000 value: 74.26700000000001 - type: ndcg_at_3 value: 65.911 - type: ndcg_at_5 value: 68.42200000000001 - type: precision_at_1 value: 60.0 - type: precision_at_10 value: 9.4 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.444 - type: precision_at_5 value: 17.0 - type: recall_at_1 value: 57.538999999999994 - type: recall_at_10 value: 83.233 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 69.883 - type: recall_at_5 value: 76.19399999999999 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82574257425742 - type: cos_sim_ap value: 95.78722833053911 - type: cos_sim_f1 value: 90.94650205761316 - type: cos_sim_precision value: 93.64406779661016 - type: cos_sim_recall value: 88.4 - type: dot_accuracy value: 99.83366336633664 - type: dot_ap value: 95.89733601612964 - type: dot_f1 value: 91.41981613891727 - type: dot_precision value: 93.42379958246346 - type: dot_recall value: 89.5 - type: euclidean_accuracy value: 99.82574257425742 - type: euclidean_ap value: 95.75227035138846 - type: euclidean_f1 value: 90.96509240246407 - type: euclidean_precision value: 93.45991561181435 - type: euclidean_recall value: 88.6 - type: manhattan_accuracy value: 99.82574257425742 - type: manhattan_ap value: 95.76278266220176 - type: manhattan_f1 value: 91.08409321175279 - type: manhattan_precision value: 92.29979466119097 - type: manhattan_recall value: 89.9 - type: max_accuracy value: 99.83366336633664 - type: max_ap value: 95.89733601612964 - type: max_f1 value: 91.41981613891727 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 61.905425988638605 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.159589881679736 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.0605499476397 - type: mrr value: 53.91594516594517 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.202718009067 - type: cos_sim_spearman value: 31.136199912366987 - type: dot_pearson value: 30.66329011927951 - type: dot_spearman value: 30.107664909625107 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.209 - type: map_at_10 value: 1.712 - type: map_at_100 value: 9.464 - type: map_at_1000 value: 23.437 - type: map_at_3 value: 0.609 - type: map_at_5 value: 0.9440000000000001 - type: mrr_at_1 value: 78.0 - type: mrr_at_10 value: 86.833 - type: mrr_at_100 value: 86.833 - type: mrr_at_1000 value: 86.833 - type: mrr_at_3 value: 85.333 - type: mrr_at_5 value: 86.833 - type: ndcg_at_1 value: 74.0 - type: ndcg_at_10 value: 69.14 - type: ndcg_at_100 value: 53.047999999999995 - type: ndcg_at_1000 value: 48.577 - type: ndcg_at_3 value: 75.592 - type: ndcg_at_5 value: 72.509 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 73.0 - type: precision_at_100 value: 54.44 - type: precision_at_1000 value: 21.326 - type: precision_at_3 value: 80.667 - type: precision_at_5 value: 77.2 - type: recall_at_1 value: 0.209 - type: recall_at_10 value: 1.932 - type: recall_at_100 value: 13.211999999999998 - type: recall_at_1000 value: 45.774 - type: recall_at_3 value: 0.644 - type: recall_at_5 value: 1.0290000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.609 - type: map_at_10 value: 8.334999999999999 - type: map_at_100 value: 14.604000000000001 - type: map_at_1000 value: 16.177 - type: map_at_3 value: 4.87 - type: map_at_5 value: 6.3149999999999995 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 45.047 - type: mrr_at_100 value: 45.808 - type: mrr_at_1000 value: 45.808 - type: mrr_at_3 value: 41.497 - type: mrr_at_5 value: 43.231 - type: ndcg_at_1 value: 30.612000000000002 - type: ndcg_at_10 value: 21.193 - type: ndcg_at_100 value: 34.97 - type: ndcg_at_1000 value: 46.69 - type: ndcg_at_3 value: 24.823 - type: ndcg_at_5 value: 22.872999999999998 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 17.959 - type: precision_at_100 value: 7.4079999999999995 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 25.85 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 2.609 - type: recall_at_10 value: 13.63 - type: recall_at_100 value: 47.014 - type: recall_at_1000 value: 83.176 - type: recall_at_3 value: 5.925 - type: recall_at_5 value: 8.574 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 72.80239999999999 - type: ap value: 15.497911013214791 - type: f1 value: 56.258411577947285 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.00452744765139 - type: f1 value: 61.42228624410908 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 50.00516915962345 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.62317458425225 - type: cos_sim_ap value: 72.95115658063823 - type: cos_sim_f1 value: 66.78976523344764 - type: cos_sim_precision value: 66.77215189873418 - type: cos_sim_recall value: 66.80738786279683 - type: dot_accuracy value: 85.62317458425225 - type: dot_ap value: 73.10385271517778 - type: dot_f1 value: 66.94853829427399 - type: dot_precision value: 61.74242424242424 - type: dot_recall value: 73.11345646437995 - type: euclidean_accuracy value: 85.65893783155511 - type: euclidean_ap value: 72.87428208473992 - type: euclidean_f1 value: 66.70919994896005 - type: euclidean_precision value: 64.5910551025451 - type: euclidean_recall value: 68.97097625329816 - type: manhattan_accuracy value: 85.59933241938367 - type: manhattan_ap value: 72.67282695064966 - type: manhattan_f1 value: 66.67537215983286 - type: manhattan_precision value: 66.00310237849017 - type: manhattan_recall value: 67.36147757255937 - type: max_accuracy value: 85.65893783155511 - type: max_ap value: 73.10385271517778 - type: max_f1 value: 66.94853829427399 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.69096130709822 - type: cos_sim_ap value: 85.30326978668063 - type: cos_sim_f1 value: 77.747088683189 - type: cos_sim_precision value: 75.4491451753115 - type: cos_sim_recall value: 80.189405605174 - type: dot_accuracy value: 88.43870066363954 - type: dot_ap value: 84.62999949222983 - type: dot_f1 value: 77.3074661963551 - type: dot_precision value: 73.93871239808828 - type: dot_recall value: 80.99784416384355 - type: euclidean_accuracy value: 88.70066363953894 - type: euclidean_ap value: 85.34184508966621 - type: euclidean_f1 value: 77.76871756856931 - type: euclidean_precision value: 74.97855917667239 - type: euclidean_recall value: 80.77456113335386 - type: manhattan_accuracy value: 88.68319944114566 - type: manhattan_ap value: 85.3026464242333 - type: manhattan_f1 value: 77.66561049296294 - type: manhattan_precision value: 74.4665818849795 - type: manhattan_recall value: 81.15183246073299 - type: max_accuracy value: 88.70066363953894 - type: max_ap value: 85.34184508966621 - type: max_f1 value: 77.76871756856931 --- <h1 align="center">GIST small Embedding v0</h1> GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning The model is fine-tuned on top of the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) # Data The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: - Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) - Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`). The MEDI Dataset is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. # Usage The model can be easily loaded using the Sentence Transformers library. ```Python import torch.nn.functional as F from sentence_transformers import SentenceTransformer revision = None # Replace with the specific revision to ensure reproducibility if the model is updated. model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision) texts = [ "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" ] # Compute embeddings embeddings = model.encode(texts, convert_to_tensor=True) # Compute cosine-similarity for each pair of sentences scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) print(scores.cpu().numpy()) ``` # Training Parameters Below are the training parameters used to fine-tune the model: ``` Epochs = 40 Warmup ratio = 0.1 Learning rate = 5e-6 Batch size = 16 Checkpoint step = 102000 Contrastive loss temperature = 0.01 ``` # Evaluation The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. # Citation Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 ``` @article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, journal={arXiv preprint arXiv:2402.16829}, year={2024}, URL={https://arxiv.org/abs/2402.16829} eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # Acknowledgements This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Darkhn/test-EXL2-3.0bpw-H6	Darkhn	2025-05-27T19:15:34Z	0	0	exllamav2	[ "exllamav2", "quantized", "license:mit", "region:us" ]	null	2025-05-27T19:14:55Z	--- library_name: exllamav2 license: mit tags: - exllamav2 - quantized --- # test-EXL2-3.0bpw-H6 EXL2 quantized model of `/mnt/test/output/merged_passthrough_20250527_185209_194400` (the original base model). ## Quantization Details - Bits per weight (bpw): 3.0 - Head Bits: 6 - Calibration Source: Measurement derived from model weights (no explicit dataset calibration or provided measurement for this specific quantization pass). Quantized using the [exllamav2 library](https://github.com/turboderp/exllamav2).
bigband/FatherlyAthena	bigband	2025-05-27T19:12:12Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T19:02:00Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
CCTV-Wiring-Cikgu-hd/Bocor.Video.CCTV.wiring.cikgu.video.nur.fadhilah.binti.zainal.guru.part.2.video	CCTV-Wiring-Cikgu-hd	2025-05-27T19:10:21Z	0	0	null	[ "region:us" ]	null	2025-05-27T19:07:43Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=CCTV-Wiring-Cikgu) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=CCTV-Wiring-Cikgu) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=CCTV-Wiring-Cikgu)
Kikinoking/MNLP_M2_quantized_model	Kikinoking	2025-05-27T19:09:04Z	16	0	null	[ "pytorch", "safetensors", "qwen3", "causal-lm", "qwen", "fine-tuned", "quantized", "mnlp", "8-bit", "compressed-tensors", "region:us" ]	null	2025-05-24T21:37:20Z	--- tags: - causal-lm - qwen - fine-tuned - quantized - mnlp --- # Qwen3-0.6B Full-Precision + W8A8 Quantized MCQA Model Repository: [Kikinoking/MNLP_M2_quantized_model](https://huggingface.co/Kikinoking/MNLP_M2_quantized_model) This is a fine-tuned Qwen-3-0.6B causal-LM, trained on a concatenation of multiple MCQA datasets and then quantized to 8-bit weights and activations using the compressed-tensors format. It is designed for multiple-choice QA tasks, evaluated with the LightEval EPFL MNLP suite. --- ## Model Details - Base architecture: Qwen-3 (0.6B parameters) - Pretrained checkpoint: `Qwen/Qwen3-0.6B-Base` - Fine-tuning data sources: - ScienceQA - QASC - OpenBookQA - MathQA - CommonsenseQA - MCQA prompts generated via ChatGPT (labeled `M1_chatgpt`) - Dataset split: 95% train / 5% validation - Tokenization: - `AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B-Base")` - Left padding, EOS token as pad_token - Sequence length capped at 2048 tokens --- ## Quantization - Method: `compressed-tensors` / `naive-quantized` - Precision: 8-bit weights + 8-bit activations - Layers kept in FP32: Language modeling head - Checkpoint: Compatible with CPU and GPU inference --- ## Evaluation Tested using LightEval EPFL MNLP on the MCQA task: ```bash lighteval accelerate --eval-mode lighteval --save-details --override-batch-size 8 --custom-tasks community_tasks/mnlp_mcqa_evals.py --output-dir out/lighteval_quant model_configs/quantized_model.yaml "community\|mnlp_mcqa_evals\|0\|0" Results: Accuracy: 0.30 ± 0.15 Normalized Accuracy: 0.30 ± 0.15 How to Use from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained( "Kikinoking/MNLP_M2_quantized_model", trust_remote_code=True ) model = AutoModelForCausalLM.from_pretrained( "Kikinoking/MNLP_M2_quantized_model", trust_remote_code=True, device_map="auto", ) License Being a 0.6B-parameter model, it may struggle with very long or ambiguous queries. Quantization can introduce a slight drop in accuracy (~5–10%). License: CC BY-NC 4.0 (inherits from the base Qwen-3 license)
Lubna-qureshi-viral/full.lubna.qureshi.viral.video.highway.lubna.qureshi.and.manohar.lal.dhakad.official	Lubna-qureshi-viral	2025-05-27T19:06:58Z	0	0	null	[ "region:us" ]	null	2025-05-27T18:56:44Z	[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Lubna-qureshi) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Lubna-qureshi) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Lubna-qureshi)
Mohamed-Aly/BABYLM-TOKENIZER-CHAR-PHON	Mohamed-Aly	2025-05-27T19:06:35Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T19:06:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Danielwu233/Llamma3.1-8B-Qlora	Danielwu233	2025-05-27T19:06:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:12:35Z	--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Danielwu233 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jaisalmer-kaka-hd/18.jaisalmer.kaka.jaisalmer.kaka.viral.jaisalmer.kaka.original.here.TRENDING	jaisalmer-kaka-hd	2025-05-27T19:05:13Z	0	0	null	[ "region:us" ]	null	2025-05-27T18:57:20Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=jaisalmer-kaka) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=jaisalmer-kaka) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=jaisalmer-kaka)
tylerachang/bigram-subnetworks-gpt2-large	tylerachang	2025-05-27T19:04:43Z	0	0	null	[ "eng", "arxiv:2504.15471", "license:apache-2.0", "region:us" ]	null	2025-04-21T04:54:16Z	--- license: apache-2.0 language: - eng --- # bigram-subnetworks-gpt2-large We release bigram subnetworks as described in [Chang and Bergen (2025)](https://arxiv.org/abs/2504.15471). These are sparse subsets of model parameters that recreate bigram predictions (next token predictions conditioned only on the current token) in Transformer language models. This repository contains the bigram subnetwork for [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large). ## Format A subnetwork file is a pickled Python dictionary that maps the original model parameter names to numpy binary masks with the same shapes as the original model parameters (1: keep, 0: drop). For details on usage, see: https://github.com/tylerachang/bigram-subnetworks. For details on how these subnetworks were trained, see [Chang and Bergen (2025)](https://arxiv.org/abs/2504.15471). For minimal usage, download the code at https://github.com/tylerachang/bigram-subnetworks (or just the file `circuit_loading_utils.py`) and run in Python: ``` from circuit_loading_utils import load_bigram_subnetwork_dict, load_subnetwork_model mask_dict = load_bigram_subnetwork_dict('openai-community/gpt2-large') model, tokenizer, config = load_subnetwork_model('openai-community/gpt2-large', mask_dict) ``` ## Citation <pre> @article{chang-bergen-2025-bigram, title={Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models}, author={Chang, Tyler A. and Bergen, Benjamin K.}, journal={Preprint}, year={2025}, url={https://arxiv.org/abs/2504.15471}, } </pre>
shaojintian/llaca-0.5B	shaojintian	2025-05-27T19:03:03Z	0	0	null	[ "safetensors", "ComplexFormer", "license:apache-2.0", "region:us" ]	null	2025-05-27T19:00:25Z	--- license: apache-2.0 ---
BootesVoid/cmb6uw4gb071rlexprcgvwbtx_cmb6uzmsu072plexpgfl5fg4h	BootesVoid	2025-05-27T19:01:36Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T19:01:34Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: vanessa_ --- # Cmb6Uw4Gb071Rlexprcgvwbtx_Cmb6Uzmsu072Plexpgfl5Fg4H <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `vanessa_` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "vanessa_", "lora_weights": "https://huggingface.co/BootesVoid/cmb6uw4gb071rlexprcgvwbtx_cmb6uzmsu072plexpgfl5fg4h/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb6uw4gb071rlexprcgvwbtx_cmb6uzmsu072plexpgfl5fg4h', weight_name='lora.safetensors') image = pipeline('vanessa_').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb6uw4gb071rlexprcgvwbtx_cmb6uzmsu072plexpgfl5fg4h/discussions) to add images that show off what you’ve made with this LoRA.
PRODRI007/ebooks	PRODRI007	2025-05-27T19:00:04Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T19:00:04Z	--- license: apache-2.0 ---
amaurypllx/MNLP_M2_quantized_model	amaurypllx	2025-05-27T18:59:25Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-27T18:59:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
HPLT/hplt2c_eng90-edu_fra10_checkpoints	HPLT	2025-05-27T18:57:59Z	0	0	null	[ "pytorch", "llama", "HPLT", "decoder", "en", "dataset:HPLT/HPLT2.0_cleaned", "arxiv:2503.10267", "license:apache-2.0", "region:us" ]	null	2025-05-26T08:49:52Z	--- language: - en tags: - HPLT - decoder license: apache-2.0 datasets: - HPLT/HPLT2.0_cleaned --- # HPLT v2.0 - Cleaned - English (90%) <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%> This is one of the decoder-only language models trained on [HPLT2.0_cleaned](https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned). All the HPLT decoder-only models use the same hyper-parameters, roughly following the llama architecture with 2.15B parameters in total: - hidden size: 2048 - attention heads: 32 - layers: 24 - sequence length: 2048 ## Intermediate checkpoints We are releasing intermediate checkpoints for each model at intervals of every 1000 training steps in separate branches. The naming convention is `checkpoint_00xxxx00`: for example, `checkpoint_0005000`. The checkpoints range from checkpoint_0001000 to checkpoint_0047684 and the latter is in the main branch. ## Cite us ```bibtex @misc{burchell2025expandedmassivemultilingualdataset, title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies}, author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu}, year={2025}, eprint={2503.10267}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.10267}, } ```
punith0110/sft-tiny-chatbot	punith0110	2025-05-27T18:53:54Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T18:52:31Z	--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: transformers model_name: sft-tiny-chatbot tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for sft-tiny-chatbot This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="punith0110/sft-tiny-chatbot", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
hjghjgn/hjgjhj	hjghjgn	2025-05-27T18:51:17Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-27T18:51:17Z	--- license: bigscience-bloom-rail-1.0 ---
aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs6	aamijar	2025-05-27T18:50:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T18:50:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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dimasik2987/20405430-db49-4bce-a10d-37a0e37de08b	dimasik2987	2025-05-27T18:49:33Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:quantized:NousResearch/Nous-Capybara-7B-V1.9", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-27T17:25:31Z	--- base_model: NousResearch/Nous-Capybara-7B-V1.9 library_name: transformers model_name: 20405430-db49-4bce-a10d-37a0e37de08b tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 20405430-db49-4bce-a10d-37a0e37de08b This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dimasik2987/20405430-db49-4bce-a10d-37a0e37de08b", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/qgmz9hnx) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
manuross1/nrmmtrfckdfll500	manuross1	2025-05-27T18:48:55Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T18:32:23Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: nrmmtrfckdfll500 --- # Nrmmtrfckdfll500 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `nrmmtrfckdfll500` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nrmmtrfckdfll500", "lora_weights": "https://huggingface.co/manuross1/nrmmtrfckdfll500/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('manuross1/nrmmtrfckdfll500', weight_name='lora.safetensors') image = pipeline('nrmmtrfckdfll500').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 750 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/manuross1/nrmmtrfckdfll500/discussions) to add images that show off what you’ve made with this LoRA.
beanne-valerie-hd/beanne.scandal.beanne.valerie.dela.cruz.beanne.valerie.dela.cruz.telegram	beanne-valerie-hd	2025-05-27T18:46:36Z	0	0	null	[ "region:us" ]	null	2025-05-27T18:44:33Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=beanne-valerie) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=beanne-valerie) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=beanne-valerie)
zinec/finetuned-eval-qwen3-0.6B	zinec	2025-05-27T18:46:23Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T18:41:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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plumpyfield/natix3	plumpyfield	2025-05-27T18:44:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T18:44:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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oskdabk/test_model_2	oskdabk	2025-05-27T18:41:57Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]	text-generation	2025-05-27T18:41:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RobertoNeglia/pepe_generator_sd2	RobertoNeglia	2025-05-27T18:37:21Z	0	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:stabilityai/stable-diffusion-2", "base_model:adapter:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-05-27T13:32:07Z	--- base_model: stabilityai/stable-diffusion-2 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - RobertoNeglia/pepe_generator_sd2 These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the RobertoNeglia/pepe_dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Nasrin02/Nasrin	Nasrin02	2025-05-27T18:34:34Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T18:34:34Z	--- license: apache-2.0 ---
BootesVoid/cmb17aape05h4u1cgfybugm82_cmb6tq3zd06rvlexpqoqenle8	BootesVoid	2025-05-27T18:32:57Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T18:32:56Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: LATINA --- # Cmb17Aape05H4U1Cgfybugm82_Cmb6Tq3Zd06Rvlexpqoqenle8 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `LATINA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LATINA", "lora_weights": "https://huggingface.co/BootesVoid/cmb17aape05h4u1cgfybugm82_cmb6tq3zd06rvlexpqoqenle8/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb17aape05h4u1cgfybugm82_cmb6tq3zd06rvlexpqoqenle8', weight_name='lora.safetensors') image = pipeline('LATINA').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb17aape05h4u1cgfybugm82_cmb6tq3zd06rvlexpqoqenle8/discussions) to add images that show off what you’ve made with this LoRA.
Mohamed-Aly/BABYLM-TOKENIZER-BPE-PHON-SPACELESS	Mohamed-Aly	2025-05-27T18:32:07Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T18:32:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BeckerAnas/still-universe-209	BeckerAnas	2025-05-27T18:31:38Z	0	0	transformers	[ "transformers", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "base_model:facebook/convnextv2-tiny-1k-224", "base_model:finetune:facebook/convnextv2-tiny-1k-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-27T10:46:05Z	--- library_name: transformers license: apache-2.0 base_model: facebook/convnextv2-tiny-1k-224 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: still-universe-209 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # still-universe-209 This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5721 - Accuracy: 0.6497 - Precision: 0.6965 - Recall: 0.6497 - F1: 0.6583 - Roc Auc: 0.8795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Roc Auc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:-------:\| \| 1.379 \| 1.0 \| 17 \| 1.3021 \| 0.4544 \| 0.5543 \| 0.4544 \| 0.4617 \| 0.7377 \| \| 1.3695 \| 2.0 \| 34 \| 1.2286 \| 0.5391 \| 0.5315 \| 0.5391 \| 0.5266 \| 0.7806 \| \| 1.1945 \| 3.0 \| 51 \| 1.1127 \| 0.5794 \| 0.5651 \| 0.5794 \| 0.5647 \| 0.8098 \| \| 1.0517 \| 4.0 \| 68 \| 0.8318 \| 0.5872 \| 0.6134 \| 0.5872 \| 0.5961 \| 0.8273 \| \| 1.0323 \| 5.0 \| 85 \| 0.8958 \| 0.5156 \| 0.6297 \| 0.5156 \| 0.5319 \| 0.8189 \| \| 0.9029 \| 6.0 \| 102 \| 0.7313 \| 0.5365 \| 0.6126 \| 0.5365 \| 0.5398 \| 0.8267 \| \| 0.9002 \| 7.0 \| 119 \| 0.7217 \| 0.5794 \| 0.5998 \| 0.5794 \| 0.5558 \| 0.8445 \| \| 0.7855 \| 8.0 \| 136 \| 0.6522 \| 0.6029 \| 0.6629 \| 0.6029 \| 0.6064 \| 0.8581 \| \| 0.756 \| 9.0 \| 153 \| 0.6371 \| 0.5964 \| 0.6263 \| 0.5964 \| 0.5653 \| 0.8643 \| \| 0.7164 \| 10.0 \| 170 \| 0.6291 \| 0.5690 \| 0.6930 \| 0.5690 \| 0.5780 \| 0.8579 \| \| 0.6894 \| 11.0 \| 187 \| 0.6194 \| 0.5938 \| 0.6360 \| 0.5938 \| 0.5735 \| 0.8699 \| \| 0.6606 \| 12.0 \| 204 \| 0.5834 \| 0.6289 \| 0.6906 \| 0.6289 \| 0.6402 \| 0.8742 \| \| 0.6273 \| 13.0 \| 221 \| 0.5766 \| 0.6510 \| 0.6972 \| 0.6510 \| 0.6607 \| 0.8780 \| \| 0.6046 \| 14.0 \| 238 \| 0.5732 \| 0.6497 \| 0.6965 \| 0.6497 \| 0.6583 \| 0.8790 \| \| 0.6255 \| 15.0 \| 255 \| 0.5721 \| 0.6497 \| 0.6965 \| 0.6497 \| 0.6583 \| 0.8795 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cpu - Datasets 3.6.0 - Tokenizers 0.21.0
Jobz-Hunting-pakistani-viral-videos/EXCLUSIVE.VIDEO.NOW.leaked.Jobz.Hunting.Sajal.Malik.viral.video.original	Jobz-Hunting-pakistani-viral-videos	2025-05-27T18:30:37Z	0	0	null	[ "region:us" ]	null	2025-05-27T18:30:01Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
Hsianchengfun/1B-80epoch	Hsianchengfun	2025-05-27T18:29:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T18:26:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
joshcd/MNLP_M2_document_encoder	joshcd	2025-05-27T18:29:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T18:15:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mlx-community/sarvam-m-8bit	mlx-community	2025-05-27T18:29:03Z	2	0	mlx	[ "mlx", "safetensors", "mistral", "text-generation", "conversational", "en", "bn", "hi", "kn", "gu", "mr", "ml", "or", "pa", "ta", "te", "base_model:sarvamai/sarvam-m", "base_model:finetune:sarvamai/sarvam-m", "license:apache-2.0", "8-bit", "region:us" ]	text-generation	2025-05-27T00:12:38Z	--- library_name: mlx license: apache-2.0 language: - en - bn - hi - kn - gu - mr - ml - or - pa - ta - te base_model: sarvamai/sarvam-m base_model_relation: finetune pipeline_tag: text-generation tags: - mlx --- # mlx-community/sarvam-m-8bit This model [mlx-community/sarvam-m-8bit](https://huggingface.co/mlx-community/sarvam-m-8bit) was converted to MLX format from [sarvamai/sarvam-m](https://huggingface.co/sarvamai/sarvam-m) using mlx-lm version 0.24.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/sarvam-m-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
Krashouse/Flux_nastya	Krashouse	2025-05-27T18:27:31Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-27T15:47:51Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
keko24/MNLP_M2_mcqa_model-W4A8-Dynamic-Per-Token	keko24	2025-05-27T18:24:06Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-generation	2025-05-27T18:23:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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RizhongLin/MNLP_M2_dpo_model	RizhongLin	2025-05-27T18:24:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T18:23:39Z	--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MicPulseGh3/MicPulseGH	MicPulseGh3	2025-05-27T18:22:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T18:22:06Z	--- license: apache-2.0 ---
margaritamikhelson/MNLP_M2_mcqa_model	margaritamikhelson	2025-05-27T18:21:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-27T18:20:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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hopvfds/bhdfgffg	hopvfds	2025-05-27T18:18:13Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-27T18:18:13Z	--- license: bigscience-bloom-rail-1.0 ---
Bonnief/mbert-am-100k-finetuned-II	Bonnief	2025-05-27T18:14:58Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-27T11:28:46Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: mbert-am-100k-finetuned-II results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbert-am-100k-finetuned-II This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.2069 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 100000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
katrina-lim-viral-videos/VIDEO.18.Katrina.Lim.Kiffy.Viral.Video.Full.Video.Original.Clip	katrina-lim-viral-videos	2025-05-27T18:14:38Z	0	0	null	[ "region:us" ]	null	2025-05-27T18:14:06Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
DoniaGasmii/MNLP_M2_dpo_pure_pref	DoniaGasmii	2025-05-27T18:13:29Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T18:13:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rtl-llm/qwen2.5coder-7b-origen-all-ordered-verilog-first	rtl-llm	2025-05-27T18:12:54Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T18:09:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ChrisKalahiki/mt0-large-lora	ChrisKalahiki	2025-05-27T18:11:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T18:11:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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vermoney/85fa0ba2-a848-4e57-a3c6-2be4516cf67d	vermoney	2025-05-27T18:11:41Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:quantized:NousResearch/Nous-Capybara-7B-V1.9", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-27T17:31:28Z	--- base_model: NousResearch/Nous-Capybara-7B-V1.9 library_name: transformers model_name: 85fa0ba2-a848-4e57-a3c6-2be4516cf67d tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 85fa0ba2-a848-4e57-a3c6-2be4516cf67d This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vermoney/85fa0ba2-a848-4e57-a3c6-2be4516cf67d", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-9/runs/05823jmk) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
rtl-llm/qwen2.5coder-7b-origen-vhdl-verilog-vhdl-pymtl	rtl-llm	2025-05-27T18:10:17Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T18:06:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dhintech/marian-ted2020-id-en-lg	dhintech	2025-05-27T18:09:32Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "indonesian", "english", "fine-tuned", "meeting-translation", "domain-adaptation", "enhanced", "id", "en", "dataset:ted_talks_iwslt", "base_model:Helsinki-NLP/opus-mt-id-en", "base_model:finetune:Helsinki-NLP/opus-mt-id-en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2025-05-27T12:51:05Z	--- language: - id - en license: apache-2.0 base_model: Helsinki-NLP/opus-mt-id-en tags: - translation - indonesian - english - marian - fine-tuned - meeting-translation - domain-adaptation - enhanced pipeline_tag: translation datasets: - ted_talks_iwslt library_name: transformers metrics: - bleu - rouge widget: - text: "Selamat pagi semuanya, mari kita mulai rapat hari ini." example_title: "Meeting Opening" - text: "Tim marketing akan bertanggung jawab untuk strategi ini." example_title: "Task Assignment" - text: "Database migration sudah selesai dan berjalan dengan lancar." example_title: "Technical Update" --- # Enhanced MarianMT Indonesian-English Translation (Meeting Domain Adaptation) This model is an enhanced fine-tuned version of [Helsinki-NLP/opus-mt-id-en](https://huggingface.co/Helsinki-NLP/opus-mt-id-en) with domain-specific adaptation for meeting and business contexts. ## 🎯 Model Highlights - Domain Adaptation: Specialized for meeting and business translation - Enhanced Dataset: TED2020 + 2000+ meeting-specific sentence pairs - Improved Performance: Better BLEU scores on meeting contexts - Robust Training: 80% dataset usage with domain mixing - Production Ready: Optimized for real-world meeting scenarios ## 📊 Performance Metrics \| Metric \| Base Model \| This Model \| Improvement \| \|--------\|------------\|------------\|-------------\| \| BLEU Score \| 1.467 \| 3.736 \| +154.6% \| \| Translation Speed \| 1.2s \| 0.14s \| -88.2% \| \| Meeting Context \| Standard \| Enhanced \| Domain Adapted \| ## 🚀 Model Details - Base Model: Helsinki-NLP/opus-mt-id-en - Training Dataset: TED2020 (80%) + Meeting Domain (10%) - Training Strategy: Domain adaptation with enhanced learning - Specialization: Business meetings, technical discussions, formal conversations - Training Date: 2025-05-27 - Languages: Indonesian (id) → English (en) - License: Apache 2.0 ## 🛠️ Usage ```python from transformers import MarianMTModel, MarianTokenizer # Load model and tokenizer model_name = "dhintech/marian-ted2020-id-en-lg" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) # Translate Indonesian to English def translate(text): inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128) outputs = model.generate( inputs, max_length=128, num_beams=3, early_stopping=True, do_sample=False ) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example usage indonesian_text = "Tim marketing akan bertanggung jawab untuk strategi ini." english_translation = translate(indonesian_text) print(english_translation) # Output: "The marketing team will be responsible for this strategy." ``` ## 📝 Example Translations ### Meeting Context Examples \| Indonesian \| English \| Context \| \|------------\|---------\|---------\| \| Selamat pagi semuanya, mari kita mulai rapat hari ini. \| Good morning everyone, let's start today's meeting. \| Meeting Opening \| \| Tim marketing akan bertanggung jawab untuk strategi ini. \| The marketing team will be responsible for this strategy. \| Task Assignment \| \| Database migration sudah selesai dan berjalan dengan lancar. \| Database migration is complete and running smoothly. \| Technical Update \| \| Budget yang disetujui adalah 500 juta rupiah. \| The approved budget is 500 million rupiah. \| Financial Discussion \| ## 🎯 Intended Use Cases - Business Meeting Translation: Real-time translation during meetings - Technical Documentation: Translating technical meeting notes - Corporate Communication: Formal business correspondence - Project Management: Translating project updates and reports - Training Materials: Educational and training content translation ## 📊 Training Configuration - Dataset Size: 118,626 sentence pairs - TED2020 Data: 80% of cleaned dataset - Meeting Domain Data: 10% specialized meeting content - Max Sequence Length: 128 tokens - Training Epochs: 12 - Learning Rate: 1e-05 - Batch Size: 12 (effective) ## 🔧 Technical Specifications - Model Architecture: MarianMT (Transformer-based) - Parameters: ~74M (with selective fine-tuning) - Max Input/Output Length: 128 tokens - Inference Time: ~0.14s per sentence - Memory Requirements: - GPU: 3GB VRAM minimum - CPU: 4GB RAM minimum ## 🚨 Limitations - Domain Specificity: Optimized for formal business/meeting contexts - Informal Language: May not perform optimally on very casual Indonesian - Regional Dialects: Trained primarily on standard Indonesian - Cultural Context: Some cultural nuances may be lost in translation ## 📚 Citation ```bibtex @misc{enhanced-marian-id-en-2025, title={Enhanced MarianMT Indonesian-English Translation (Meeting Domain Adaptation)}, author={DhinTech}, year={2025}, publisher={Hugging Face}, journal={Hugging Face Model Hub}, howpublished={\url{https://huggingface.co/dhintech/marian-id-en-enhanced}}, note={Enhanced with TED2020 and meeting-specific domain adaptation} } ``` ## 🙏 Acknowledgments - Base Model: Helsinki-NLP team for the original opus-mt-id-en model - Dataset: TED2020 corpus and custom meeting domain data - Framework*: Hugging Face Transformers team --- This model is specifically enhanced for Indonesian business meeting translation scenarios with domain adaptation techniques.*
JorgeTC/electra-corrected-POS	JorgeTC	2025-05-27T18:08:57Z	0	0	transformers	[ "transformers", "safetensors", "electra", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-27T18:08:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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18-Katrina-Lim-Kiffy-Viral-Video-Link-hd/INDIA.FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official	18-Katrina-Lim-Kiffy-Viral-Video-Link-hd	2025-05-27T18:08:35Z	0	0	null	[ "region:us" ]	null	2025-05-27T18:08:07Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
JorgeTC/miniLM-corrected-POS	JorgeTC	2025-05-27T18:03:20Z	0	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-05-27T18:03:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hexuan21/Qwen2.5-7B-EnergyQA_lora	hexuan21	2025-05-27T18:01:43Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-05-27T17:16:00Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
slang88/gemma-sql	slang88	2025-05-27T17:59:33Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-22T16:09:42Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-sql tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-sql This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="slang88/gemma-sql", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
phunghuy159/full_model_sft	phunghuy159	2025-05-27T17:59:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T17:44:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
othoi-113-viral-video-link-hdq/exclusive.link.othoiiii.viral.video.link.othoi.viral.video.link.1.13.second	othoi-113-viral-video-link-hdq	2025-05-27T17:58:04Z	0	0	null	[ "region:us" ]	null	2025-05-27T17:57:14Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
maksymveremchuk/deepseek_qwen_32B_v2.1	maksymveremchuk	2025-05-27T17:56:07Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:54:42Z	--- base_model: unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: maksymveremchuk - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
luis-orvium/prueba-memo-desde-checkpoint	luis-orvium	2025-05-27T17:54:11Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:53:50Z	--- base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: luis-orvium - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
maksymveremchuk/deepseek_qwen_23B_v2.1	maksymveremchuk	2025-05-27T17:53:12Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:53:12Z	--- base_model: unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: maksymveremchuk - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
othoi-113-viral-video-link-4k-hd/Original.othoiiii.viral.video.link.othoi.viral.video.link.1.13.second	othoi-113-viral-video-link-4k-hd	2025-05-27T17:51:46Z	0	0	null	[ "region:us" ]	null	2025-05-27T17:51:20Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4	BootesVoid	2025-05-27T17:51:22Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T17:51:20Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: LIA --- # Cmay2E8B8038Bu1Cguoswiyvb_Cmb6Sbsy206Islexp4Uw5Jtb4 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `LIA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LIA", "lora_weights": "https://huggingface.co/BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4', weight_name='lora.safetensors') image = pipeline('LIA').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4/discussions) to add images that show off what you’ve made with this LoRA.
aldigobbler/smol-moe-360M-v0.1	aldigobbler	2025-05-27T17:51:15Z	0	0	null	[ "region:us" ]	null	2025-05-27T17:48:52Z	# smol-moe-360M-v0.1 Experimental Sparse MoE (Mixture of Experts) with 4x 360M Llama model (smollmv2)s Router is a learned gating network, experts are: - HuggingFaceTB/SmolLM2-360M-Instruct - motexture/SmolLCoder-360M-Instruct - prithivMLmods/SmolLM2-CoT-360M - quwsarohi/SmolThink ## Training - Dataset: [`flytech/python-codes-25k`](https://huggingface.co/datasets/flytech/python-codes-25k) - Each sample is formatted as a chat: ``` [ {"role": "user", "content": instruction}, {"role": "assistant", "content": output} ] ``` - MoE layers at: 8, 12, 16, 20, 24, 28 (out of 32 total) - Top-2 routing (each token activates 2 out of 4 experts) - Trained for a few epochs, batch size 4, gradient accumulation 8, max length 1024 - Used AdamW, linear warmup, and auxiliary load balancing loss ## Model - Total params: ~1.5B (but only 2 experts active per token, so much faster than a dense 4x model) - All expert MLPs are included in the checkpoint, you don’t need the original models - Router and experts are trained end-to-end - Checkpoints include: `pytorch_model.bin` (full model) and `config.json` (architecture info) ## Results ### COME BACK LATER ITS TRAINING ## Notes - This is a real MoE: router is learned, experts are tied into the same model, and routing is sparse (top-2). - For research/experimentation only. - If you make something cool with it, let me know! --- smol-moe-360M-v0.1: for science, for fun, for smol code
BootesVoid/cmb68j487037slexpyp14cyxw_cmb69dzvn03avlexphlqqxvt8	BootesVoid	2025-05-27T17:49:43Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-27T17:49:41Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: jaylin --- # Cmb68J487037Slexpyp14Cyxw_Cmb69Dzvn03Avlexphlqqxvt8 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `jaylin` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "jaylin", "lora_weights": "https://huggingface.co/BootesVoid/cmb68j487037slexpyp14cyxw_cmb69dzvn03avlexphlqqxvt8/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb68j487037slexpyp14cyxw_cmb69dzvn03avlexphlqqxvt8', weight_name='lora.safetensors') image = pipeline('jaylin').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb68j487037slexpyp14cyxw_cmb69dzvn03avlexphlqqxvt8/discussions) to add images that show off what you’ve made with this LoRA.
Farmerobot/deepseek-r1-among-them	Farmerobot	2025-05-27T17:48:06Z	31	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T17:16:19Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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reza-rgb/MNLP_M2_dpo_model	reza-rgb	2025-05-27T17:47:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T17:45:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Smxldo/MNLP_M2_document_encoder	Smxldo	2025-05-27T17:44:55Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-21T09:33:52Z	--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L12-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L12-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs \| - \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs \| - \| 21,396,559 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs \| - \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) \| \| 304,525 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) \| \| 250,519 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) \| \| 250,460 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total \| \| 1,170,060,424** \|
nielsgl/olmOCR-7B-0225-preview-8bit	nielsgl	2025-05-27T17:43:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "mlx", "conversational", "en", "dataset:allenai/olmOCR-mix-0225", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-27T17:32:28Z	--- language: - en license: apache-2.0 datasets: - allenai/olmOCR-mix-0225 base_model: - Qwen/Qwen2-VL-7B-Instruct library_name: transformers tags: - mlx --- # nielsgl/olmOCR-7B-0225-preview-8bit This model was converted to MLX format from [`allenai/olmOCR-7B-0225-preview`]() using mlx-vlm version 0.1.26. Refer to the [original model card](https://huggingface.co/allenai/olmOCR-7B-0225-preview) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model nielsgl/olmOCR-7B-0225-preview-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image> ```
09-Sophie-Rain-Sophie-Rain-SpiderMan-Video/Sophie.Rain.Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video	09-Sophie-Rain-Sophie-Rain-SpiderMan-Video	2025-05-27T17:42:59Z	0	0	null	[ "region:us" ]	null	2025-05-27T17:42:47Z	18 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
eth-nlped/TutorRL-7B-think	eth-nlped	2025-05-27T17:42:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "math-tutor", "grpo", "conversational", "dataset:SynthLabsAI/Big-Math-RL-Verified", "arxiv:2505.15607", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T11:59:52Z	--- library_name: transformers license: apache-2.0 license_link: https://github.com/eth-lre/PedagogicalRL/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen2.5-7B-Instruct tags: - math-tutor - grpo datasets: - SynthLabsAI/Big-Math-RL-Verified --- # TutorRL-7B-think ## Overview TutorRL-7B-think is a fine-tuned variant of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), trained to act as a math tutor rather than a solver. It is aligned to pedagogical principles using reinforcement learning (GRPO) in a synthetic multi-turn classroom setting, without requiring any human-labeled data. This model was developed as part of the research project [From Problem-Solving to Teaching Problem-Solving](https://arxiv.org/abs/2505.15607), which proposes a scalable, annotation-free approach to training LLMs as educational tutors. Instead of directly answering questions, the model is optimized to scaffold reasoning, guide through Socratic questioning, and withhold final solutions when beneficial for learning. Repository: [https://github.com/eth-lre/PedagogicalRL](https://github.com/eth-lre/PedagogicalRL) ## Intended Use This model is intended for use in: * Interactive math tutoring * Socratic dialogue generation * Research on educational alignment of LLMs * Safe and indirect teaching in problem-solving contexts ## Thinking This model variant allows for hidden thinking. The thinking content is enclosed in tags: `<think> ... </think>`. ## Example Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "eth-nlped/TutorRL-7B-think" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") messages = [ {"role": "user", "content": "Can you help me solve 3x + 5 = 20?"} ] prompt = tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Citation If you use this model or build upon the training framework, please cite: ``` @misc{dinucujianu2025problemsolvingteachingproblemsolvingaligning, title={From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning}, author={David Dinucu-Jianu and Jakub Macina and Nico Daheim and Ido Hakimi and Iryna Gurevych and Mrinmaya Sachan}, year={2025}, eprint={2505.15607}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.15607} } ```
ngfh54456/bvcvdfsa	ngfh54456	2025-05-27T17:41:53Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-27T17:41:53Z	--- license: bigcode-openrail-m ---
dwi1205/A1B2C3	dwi1205	2025-05-27T17:41:17Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-27T17:41:17Z	--- license: apache-2.0 ---
Mohamed-Aly/BABYLM-TOKENIZER-CHAR-TXT	Mohamed-Aly	2025-05-27T17:40:43Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:40:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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07-Sophie-Rain-Sophie-Rain-SpiderMan-Video/Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video	07-Sophie-Rain-Sophie-Rain-SpiderMan-Video	2025-05-27T17:40:07Z	0	0	null	[ "region:us" ]	null	2025-05-27T17:39:35Z	18 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
jegeblad/poca-SoccerTwos	jegeblad	2025-05-27T17:40:02Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2025-05-27T06:17:59Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jegeblad/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
moelanoby/ALM-Qwen-0.5B-testing	moelanoby	2025-05-27T17:35:41Z	0	1	transformers	[ "transformers", "safetensors", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T13:55:25Z	--- base_model: - Qwen/Qwen2.5-0.5B-Instruct library_name: transformers --- # ALM-Qwen Model: ALM-Qwen-0.5B-testing This repository contains an Attention-Linked Memory augmented Qwen model (ALM-Qwen). ## Model Components * AttentionLinkedMemory (ALM): A custom PyTorch module for two-level attention-based retrieval from structured memory. (See `ALM.py`) * QwenGenerator: Wraps a Hugging Face Qwen model (e.g., Qwen2.5-0.5B-Instruct or Qwen2.5-7B-Instruct) for text generation. * ALMQwenModel_HF: The main class orchestrating the ALM retrieval and Qwen generation. (See `alm_qwen.py`) * Saved Weights & Config: * `alm_layer_state_dict.pth`: Trained weights for the ALM layer. * `alm_qwen_hf_config.json`: Configuration for the `ALMQwenModel_HF`, including ALM parameters and paths to the Qwen components. * `qwen_generator/`: Contains the saved Hugging Face Qwen model and tokenizer. ## How to Use 1. Prerequisites: ```bash pip install torch transformers huggingface_hub sentencepiece accelerate # Add other dependencies if any, e.g., bitsandbytes for quantization ``` 2. Clone the repository (or download files manually): ```bash git lfs install # if large files are used, though typically not for these components directly git clone https://huggingface.co/moelanoby/ALM-Qwen-0.5B-testing cd ALM-Qwen-0.5B-testing ``` 3. Load the model in Python: ```python from alm_qwen import ALMQwenModel_HF # Make sure alm_qwen_hf.py and ALM.py are in your PYTHONPATH import torch # Desired device device = "cuda" if torch.cuda.is_available() else "cpu" # Path to the directory where you cloned/downloaded the model model_directory = "." # Or the specific path if you are running from outside the cloned repo # Load the model loaded_model = ALMQwenModel_HF.load_model(model_directory, device=device) print("ALM-Qwen model loaded successfully!") # --- Prepare Dummy Input Data (similar to the example in alm_qwen_hf.py) --- # batch_size = 1 # alm_query_dim = loaded_model.alm_config['query_dim'] # alm_memory_dim = loaded_model.alm_config['memory_dim'] # num_kb_buckets = 3 # Example # max_kb_items_per_bucket = 5 # Example # query_texts = ["What is the capital of France?"] # query_embeddings_for_alm = torch.randn(batch_size, alm_query_dim) # memory_item_embeddings = torch.randn(batch_size, num_kb_buckets, max_kb_items_per_bucket, alm_memory_dim) # memory_text_items = [[["Paris is the capital of France." for _ in range(max_kb_items_per_bucket)] for _ in range(num_kb_buckets)] for _ in range(batch_size)] # memory_mask = torch.ones(batch_size, num_kb_buckets, max_kb_items_per_bucket, dtype=torch.bool) # memory_mask[:, :, -1] = False # Example mask # # Run inference # generated_answers, _, _ = loaded_model( # query_texts, # query_embeddings_for_alm, # memory_item_embeddings, # memory_text_items, # memory_mask # ) # print(f"Query: {query_texts[0]}") # print(f"Answer: {generated_answers[0]}") ``` ## Training The ALM layer (`alm_layer_state_dict.pth`) might have been trained. The Qwen model inside `qwen_generator/` is typically a pre-trained model from Hugging Face, possibly fine-tuned. ## Notes * The Qwen model components can be large. Ensure you have sufficient disk space and network bandwidth. * The `load_model` method in `alm_qwen_hf.py` handles the reconstruction of the composite model. * If any errors happen use alm_qwen.py directly ---
aldigobbler/smollmv2-135Mx3E-MoE-v0.1	aldigobbler	2025-05-27T17:29:57Z	0	0	null	[ "region:us" ]	null	2025-05-27T15:37:44Z	# !! "moe" - routed inference between 3 different models without any tying experimental MoE with 3 experts totalling 480m~ params router is roughly 70M params no loss chart for this router trained on 15 samples
davanstrien/SmolLM2-360M-tldr-sft-2025-05-27_18-14	davanstrien	2025-05-27T17:29:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:HuggingFaceTB/SmolLM2-360M", "base_model:finetune:HuggingFaceTB/SmolLM2-360M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T16:15:27Z	--- base_model: HuggingFaceTB/SmolLM2-360M library_name: transformers model_name: SmolLM2-360M-tldr-sft-2025-05-27_18-14 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for SmolLM2-360M-tldr-sft-2025-05-27_18-14 This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="davanstrien/SmolLM2-360M-tldr-sft-2025-05-27_18-14", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/davanstrien/huggingface/runs/tsp2cqil) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
lisabdunlap/balanced_sft_long_e10	lisabdunlap	2025-05-27T17:29:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-27T17:28:07Z	--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MarceauBBB/qwen3-0.6B-Base-ORPO-OpenAnswers	MarceauBBB	2025-05-27T17:26:46Z	21	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T21:14:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
AShi846/MNLP_M2_document_encoder	AShi846	2025-05-27T17:22:55Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-27T14:33:12Z	--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs \| - \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs \| - \| 21,396,559 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs \| - \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) \| \| 304,525 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) \| \| 250,519 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) \| \| 250,460 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total \| \| 1,170,060,424** \|
OlofBen/HeartLM-v4.3	OlofBen	2025-05-27T17:22:46Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-27T17:05:36Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

GoYM/gemma-product-description

GoYM

2025-05-27T19:33:15Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-4b-pt", "base_model:finetune:google/gemma-3-4b-pt", "endpoints_compatible", "region:us" ]

null

2025-05-16T03:32:47Z

--- base_model: google/gemma-3-4b-pt library_name: transformers model_name: gemma-product-description tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-product-description This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="GoYM/gemma-product-description", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

smartmyapp/ladji5_2

smartmyapp

2025-05-27T19:33:13Z

0

transformers

[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

text-to-audio

2025-05-27T12:17:14Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

zidsi/Zlatorog-12B-Instruct-Beta-GGUF

zidsi

2025-05-27T19:33:05Z

9

0

transformers

[ "transformers", "gguf", "full", "generated_from_trainer", "text-generation", "sl", "en", "base_model:zidsi/MistralNemoCPT6", "base_model:quantized:zidsi/MistralNemoCPT6", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-05-23T15:41:35Z

--- library_name: transformers license: cc-by-nc-nd-4.0 base_model: zidsi/MistralNemoCPT6 tags: - full - generated_from_trainer model-index: - name: zlatorog_12b_sft_v6 results: [] language: - sl - en pipeline_tag: text-generation ---  # Zlatorog-12B-Instruct-Beta This model is a fine-tuned version of [zidsi/MistralNemoCPT6](https://huggingface.co/zidsi/MistralNemoCPT6) on the custom mix of SFT datasets. ## Model description More information needed ## Intended uses & limitations Research explore and have fun with Slovenian LLM :) ## Training and evaluation data Bad standard Slovenian benchmarks results **but** sometimes impresssive "real world" prompt responses :) Reduced hallucinations rate on "Who is ...?" prompts. Tools use to be evaluated Up to 16k ctx should work OK, for longer contexts training data would be required to improve CPT Long stage More information needed ## GGUF The HF model was coverted to GGUF using llama.cpp

yutakas/llava-v1.6-mistral-7b-hf-test

yutakas

2025-05-27T19:32:51Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T18:49:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

vermoney/cdc5327d-3aaa-49a2-a63f-78fc847a8490

vermoney

2025-05-27T19:32:22Z

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "base_model:adapter:NousResearch/Nous-Hermes-2-SOLAR-10.7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]

null

2025-05-27T18:35:14Z

--- library_name: peft license: apache-2.0 base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B tags: - axolotl - generated_from_trainer model-index: - name: cdc5327d-3aaa-49a2-a63f-78fc847a8490 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e9539959e5b475cc_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/cdc5327d-3aaa-49a2-a63f-78fc847a8490 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/e9539959e5b475cc_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 074d0027-87b6-4ea0-a8be-5f7675bf7878 wandb_project: s56-9 wandb_run: your_name wandb_runid: 074d0027-87b6-4ea0-a8be-5f7675bf7878 warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details> # cdc5327d-3aaa-49a2-a63f-78fc847a8490 This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9817 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.7481 | 0.0132 | 280 | 0.9817 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

JasperV13/yehia-7b-CoT

JasperV13

2025-05-27T19:31:54Z

0

null

[ "yehia_reasoning", "arabic", "reasoning", "cot", "chain-of-thought", "yehia", "custom_code", "ar", "base_model:Navid-AI/Yehia-7B-preview", "base_model:finetune:Navid-AI/Yehia-7B-preview", "license:apache-2.0", "region:us" ]

null

2025-05-27T19:31:53Z

--- language: ar license: apache-2.0 tags: - arabic - reasoning - cot - chain-of-thought - yehia base_model: Navid-AI/Yehia-7B-preview --- # Yehia-7B Chain of Thought Model نموذج يهيا المحسن بتقنية التفكير المتسلسل (Chain of Thought) ## الوصف هذا النموذج يطبق تقنية التفكير المتسلسل تلقائياً عند طرح أي سؤال. يقوم النموذج بتقسيم المشكلة إلى خطوات منطقية متتابعة للوصول إلى الحل الصحيح. ## الاستخدام ```python from transformers import AutoModel, AutoTokenizer # تحميل النموذج model = AutoModel.from_pretrained("JasperV13/yehia-7b-CoT", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("JasperV13/yehia-7b-CoT") # طرح سؤال question = "احسب 25 × 16" inputs = tokenizer(question, return_tensors="pt") outputs = model.generate(**inputs) answer = tokenizer.decode(outputs[0], skip_special_tokens=True) print(answer) ``` ## مثال على النتيجة ``` السؤال: احسب 25 × 16 دعني أحل هذا السؤال خطوة بخطوة: الخطوة 1: سأقوم بضرب 25 في 16 الخطوة 2: يمكنني تقسيم هذا إلى (20 + 5) × 16 الخطوة 3: = (20 × 16) + (5 × 16) الخطوة 4: = 320 + 80 الخطوة 5: = 400 الإجابة النهائية: 400 ``` ## المميزات - ✅ تفكير خطوة بخطوة تلقائي - ✅ محسن للغة العربية - ✅ متوافق مع مكتبة transformers - ✅ دقة عالية في الحسابات والمنطق - ✅ سهولة الاستخدام ## متطلبات ```bash pip install transformers torch ``` ## النموذج الأساسي يستخدم هذا النموذج `Navid-AI/Yehia-7B-preview` كنموذج أساسي مع إضافة طبقة التفكير المتسلسل. ## الاستخدام المتقدم ```python # للحصول على تحكم أكبر في المعاملات from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("JasperV13/yehia-7b-CoT", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("JasperV13/yehia-7b-CoT") # يمكنك تخصيص معاملات التوليد question = "كيف أحل معادلة من الدرجة الثانية؟" inputs = tokenizer(question, return_tensors="pt") # مع معاملات مخصصة outputs = model.generate( **inputs, max_length=500, temperature=0.7, do_sample=True ) answer = tokenizer.decode(outputs[0], skip_special_tokens=True) print(answer) ``` ## الإصدار - الإصدار: 1.0 - تاريخ الإنشاء: 2025 - المطور: JasperV13 ## الترخيص Apache 2.0

07-jobz-hunting-viral-video/Original.Full.Clip.Jobz.Hunting.Sajal.Malik.Viral.nimra.mehra.Video.Leaks.Official

07-jobz-hunting-viral-video

2025-05-27T19:30:55Z

0

null

[ "region:us" ]

null

2025-05-27T19:30:32Z

<a href="https://tv2online.com/Video/?v=xxx" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a> <a href="https://tv2online.com/Video/?v=xxx" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a> <a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Video/?v=xxx"><img border="Viral+Leaked+Video" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a>

nimra-mehra-hd/Link.Video.18.nimra.mehra.jobz.hunting.video.nimra.mehra.video.nimra.mehra

nimra-mehra-hd

2025-05-27T19:30:23Z

0

null

[ "region:us" ]

null

2025-05-27T19:26:02Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=nimra-mehra) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=nimra-mehra) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=nimra-mehra)

task-aware/Llama_3.2_3B_Instruct

task-aware

2025-05-27T19:30:12Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-24T16:38:32Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

goalaphx/outputs_qcm_then_fitb

goalaphx

2025-05-27T19:26:42Z

0

peft

[ "peft", "safetensors", "gguf", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-05-27T19:20:27Z

--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2

shishirahm3d/lawyer

shishirahm3d

2025-05-27T19:26:05Z

4

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2025-05-27T03:30:50Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** shishirahm3d - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit

Alexhuou/MNLP_M2_document_encoder

Alexhuou

2025-05-27T19:25:39Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "mteb", "sentence-similarity", "Sentence Transformers", "en", "arxiv:2308.03281", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-05-27T19:22:29Z

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.62686567164178 - type: ap value: 34.46944126809772 - type: f1 value: 66.23684353950857 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.51805 - type: ap value: 89.49842783330848 - type: f1 value: 92.51112169431808 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.074 - type: f1 value: 48.44785682572955 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 32.077 - type: map_at_10 value: 48.153 - type: map_at_100 value: 48.963 - type: map_at_1000 value: 48.966 - type: map_at_3 value: 43.184 - type: map_at_5 value: 46.072 - type: mrr_at_1 value: 33.073 - type: mrr_at_10 value: 48.54 - type: mrr_at_100 value: 49.335 - type: mrr_at_1000 value: 49.338 - type: mrr_at_3 value: 43.563 - type: mrr_at_5 value: 46.383 - type: ndcg_at_1 value: 32.077 - type: ndcg_at_10 value: 57.158 - type: ndcg_at_100 value: 60.324999999999996 - type: ndcg_at_1000 value: 60.402 - type: ndcg_at_3 value: 46.934 - type: ndcg_at_5 value: 52.158 - type: precision_at_1 value: 32.077 - type: precision_at_10 value: 8.591999999999999 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 19.275000000000002 - type: precision_at_5 value: 14.111 - type: recall_at_1 value: 32.077 - type: recall_at_10 value: 85.917 - type: recall_at_100 value: 99.075 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 57.824 - type: recall_at_5 value: 70.555 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.619246083417295 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.3574067664688 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.06359661829253 - type: mrr value: 76.15596007562766 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 90.25407547368691 - type: cos_sim_spearman value: 88.65081514968477 - type: euclidean_pearson value: 88.14857116664494 - type: euclidean_spearman value: 88.50683596540692 - type: manhattan_pearson value: 87.9654797992225 - type: manhattan_spearman value: 88.21164851646908 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.05844155844157 - type: f1 value: 86.01555597681825 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.10510519739522 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.84689960264385 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.800000000000004 - type: map_at_10 value: 44.857 - type: map_at_100 value: 46.512 - type: map_at_1000 value: 46.635 - type: map_at_3 value: 41.062 - type: map_at_5 value: 43.126 - type: mrr_at_1 value: 39.628 - type: mrr_at_10 value: 50.879 - type: mrr_at_100 value: 51.605000000000004 - type: mrr_at_1000 value: 51.641000000000005 - type: mrr_at_3 value: 48.14 - type: mrr_at_5 value: 49.835 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 51.819 - type: ndcg_at_100 value: 57.318999999999996 - type: ndcg_at_1000 value: 58.955999999999996 - type: ndcg_at_3 value: 46.409 - type: ndcg_at_5 value: 48.825 - type: precision_at_1 value: 39.628 - type: precision_at_10 value: 10.072000000000001 - type: precision_at_100 value: 1.625 - type: precision_at_1000 value: 0.21 - type: precision_at_3 value: 22.556 - type: precision_at_5 value: 16.309 - type: recall_at_1 value: 32.800000000000004 - type: recall_at_10 value: 65.078 - type: recall_at_100 value: 87.491 - type: recall_at_1000 value: 97.514 - type: recall_at_3 value: 49.561 - type: recall_at_5 value: 56.135999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.614 - type: map_at_10 value: 43.578 - type: map_at_100 value: 44.897 - type: map_at_1000 value: 45.023 - type: map_at_3 value: 40.282000000000004 - type: map_at_5 value: 42.117 - type: mrr_at_1 value: 40.510000000000005 - type: mrr_at_10 value: 49.428 - type: mrr_at_100 value: 50.068999999999996 - type: mrr_at_1000 value: 50.111000000000004 - type: mrr_at_3 value: 47.176 - type: mrr_at_5 value: 48.583999999999996 - type: ndcg_at_1 value: 40.510000000000005 - type: ndcg_at_10 value: 49.478 - type: ndcg_at_100 value: 53.852 - type: ndcg_at_1000 value: 55.782 - type: ndcg_at_3 value: 45.091 - type: ndcg_at_5 value: 47.19 - type: precision_at_1 value: 40.510000000000005 - type: precision_at_10 value: 9.363000000000001 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 21.741 - type: precision_at_5 value: 15.465000000000002 - type: recall_at_1 value: 32.614 - type: recall_at_10 value: 59.782000000000004 - type: recall_at_100 value: 78.012 - type: recall_at_1000 value: 90.319 - type: recall_at_3 value: 46.825 - type: recall_at_5 value: 52.688 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 40.266000000000005 - type: map_at_10 value: 53.756 - type: map_at_100 value: 54.809 - type: map_at_1000 value: 54.855 - type: map_at_3 value: 50.073 - type: map_at_5 value: 52.293 - type: mrr_at_1 value: 46.332 - type: mrr_at_10 value: 57.116 - type: mrr_at_100 value: 57.767 - type: mrr_at_1000 value: 57.791000000000004 - type: mrr_at_3 value: 54.461999999999996 - type: mrr_at_5 value: 56.092 - type: ndcg_at_1 value: 46.332 - type: ndcg_at_10 value: 60.092 - type: ndcg_at_100 value: 64.034 - type: ndcg_at_1000 value: 64.937 - type: ndcg_at_3 value: 54.071000000000005 - type: ndcg_at_5 value: 57.254000000000005 - type: precision_at_1 value: 46.332 - type: precision_at_10 value: 9.799 - type: precision_at_100 value: 1.278 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.368000000000002 - type: precision_at_5 value: 16.89 - type: recall_at_1 value: 40.266000000000005 - type: recall_at_10 value: 75.41499999999999 - type: recall_at_100 value: 92.01700000000001 - type: recall_at_1000 value: 98.379 - type: recall_at_3 value: 59.476 - type: recall_at_5 value: 67.297 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 28.589 - type: map_at_10 value: 37.755 - type: map_at_100 value: 38.881 - type: map_at_1000 value: 38.954 - type: map_at_3 value: 34.759 - type: map_at_5 value: 36.544 - type: mrr_at_1 value: 30.734 - type: mrr_at_10 value: 39.742 - type: mrr_at_100 value: 40.774 - type: mrr_at_1000 value: 40.824 - type: mrr_at_3 value: 37.137 - type: mrr_at_5 value: 38.719 - type: ndcg_at_1 value: 30.734 - type: ndcg_at_10 value: 42.978 - type: ndcg_at_100 value: 48.309000000000005 - type: ndcg_at_1000 value: 50.068 - type: ndcg_at_3 value: 37.361 - type: ndcg_at_5 value: 40.268 - type: precision_at_1 value: 30.734 - type: precision_at_10 value: 6.565 - type: precision_at_100 value: 0.964 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.096 - type: recall_at_1 value: 28.589 - type: recall_at_10 value: 57.126999999999995 - type: recall_at_100 value: 81.051 - type: recall_at_1000 value: 94.027 - type: recall_at_3 value: 42.045 - type: recall_at_5 value: 49.019 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.5 - type: map_at_10 value: 27.950999999999997 - type: map_at_100 value: 29.186 - type: map_at_1000 value: 29.298000000000002 - type: map_at_3 value: 25.141000000000002 - type: map_at_5 value: 26.848 - type: mrr_at_1 value: 22.637 - type: mrr_at_10 value: 32.572 - type: mrr_at_100 value: 33.472 - type: mrr_at_1000 value: 33.533 - type: mrr_at_3 value: 29.747 - type: mrr_at_5 value: 31.482 - type: ndcg_at_1 value: 22.637 - type: ndcg_at_10 value: 33.73 - type: ndcg_at_100 value: 39.568 - type: ndcg_at_1000 value: 42.201 - type: ndcg_at_3 value: 28.505999999999997 - type: ndcg_at_5 value: 31.255 - type: precision_at_1 value: 22.637 - type: precision_at_10 value: 6.281000000000001 - type: precision_at_100 value: 1.073 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 13.847000000000001 - type: precision_at_5 value: 10.224 - type: recall_at_1 value: 18.5 - type: recall_at_10 value: 46.744 - type: recall_at_100 value: 72.072 - type: recall_at_1000 value: 91.03999999999999 - type: recall_at_3 value: 32.551 - type: recall_at_5 value: 39.533 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.602 - type: map_at_10 value: 42.18 - type: map_at_100 value: 43.6 - type: map_at_1000 value: 43.704 - type: map_at_3 value: 38.413000000000004 - type: map_at_5 value: 40.626 - type: mrr_at_1 value: 37.344 - type: mrr_at_10 value: 47.638000000000005 - type: mrr_at_100 value: 48.485 - type: mrr_at_1000 value: 48.52 - type: mrr_at_3 value: 44.867000000000004 - type: mrr_at_5 value: 46.566 - type: ndcg_at_1 value: 37.344 - type: ndcg_at_10 value: 48.632 - type: ndcg_at_100 value: 54.215 - type: ndcg_at_1000 value: 55.981 - type: ndcg_at_3 value: 42.681999999999995 - type: ndcg_at_5 value: 45.732 - type: precision_at_1 value: 37.344 - type: precision_at_10 value: 8.932 - type: precision_at_100 value: 1.376 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 20.276 - type: precision_at_5 value: 14.726 - type: recall_at_1 value: 30.602 - type: recall_at_10 value: 62.273 - type: recall_at_100 value: 85.12100000000001 - type: recall_at_1000 value: 96.439 - type: recall_at_3 value: 45.848 - type: recall_at_5 value: 53.615 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.952 - type: map_at_10 value: 35.177 - type: map_at_100 value: 36.59 - type: map_at_1000 value: 36.703 - type: map_at_3 value: 31.261 - type: map_at_5 value: 33.222 - type: mrr_at_1 value: 29.337999999999997 - type: mrr_at_10 value: 40.152 - type: mrr_at_100 value: 40.963 - type: mrr_at_1000 value: 41.016999999999996 - type: mrr_at_3 value: 36.91 - type: mrr_at_5 value: 38.685 - type: ndcg_at_1 value: 29.337999999999997 - type: ndcg_at_10 value: 41.994 - type: ndcg_at_100 value: 47.587 - type: ndcg_at_1000 value: 49.791000000000004 - type: ndcg_at_3 value: 35.27 - type: ndcg_at_5 value: 38.042 - type: precision_at_1 value: 29.337999999999997 - type: precision_at_10 value: 8.276 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.164 - type: precision_at_3 value: 17.161 - type: precision_at_5 value: 12.671 - type: recall_at_1 value: 23.952 - type: recall_at_10 value: 57.267 - type: recall_at_100 value: 80.886 - type: recall_at_1000 value: 95.611 - type: recall_at_3 value: 38.622 - type: recall_at_5 value: 45.811 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.092083333333335 - type: map_at_10 value: 37.2925 - type: map_at_100 value: 38.57041666666666 - type: map_at_1000 value: 38.68141666666667 - type: map_at_3 value: 34.080000000000005 - type: map_at_5 value: 35.89958333333333 - type: mrr_at_1 value: 31.94758333333333 - type: mrr_at_10 value: 41.51049999999999 - type: mrr_at_100 value: 42.36099999999999 - type: mrr_at_1000 value: 42.4125 - type: mrr_at_3 value: 38.849583333333335 - type: mrr_at_5 value: 40.448249999999994 - type: ndcg_at_1 value: 31.94758333333333 - type: ndcg_at_10 value: 43.17633333333333 - type: ndcg_at_100 value: 48.45241666666668 - type: ndcg_at_1000 value: 50.513999999999996 - type: ndcg_at_3 value: 37.75216666666667 - type: ndcg_at_5 value: 40.393833333333326 - type: precision_at_1 value: 31.94758333333333 - type: precision_at_10 value: 7.688916666666666 - type: precision_at_100 value: 1.2250833333333333 - type: precision_at_1000 value: 0.1595 - type: precision_at_3 value: 17.465999999999998 - type: precision_at_5 value: 12.548083333333333 - type: recall_at_1 value: 27.092083333333335 - type: recall_at_10 value: 56.286583333333326 - type: recall_at_100 value: 79.09033333333333 - type: recall_at_1000 value: 93.27483333333335 - type: recall_at_3 value: 41.35325 - type: recall_at_5 value: 48.072750000000006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.825 - type: map_at_10 value: 33.723 - type: map_at_100 value: 34.74 - type: map_at_1000 value: 34.824 - type: map_at_3 value: 31.369000000000003 - type: map_at_5 value: 32.533 - type: mrr_at_1 value: 29.293999999999997 - type: mrr_at_10 value: 36.84 - type: mrr_at_100 value: 37.681 - type: mrr_at_1000 value: 37.742 - type: mrr_at_3 value: 34.79 - type: mrr_at_5 value: 35.872 - type: ndcg_at_1 value: 29.293999999999997 - type: ndcg_at_10 value: 38.385999999999996 - type: ndcg_at_100 value: 43.327 - type: ndcg_at_1000 value: 45.53 - type: ndcg_at_3 value: 33.985 - type: ndcg_at_5 value: 35.817 - type: precision_at_1 value: 29.293999999999997 - type: precision_at_10 value: 6.12 - type: precision_at_100 value: 0.9329999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 14.621999999999998 - type: precision_at_5 value: 10.030999999999999 - type: recall_at_1 value: 25.825 - type: recall_at_10 value: 49.647000000000006 - type: recall_at_100 value: 72.32300000000001 - type: recall_at_1000 value: 88.62400000000001 - type: recall_at_3 value: 37.366 - type: recall_at_5 value: 41.957 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.139 - type: map_at_10 value: 26.107000000000003 - type: map_at_100 value: 27.406999999999996 - type: map_at_1000 value: 27.535999999999998 - type: map_at_3 value: 23.445 - type: map_at_5 value: 24.916 - type: mrr_at_1 value: 21.817 - type: mrr_at_10 value: 29.99 - type: mrr_at_100 value: 31.052000000000003 - type: mrr_at_1000 value: 31.128 - type: mrr_at_3 value: 27.627000000000002 - type: mrr_at_5 value: 29.005 - type: ndcg_at_1 value: 21.817 - type: ndcg_at_10 value: 31.135 - type: ndcg_at_100 value: 37.108000000000004 - type: ndcg_at_1000 value: 39.965 - type: ndcg_at_3 value: 26.439 - type: ndcg_at_5 value: 28.655 - type: precision_at_1 value: 21.817 - type: precision_at_10 value: 5.757000000000001 - type: precision_at_100 value: 1.036 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 12.537 - type: precision_at_5 value: 9.229 - type: recall_at_1 value: 18.139 - type: recall_at_10 value: 42.272999999999996 - type: recall_at_100 value: 68.657 - type: recall_at_1000 value: 88.93799999999999 - type: recall_at_3 value: 29.266 - type: recall_at_5 value: 34.892 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 27.755000000000003 - type: map_at_10 value: 37.384 - type: map_at_100 value: 38.56 - type: map_at_1000 value: 38.655 - type: map_at_3 value: 34.214 - type: map_at_5 value: 35.96 - type: mrr_at_1 value: 32.369 - type: mrr_at_10 value: 41.625 - type: mrr_at_100 value: 42.449 - type: mrr_at_1000 value: 42.502 - type: mrr_at_3 value: 38.899 - type: mrr_at_5 value: 40.489999999999995 - type: ndcg_at_1 value: 32.369 - type: ndcg_at_10 value: 43.287 - type: ndcg_at_100 value: 48.504999999999995 - type: ndcg_at_1000 value: 50.552 - type: ndcg_at_3 value: 37.549 - type: ndcg_at_5 value: 40.204 - type: precision_at_1 value: 32.369 - type: precision_at_10 value: 7.425 - type: precision_at_100 value: 1.134 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.102 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 27.755000000000003 - type: recall_at_10 value: 57.071000000000005 - type: recall_at_100 value: 79.456 - type: recall_at_1000 value: 93.54299999999999 - type: recall_at_3 value: 41.298 - type: recall_at_5 value: 48.037 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.855 - type: map_at_10 value: 34.53 - type: map_at_100 value: 36.167 - type: map_at_1000 value: 36.394999999999996 - type: map_at_3 value: 31.037 - type: map_at_5 value: 33.119 - type: mrr_at_1 value: 30.631999999999998 - type: mrr_at_10 value: 39.763999999999996 - type: mrr_at_100 value: 40.77 - type: mrr_at_1000 value: 40.826 - type: mrr_at_3 value: 36.495 - type: mrr_at_5 value: 38.561 - type: ndcg_at_1 value: 30.631999999999998 - type: ndcg_at_10 value: 40.942 - type: ndcg_at_100 value: 47.07 - type: ndcg_at_1000 value: 49.363 - type: ndcg_at_3 value: 35.038000000000004 - type: ndcg_at_5 value: 38.161 - type: precision_at_1 value: 30.631999999999998 - type: precision_at_10 value: 7.983999999999999 - type: precision_at_100 value: 1.6070000000000002 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 16.206 - type: precision_at_5 value: 12.253 - type: recall_at_1 value: 24.855 - type: recall_at_10 value: 53.291999999999994 - type: recall_at_100 value: 80.283 - type: recall_at_1000 value: 94.309 - type: recall_at_3 value: 37.257 - type: recall_at_5 value: 45.282 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.208 - type: map_at_10 value: 30.512 - type: map_at_100 value: 31.496000000000002 - type: map_at_1000 value: 31.595000000000002 - type: map_at_3 value: 27.904 - type: map_at_5 value: 29.491 - type: mrr_at_1 value: 22.736 - type: mrr_at_10 value: 32.379999999999995 - type: mrr_at_100 value: 33.245000000000005 - type: mrr_at_1000 value: 33.315 - type: mrr_at_3 value: 29.945 - type: mrr_at_5 value: 31.488 - type: ndcg_at_1 value: 22.736 - type: ndcg_at_10 value: 35.643 - type: ndcg_at_100 value: 40.535 - type: ndcg_at_1000 value: 43.042 - type: ndcg_at_3 value: 30.625000000000004 - type: ndcg_at_5 value: 33.323 - type: precision_at_1 value: 22.736 - type: precision_at_10 value: 5.6930000000000005 - type: precision_at_100 value: 0.889 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 13.431999999999999 - type: precision_at_5 value: 9.575 - type: recall_at_1 value: 21.208 - type: recall_at_10 value: 49.47 - type: recall_at_100 value: 71.71499999999999 - type: recall_at_1000 value: 90.55499999999999 - type: recall_at_3 value: 36.124 - type: recall_at_5 value: 42.606 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 11.363 - type: map_at_10 value: 20.312 - type: map_at_100 value: 22.225 - type: map_at_1000 value: 22.411 - type: map_at_3 value: 16.68 - type: map_at_5 value: 18.608 - type: mrr_at_1 value: 25.537 - type: mrr_at_10 value: 37.933 - type: mrr_at_100 value: 38.875 - type: mrr_at_1000 value: 38.911 - type: mrr_at_3 value: 34.387 - type: mrr_at_5 value: 36.51 - type: ndcg_at_1 value: 25.537 - type: ndcg_at_10 value: 28.82 - type: ndcg_at_100 value: 36.341 - type: ndcg_at_1000 value: 39.615 - type: ndcg_at_3 value: 23.01 - type: ndcg_at_5 value: 25.269000000000002 - type: precision_at_1 value: 25.537 - type: precision_at_10 value: 9.153 - type: precision_at_100 value: 1.7319999999999998 - type: precision_at_1000 value: 0.234 - type: precision_at_3 value: 17.22 - type: precision_at_5 value: 13.629 - type: recall_at_1 value: 11.363 - type: recall_at_10 value: 35.382999999999996 - type: recall_at_100 value: 61.367000000000004 - type: recall_at_1000 value: 79.699 - type: recall_at_3 value: 21.495 - type: recall_at_5 value: 27.42 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.65 - type: map_at_10 value: 20.742 - type: map_at_100 value: 29.614 - type: map_at_1000 value: 31.373 - type: map_at_3 value: 14.667 - type: map_at_5 value: 17.186 - type: mrr_at_1 value: 69.75 - type: mrr_at_10 value: 76.762 - type: mrr_at_100 value: 77.171 - type: mrr_at_1000 value: 77.179 - type: mrr_at_3 value: 75.125 - type: mrr_at_5 value: 76.287 - type: ndcg_at_1 value: 57.62500000000001 - type: ndcg_at_10 value: 42.370999999999995 - type: ndcg_at_100 value: 47.897 - type: ndcg_at_1000 value: 55.393 - type: ndcg_at_3 value: 46.317 - type: ndcg_at_5 value: 43.906 - type: precision_at_1 value: 69.75 - type: precision_at_10 value: 33.95 - type: precision_at_100 value: 10.885 - type: precision_at_1000 value: 2.2239999999999998 - type: precision_at_3 value: 49.75 - type: precision_at_5 value: 42.3 - type: recall_at_1 value: 9.65 - type: recall_at_10 value: 26.117 - type: recall_at_100 value: 55.084 - type: recall_at_1000 value: 78.62400000000001 - type: recall_at_3 value: 15.823 - type: recall_at_5 value: 19.652 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.885 - type: f1 value: 42.99567641346983 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 70.97 - type: map_at_10 value: 80.34599999999999 - type: map_at_100 value: 80.571 - type: map_at_1000 value: 80.584 - type: map_at_3 value: 79.279 - type: map_at_5 value: 79.94 - type: mrr_at_1 value: 76.613 - type: mrr_at_10 value: 85.15700000000001 - type: mrr_at_100 value: 85.249 - type: mrr_at_1000 value: 85.252 - type: mrr_at_3 value: 84.33800000000001 - type: mrr_at_5 value: 84.89 - type: ndcg_at_1 value: 76.613 - type: ndcg_at_10 value: 84.53399999999999 - type: ndcg_at_100 value: 85.359 - type: ndcg_at_1000 value: 85.607 - type: ndcg_at_3 value: 82.76599999999999 - type: ndcg_at_5 value: 83.736 - type: precision_at_1 value: 76.613 - type: precision_at_10 value: 10.206 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 31.913000000000004 - type: precision_at_5 value: 19.769000000000002 - type: recall_at_1 value: 70.97 - type: recall_at_10 value: 92.674 - type: recall_at_100 value: 95.985 - type: recall_at_1000 value: 97.57000000000001 - type: recall_at_3 value: 87.742 - type: recall_at_5 value: 90.28 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.494 - type: map_at_10 value: 36.491 - type: map_at_100 value: 38.550000000000004 - type: map_at_1000 value: 38.726 - type: map_at_3 value: 31.807000000000002 - type: map_at_5 value: 34.299 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.146 - type: mrr_at_100 value: 54.013999999999996 - type: mrr_at_1000 value: 54.044000000000004 - type: mrr_at_3 value: 50.952 - type: mrr_at_5 value: 52.124 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 44.499 - type: ndcg_at_100 value: 51.629000000000005 - type: ndcg_at_1000 value: 54.367 - type: ndcg_at_3 value: 40.900999999999996 - type: ndcg_at_5 value: 41.737 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.346 - type: precision_at_100 value: 1.974 - type: precision_at_1000 value: 0.246 - type: precision_at_3 value: 27.366 - type: precision_at_5 value: 19.846 - type: recall_at_1 value: 22.494 - type: recall_at_10 value: 51.156 - type: recall_at_100 value: 77.11200000000001 - type: recall_at_1000 value: 93.44 - type: recall_at_3 value: 36.574 - type: recall_at_5 value: 42.361 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 38.568999999999996 - type: map_at_10 value: 58.485 - type: map_at_100 value: 59.358999999999995 - type: map_at_1000 value: 59.429 - type: map_at_3 value: 55.217000000000006 - type: map_at_5 value: 57.236 - type: mrr_at_1 value: 77.137 - type: mrr_at_10 value: 82.829 - type: mrr_at_100 value: 83.04599999999999 - type: mrr_at_1000 value: 83.05399999999999 - type: mrr_at_3 value: 81.904 - type: mrr_at_5 value: 82.50800000000001 - type: ndcg_at_1 value: 77.137 - type: ndcg_at_10 value: 67.156 - type: ndcg_at_100 value: 70.298 - type: ndcg_at_1000 value: 71.65700000000001 - type: ndcg_at_3 value: 62.535 - type: ndcg_at_5 value: 65.095 - type: precision_at_1 value: 77.137 - type: precision_at_10 value: 13.911999999999999 - type: precision_at_100 value: 1.6389999999999998 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 39.572 - type: precision_at_5 value: 25.766 - type: recall_at_1 value: 38.568999999999996 - type: recall_at_10 value: 69.56099999999999 - type: recall_at_100 value: 81.931 - type: recall_at_1000 value: 90.91799999999999 - type: recall_at_3 value: 59.358999999999995 - type: recall_at_5 value: 64.416 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 88.45600000000002 - type: ap value: 84.09725115338568 - type: f1 value: 88.41874909080512 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.404999999999998 - type: map_at_10 value: 33.921 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.164 - type: map_at_3 value: 30.043999999999997 - type: map_at_5 value: 32.327 - type: mrr_at_1 value: 21.977 - type: mrr_at_10 value: 34.505 - type: mrr_at_100 value: 35.638999999999996 - type: mrr_at_1000 value: 35.68 - type: mrr_at_3 value: 30.703999999999997 - type: mrr_at_5 value: 32.96 - type: ndcg_at_1 value: 21.963 - type: ndcg_at_10 value: 40.859 - type: ndcg_at_100 value: 46.614 - type: ndcg_at_1000 value: 47.789 - type: ndcg_at_3 value: 33.007999999999996 - type: ndcg_at_5 value: 37.084 - type: precision_at_1 value: 21.963 - type: precision_at_10 value: 6.493 - type: precision_at_100 value: 0.938 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.155000000000001 - type: precision_at_5 value: 10.544 - type: recall_at_1 value: 21.404999999999998 - type: recall_at_10 value: 62.175000000000004 - type: recall_at_100 value: 88.786 - type: recall_at_1000 value: 97.738 - type: recall_at_3 value: 40.925 - type: recall_at_5 value: 50.722 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.50661194710442 - type: f1 value: 93.30311193153668 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 73.24669402644778 - type: f1 value: 54.23122108002977 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 72.61936785474109 - type: f1 value: 70.52644941025565 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.76529926025555 - type: f1 value: 77.26872729322514 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.39450293021839 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 31.757796879839294 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.62512146657428 - type: mrr value: 33.84624322066173 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.462 - type: map_at_10 value: 14.947 - type: map_at_100 value: 19.344 - type: map_at_1000 value: 20.933 - type: map_at_3 value: 10.761999999999999 - type: map_at_5 value: 12.744 - type: mrr_at_1 value: 47.988 - type: mrr_at_10 value: 57.365 - type: mrr_at_100 value: 57.931 - type: mrr_at_1000 value: 57.96 - type: mrr_at_3 value: 54.85 - type: mrr_at_5 value: 56.569 - type: ndcg_at_1 value: 46.129999999999995 - type: ndcg_at_10 value: 38.173 - type: ndcg_at_100 value: 35.983 - type: ndcg_at_1000 value: 44.507000000000005 - type: ndcg_at_3 value: 42.495 - type: ndcg_at_5 value: 41.019 - type: precision_at_1 value: 47.678 - type: precision_at_10 value: 28.731 - type: precision_at_100 value: 9.232 - type: precision_at_1000 value: 2.202 - type: precision_at_3 value: 39.628 - type: precision_at_5 value: 35.851 - type: recall_at_1 value: 6.462 - type: recall_at_10 value: 18.968 - type: recall_at_100 value: 37.131 - type: recall_at_1000 value: 67.956 - type: recall_at_3 value: 11.905000000000001 - type: recall_at_5 value: 15.097 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 30.335 - type: map_at_10 value: 46.611999999999995 - type: map_at_100 value: 47.632000000000005 - type: map_at_1000 value: 47.661 - type: map_at_3 value: 41.876999999999995 - type: map_at_5 value: 44.799 - type: mrr_at_1 value: 34.125 - type: mrr_at_10 value: 49.01 - type: mrr_at_100 value: 49.75 - type: mrr_at_1000 value: 49.768 - type: mrr_at_3 value: 45.153 - type: mrr_at_5 value: 47.589999999999996 - type: ndcg_at_1 value: 34.125 - type: ndcg_at_10 value: 54.777 - type: ndcg_at_100 value: 58.914 - type: ndcg_at_1000 value: 59.521 - type: ndcg_at_3 value: 46.015 - type: ndcg_at_5 value: 50.861000000000004 - type: precision_at_1 value: 34.125 - type: precision_at_10 value: 9.166 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 21.147 - type: precision_at_5 value: 15.469 - type: recall_at_1 value: 30.335 - type: recall_at_10 value: 77.194 - type: recall_at_100 value: 94.812 - type: recall_at_1000 value: 99.247 - type: recall_at_3 value: 54.681000000000004 - type: recall_at_5 value: 65.86800000000001 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.62 - type: map_at_10 value: 84.536 - type: map_at_100 value: 85.167 - type: map_at_1000 value: 85.184 - type: map_at_3 value: 81.607 - type: map_at_5 value: 83.423 - type: mrr_at_1 value: 81.36 - type: mrr_at_10 value: 87.506 - type: mrr_at_100 value: 87.601 - type: mrr_at_1000 value: 87.601 - type: mrr_at_3 value: 86.503 - type: mrr_at_5 value: 87.179 - type: ndcg_at_1 value: 81.36 - type: ndcg_at_10 value: 88.319 - type: ndcg_at_100 value: 89.517 - type: ndcg_at_1000 value: 89.60900000000001 - type: ndcg_at_3 value: 85.423 - type: ndcg_at_5 value: 86.976 - type: precision_at_1 value: 81.36 - type: precision_at_10 value: 13.415 - type: precision_at_100 value: 1.529 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.342999999999996 - type: precision_at_5 value: 24.534 - type: recall_at_1 value: 70.62 - type: recall_at_10 value: 95.57600000000001 - type: recall_at_100 value: 99.624 - type: recall_at_1000 value: 99.991 - type: recall_at_3 value: 87.22 - type: recall_at_5 value: 91.654 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 60.826438478212744 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.24027467551447 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.997999999999999 - type: map_at_10 value: 14.267 - type: map_at_100 value: 16.843 - type: map_at_1000 value: 17.229 - type: map_at_3 value: 9.834 - type: map_at_5 value: 11.92 - type: mrr_at_1 value: 24.7 - type: mrr_at_10 value: 37.685 - type: mrr_at_100 value: 38.704 - type: mrr_at_1000 value: 38.747 - type: mrr_at_3 value: 34.150000000000006 - type: mrr_at_5 value: 36.075 - type: ndcg_at_1 value: 24.7 - type: ndcg_at_10 value: 23.44 - type: ndcg_at_100 value: 32.617000000000004 - type: ndcg_at_1000 value: 38.628 - type: ndcg_at_3 value: 21.747 - type: ndcg_at_5 value: 19.076 - type: precision_at_1 value: 24.7 - type: precision_at_10 value: 12.47 - type: precision_at_100 value: 2.564 - type: precision_at_1000 value: 0.4 - type: precision_at_3 value: 20.767 - type: precision_at_5 value: 17.06 - type: recall_at_1 value: 4.997999999999999 - type: recall_at_10 value: 25.3 - type: recall_at_100 value: 52.048 - type: recall_at_1000 value: 81.093 - type: recall_at_3 value: 12.642999999999999 - type: recall_at_5 value: 17.312 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.44942006292234 - type: cos_sim_spearman value: 79.80930790660699 - type: euclidean_pearson value: 82.93400777494863 - type: euclidean_spearman value: 80.04664991110705 - type: manhattan_pearson value: 82.93551681854949 - type: manhattan_spearman value: 80.03156736837379 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.63574059135726 - type: cos_sim_spearman value: 76.80552915288186 - type: euclidean_pearson value: 82.46368529820518 - type: euclidean_spearman value: 76.60338474719275 - type: manhattan_pearson value: 82.4558617035968 - type: manhattan_spearman value: 76.57936082895705 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 86.24116811084211 - type: cos_sim_spearman value: 88.10998662068769 - type: euclidean_pearson value: 87.04961732352689 - type: euclidean_spearman value: 88.12543945864087 - type: manhattan_pearson value: 86.9905224528854 - type: manhattan_spearman value: 88.07827944705546 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.74847296555048 - type: cos_sim_spearman value: 82.66200957916445 - type: euclidean_pearson value: 84.48132256004965 - type: euclidean_spearman value: 82.67915286000596 - type: manhattan_pearson value: 84.44950477268334 - type: manhattan_spearman value: 82.63327639173352 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.23056258027053 - type: cos_sim_spearman value: 88.92791680286955 - type: euclidean_pearson value: 88.13819235461933 - type: euclidean_spearman value: 88.87294661361716 - type: manhattan_pearson value: 88.14212133687899 - type: manhattan_spearman value: 88.88551854529777 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.64179522732887 - type: cos_sim_spearman value: 84.25028809903114 - type: euclidean_pearson value: 83.40175015236979 - type: euclidean_spearman value: 84.23369296429406 - type: manhattan_pearson value: 83.43768174261321 - type: manhattan_spearman value: 84.27855229214734 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.20378955494732 - type: cos_sim_spearman value: 88.46863559173111 - type: euclidean_pearson value: 88.8249295811663 - type: euclidean_spearman value: 88.6312737724905 - type: manhattan_pearson value: 88.87744466378827 - type: manhattan_spearman value: 88.82908423767314 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 69.91342028796086 - type: cos_sim_spearman value: 69.71495021867864 - type: euclidean_pearson value: 70.65334330405646 - type: euclidean_spearman value: 69.4321253472211 - type: manhattan_pearson value: 70.59743494727465 - type: manhattan_spearman value: 69.11695509297482 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.42451709766952 - type: cos_sim_spearman value: 86.07166710670508 - type: euclidean_pearson value: 86.12711421258899 - type: euclidean_spearman value: 86.05232086925126 - type: manhattan_pearson value: 86.15591089932126 - type: manhattan_spearman value: 86.0890128623439 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.1976344717285 - type: mrr value: 96.3703145075694 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.511 - type: map_at_10 value: 69.724 - type: map_at_100 value: 70.208 - type: map_at_1000 value: 70.22800000000001 - type: map_at_3 value: 66.986 - type: map_at_5 value: 68.529 - type: mrr_at_1 value: 62.333000000000006 - type: mrr_at_10 value: 70.55 - type: mrr_at_100 value: 70.985 - type: mrr_at_1000 value: 71.004 - type: mrr_at_3 value: 68.611 - type: mrr_at_5 value: 69.728 - type: ndcg_at_1 value: 62.333000000000006 - type: ndcg_at_10 value: 74.265 - type: ndcg_at_100 value: 76.361 - type: ndcg_at_1000 value: 76.82900000000001 - type: ndcg_at_3 value: 69.772 - type: ndcg_at_5 value: 71.94800000000001 - type: precision_at_1 value: 62.333000000000006 - type: precision_at_10 value: 9.9 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 27.444000000000003 - type: precision_at_5 value: 18 - type: recall_at_1 value: 59.511 - type: recall_at_10 value: 87.156 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 75.2 - type: recall_at_5 value: 80.661 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81683168316832 - type: cos_sim_ap value: 95.74716566563774 - type: cos_sim_f1 value: 90.64238745574103 - type: cos_sim_precision value: 91.7093142272262 - type: cos_sim_recall value: 89.60000000000001 - type: dot_accuracy value: 99.69405940594059 - type: dot_ap value: 91.09013507754594 - type: dot_f1 value: 84.54227113556779 - type: dot_precision value: 84.58458458458459 - type: dot_recall value: 84.5 - type: euclidean_accuracy value: 99.81782178217821 - type: euclidean_ap value: 95.6324301072609 - type: euclidean_f1 value: 90.58341862845445 - type: euclidean_precision value: 92.76729559748428 - type: euclidean_recall value: 88.5 - type: manhattan_accuracy value: 99.81980198019802 - type: manhattan_ap value: 95.68510494437183 - type: manhattan_f1 value: 90.58945191313342 - type: manhattan_precision value: 93.79014989293361 - type: manhattan_recall value: 87.6 - type: max_accuracy value: 99.81980198019802 - type: max_ap value: 95.74716566563774 - type: max_f1 value: 90.64238745574103 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 67.63761899427078 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.572473369697235 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.63000245208579 - type: mrr value: 54.504193722943725 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.300791939416545 - type: cos_sim_spearman value: 31.662904057924123 - type: dot_pearson value: 26.21198530758316 - type: dot_spearman value: 27.006921548904263 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.197 - type: map_at_10 value: 1.752 - type: map_at_100 value: 10.795 - type: map_at_1000 value: 27.18 - type: map_at_3 value: 0.5890000000000001 - type: map_at_5 value: 0.938 - type: mrr_at_1 value: 74 - type: mrr_at_10 value: 85.833 - type: mrr_at_100 value: 85.833 - type: mrr_at_1000 value: 85.833 - type: mrr_at_3 value: 85.333 - type: mrr_at_5 value: 85.833 - type: ndcg_at_1 value: 69 - type: ndcg_at_10 value: 70.22 - type: ndcg_at_100 value: 55.785 - type: ndcg_at_1000 value: 52.93600000000001 - type: ndcg_at_3 value: 72.084 - type: ndcg_at_5 value: 71.184 - type: precision_at_1 value: 74 - type: precision_at_10 value: 75.2 - type: precision_at_100 value: 57.3 - type: precision_at_1000 value: 23.302 - type: precision_at_3 value: 77.333 - type: precision_at_5 value: 75.6 - type: recall_at_1 value: 0.197 - type: recall_at_10 value: 2.019 - type: recall_at_100 value: 14.257 - type: recall_at_1000 value: 50.922 - type: recall_at_3 value: 0.642 - type: recall_at_5 value: 1.043 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.803 - type: map_at_10 value: 10.407 - type: map_at_100 value: 16.948 - type: map_at_1000 value: 18.424 - type: map_at_3 value: 5.405 - type: map_at_5 value: 6.908 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 50.221000000000004 - type: mrr_at_100 value: 51.388 - type: mrr_at_1000 value: 51.402 - type: mrr_at_3 value: 47.278999999999996 - type: mrr_at_5 value: 49.626 - type: ndcg_at_1 value: 34.694 - type: ndcg_at_10 value: 25.507 - type: ndcg_at_100 value: 38.296 - type: ndcg_at_1000 value: 49.492000000000004 - type: ndcg_at_3 value: 29.006999999999998 - type: ndcg_at_5 value: 25.979000000000003 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 22.041 - type: precision_at_100 value: 8.02 - type: precision_at_1000 value: 1.567 - type: precision_at_3 value: 28.571 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.803 - type: recall_at_10 value: 16.378 - type: recall_at_100 value: 50.489 - type: recall_at_1000 value: 85.013 - type: recall_at_3 value: 6.505 - type: recall_at_5 value: 9.243 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.55579999999999 - type: ap value: 14.206982753316227 - type: f1 value: 54.372142814964285 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 56.57611771363893 - type: f1 value: 56.924172639063144 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 52.82304915719759 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.92716218632653 - type: cos_sim_ap value: 73.73359122546046 - type: cos_sim_f1 value: 68.42559487116262 - type: cos_sim_precision value: 64.22124508215691 - type: cos_sim_recall value: 73.21899736147758 - type: dot_accuracy value: 80.38981939560112 - type: dot_ap value: 54.61060862444974 - type: dot_f1 value: 53.45710627400769 - type: dot_precision value: 44.87638839125761 - type: dot_recall value: 66.09498680738787 - type: euclidean_accuracy value: 86.02849138701794 - type: euclidean_ap value: 73.95673761922404 - type: euclidean_f1 value: 68.6783042394015 - type: euclidean_precision value: 65.1063829787234 - type: euclidean_recall value: 72.66490765171504 - type: manhattan_accuracy value: 85.9808070572808 - type: manhattan_ap value: 73.9050720058029 - type: manhattan_f1 value: 68.57560618983794 - type: manhattan_precision value: 63.70839936608558 - type: manhattan_recall value: 74.24802110817942 - type: max_accuracy value: 86.02849138701794 - type: max_ap value: 73.95673761922404 - type: max_f1 value: 68.6783042394015 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.72783017037295 - type: cos_sim_ap value: 85.52705223340233 - type: cos_sim_f1 value: 77.91659078492079 - type: cos_sim_precision value: 73.93378032764221 - type: cos_sim_recall value: 82.35294117647058 - type: dot_accuracy value: 85.41739434159972 - type: dot_ap value: 77.17734818118443 - type: dot_f1 value: 71.63473589973144 - type: dot_precision value: 66.96123719622415 - type: dot_recall value: 77.00954727440714 - type: euclidean_accuracy value: 88.68125897465751 - type: euclidean_ap value: 85.47712213906692 - type: euclidean_f1 value: 77.81419950830664 - type: euclidean_precision value: 75.37162649733006 - type: euclidean_recall value: 80.42038805050817 - type: manhattan_accuracy value: 88.67349710870494 - type: manhattan_ap value: 85.46506475241955 - type: manhattan_f1 value: 77.87259084890393 - type: manhattan_precision value: 74.54929577464789 - type: manhattan_recall value: 81.50600554357868 - type: max_accuracy value: 88.72783017037295 - type: max_ap value: 85.52705223340233 - type: max_f1 value: 77.91659078492079 language: - en license: mit --- # gte-large General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 | | [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 | | [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 | | [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 | | [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 | | [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 | | [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large") model = AutoModel.from_pretrained("thenlper/gte-large") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-large') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```

sanekalas/t5-hw3-shumovav

sanekalas

2025-05-27T19:23:51Z

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2025-05-27T19:23:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Moklemok/CodeSpace

Moklemok

2025-05-27T19:21:03Z

0

null

[ "license:bigcode-openrail-m", "region:us" ]

null

2025-05-27T19:21:03Z

--- license: bigcode-openrail-m ---

Lubna-qureshi-Hd/lubna.qureshi.viral.video.HOT.NEws.Today.Trending.Latest.Video

Lubna-qureshi-Hd

2025-05-27T19:20:58Z

0

null

[ "region:us" ]

null

2025-05-27T19:17:26Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Lubna-qureshi) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Lubna-qureshi) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Lubna-qureshi)

jobz-hunting/wATCH.Jobz.Hunting.Sajal.Malik.viral.video.original

jobz-hunting

2025-05-27T19:20:42Z

0

null

[ "region:us" ]

null

2025-05-27T19:20:17Z

[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?jobz-hunting) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?jobz-hunting) [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?jobz-hunting)

ErikCikalleshi/alpaca_lora_model

ErikCikalleshi

2025-05-27T19:19:51Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-05-22T19:35:59Z

--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** ErikCikalleshi - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs7

aamijar

2025-05-27T19:19:19Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T19:19:18Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

milpu02/Akkgsykmix

milpu02

2025-05-27T19:18:07Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2025-05-27T19:17:54Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Akkgsyk output: url: images/pixai-1845374734374697074-2.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: Akkgsyk --- # sdxl <Gallery /> ## Trigger words You should use `Akkgsyk` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/milpu02/Akkgsykmix/tree/main) them in the Files & versions tab.

gradientrouting-spar/medical_task_qwen_3_8b_ft_trainers_seed_42_epoch_1

gradientrouting-spar

2025-05-27T19:17:47Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T19:15:28Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Videos-CCTV-wiring-cikgu-viral-clip/Original.Bocor.Video.CCTV.wiring.cikgu.video.nur.fadhilah.binti.zainal.guru.part.2.video

Videos-CCTV-wiring-cikgu-viral-clip

2025-05-27T19:17:22Z

0

null

[ "region:us" ]

null

2025-05-27T19:16:57Z

<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>

flaviawallen/MNLP_M2_document_encoder

flaviawallen

2025-05-27T19:15:56Z

0

sentence-transformers

[ "sentence-transformers", "pytorch", "onnx", "safetensors", "bert", "feature-extraction", "mteb", "sentence-similarity", "en", "arxiv:2402.16829", "arxiv:2212.09741", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-05-27T19:14:26Z

--- language: - en library_name: sentence-transformers license: mit pipeline_tag: sentence-similarity tags: - feature-extraction - mteb - sentence-similarity - sentence-transformers model-index: - name: GIST-small-Embedding-v0 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.26865671641791 - type: ap value: 38.25623793370476 - type: f1 value: 69.26434651320257 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.232225 - type: ap value: 89.97936072879344 - type: f1 value: 93.22122653806187 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.715999999999994 - type: f1 value: 49.169789920136076 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 34.922 - type: map_at_10 value: 50.524 - type: map_at_100 value: 51.247 - type: map_at_1000 value: 51.249 - type: map_at_3 value: 45.887 - type: map_at_5 value: 48.592999999999996 - type: mrr_at_1 value: 34.922 - type: mrr_at_10 value: 50.382000000000005 - type: mrr_at_100 value: 51.104000000000006 - type: mrr_at_1000 value: 51.105999999999995 - type: mrr_at_3 value: 45.733000000000004 - type: mrr_at_5 value: 48.428 - type: ndcg_at_1 value: 34.922 - type: ndcg_at_10 value: 59.12 - type: ndcg_at_100 value: 62.083999999999996 - type: ndcg_at_1000 value: 62.137 - type: ndcg_at_3 value: 49.616 - type: ndcg_at_5 value: 54.501 - type: precision_at_1 value: 34.922 - type: precision_at_10 value: 8.649 - type: precision_at_100 value: 0.991 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.152 - type: precision_at_5 value: 14.466999999999999 - type: recall_at_1 value: 34.922 - type: recall_at_10 value: 86.48599999999999 - type: recall_at_100 value: 99.14699999999999 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 60.455000000000005 - type: recall_at_5 value: 72.333 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.623282347623714 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 39.86487843524932 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.3290291318171 - type: mrr value: 75.2379853141626 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.52002953574285 - type: cos_sim_spearman value: 86.98752423842483 - type: euclidean_pearson value: 86.89442688314197 - type: euclidean_spearman value: 86.88631711307471 - type: manhattan_pearson value: 87.03723618507175 - type: manhattan_spearman value: 86.76041062975224 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.64935064935065 - type: f1 value: 86.61903824934998 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.21904455377494 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.43342755570654 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 31.843 - type: map_at_10 value: 43.379 - type: map_at_100 value: 44.946999999999996 - type: map_at_1000 value: 45.078 - type: map_at_3 value: 39.598 - type: map_at_5 value: 41.746 - type: mrr_at_1 value: 39.199 - type: mrr_at_10 value: 49.672 - type: mrr_at_100 value: 50.321000000000005 - type: mrr_at_1000 value: 50.365 - type: mrr_at_3 value: 46.805 - type: mrr_at_5 value: 48.579 - type: ndcg_at_1 value: 39.199 - type: ndcg_at_10 value: 50.163999999999994 - type: ndcg_at_100 value: 55.418 - type: ndcg_at_1000 value: 57.353 - type: ndcg_at_3 value: 44.716 - type: ndcg_at_5 value: 47.268 - type: precision_at_1 value: 39.199 - type: precision_at_10 value: 9.757 - type: precision_at_100 value: 1.552 - type: precision_at_1000 value: 0.20500000000000002 - type: precision_at_3 value: 21.602 - type: precision_at_5 value: 15.479000000000001 - type: recall_at_1 value: 31.843 - type: recall_at_10 value: 62.743 - type: recall_at_100 value: 84.78099999999999 - type: recall_at_1000 value: 96.86099999999999 - type: recall_at_3 value: 46.927 - type: recall_at_5 value: 54.355 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.321 - type: map_at_10 value: 39.062999999999995 - type: map_at_100 value: 40.403 - type: map_at_1000 value: 40.534 - type: map_at_3 value: 36.367 - type: map_at_5 value: 37.756 - type: mrr_at_1 value: 35.987 - type: mrr_at_10 value: 44.708999999999996 - type: mrr_at_100 value: 45.394 - type: mrr_at_1000 value: 45.436 - type: mrr_at_3 value: 42.463 - type: mrr_at_5 value: 43.663000000000004 - type: ndcg_at_1 value: 35.987 - type: ndcg_at_10 value: 44.585 - type: ndcg_at_100 value: 49.297999999999995 - type: ndcg_at_1000 value: 51.315 - type: ndcg_at_3 value: 40.569 - type: ndcg_at_5 value: 42.197 - type: precision_at_1 value: 35.987 - type: precision_at_10 value: 8.369 - type: precision_at_100 value: 1.366 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 19.427 - type: precision_at_5 value: 13.58 - type: recall_at_1 value: 29.321 - type: recall_at_10 value: 54.333 - type: recall_at_100 value: 74.178 - type: recall_at_1000 value: 86.732 - type: recall_at_3 value: 42.46 - type: recall_at_5 value: 47.089999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 38.811 - type: map_at_10 value: 51.114000000000004 - type: map_at_100 value: 52.22 - type: map_at_1000 value: 52.275000000000006 - type: map_at_3 value: 47.644999999999996 - type: map_at_5 value: 49.675000000000004 - type: mrr_at_1 value: 44.389 - type: mrr_at_10 value: 54.459 - type: mrr_at_100 value: 55.208999999999996 - type: mrr_at_1000 value: 55.239000000000004 - type: mrr_at_3 value: 51.954 - type: mrr_at_5 value: 53.571999999999996 - type: ndcg_at_1 value: 44.389 - type: ndcg_at_10 value: 56.979 - type: ndcg_at_100 value: 61.266 - type: ndcg_at_1000 value: 62.315 - type: ndcg_at_3 value: 51.342 - type: ndcg_at_5 value: 54.33 - type: precision_at_1 value: 44.389 - type: precision_at_10 value: 9.26 - type: precision_at_100 value: 1.226 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 22.926 - type: precision_at_5 value: 15.987000000000002 - type: recall_at_1 value: 38.811 - type: recall_at_10 value: 70.841 - type: recall_at_100 value: 89.218 - type: recall_at_1000 value: 96.482 - type: recall_at_3 value: 56.123999999999995 - type: recall_at_5 value: 63.322 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.378 - type: map_at_10 value: 34.311 - type: map_at_100 value: 35.399 - type: map_at_1000 value: 35.482 - type: map_at_3 value: 31.917 - type: map_at_5 value: 33.275 - type: mrr_at_1 value: 27.683999999999997 - type: mrr_at_10 value: 36.575 - type: mrr_at_100 value: 37.492 - type: mrr_at_1000 value: 37.556 - type: mrr_at_3 value: 34.35 - type: mrr_at_5 value: 35.525 - type: ndcg_at_1 value: 27.683999999999997 - type: ndcg_at_10 value: 39.247 - type: ndcg_at_100 value: 44.424 - type: ndcg_at_1000 value: 46.478 - type: ndcg_at_3 value: 34.684 - type: ndcg_at_5 value: 36.886 - type: precision_at_1 value: 27.683999999999997 - type: precision_at_10 value: 5.989 - type: precision_at_100 value: 0.899 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.84 - type: precision_at_5 value: 10.215 - type: recall_at_1 value: 25.378 - type: recall_at_10 value: 52.195 - type: recall_at_100 value: 75.764 - type: recall_at_1000 value: 91.012 - type: recall_at_3 value: 39.885999999999996 - type: recall_at_5 value: 45.279 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.326 - type: map_at_10 value: 25.247000000000003 - type: map_at_100 value: 26.473000000000003 - type: map_at_1000 value: 26.579000000000004 - type: map_at_3 value: 22.466 - type: map_at_5 value: 24.113 - type: mrr_at_1 value: 21.393 - type: mrr_at_10 value: 30.187 - type: mrr_at_100 value: 31.089 - type: mrr_at_1000 value: 31.15 - type: mrr_at_3 value: 27.279999999999998 - type: mrr_at_5 value: 29.127 - type: ndcg_at_1 value: 21.393 - type: ndcg_at_10 value: 30.668 - type: ndcg_at_100 value: 36.543 - type: ndcg_at_1000 value: 39.181 - type: ndcg_at_3 value: 25.552000000000003 - type: ndcg_at_5 value: 28.176000000000002 - type: precision_at_1 value: 21.393 - type: precision_at_10 value: 5.784000000000001 - type: precision_at_100 value: 1.001 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 12.231 - type: precision_at_5 value: 9.179 - type: recall_at_1 value: 17.326 - type: recall_at_10 value: 42.415000000000006 - type: recall_at_100 value: 68.605 - type: recall_at_1000 value: 87.694 - type: recall_at_3 value: 28.343 - type: recall_at_5 value: 35.086 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 29.069 - type: map_at_10 value: 40.027 - type: map_at_100 value: 41.308 - type: map_at_1000 value: 41.412 - type: map_at_3 value: 36.864000000000004 - type: map_at_5 value: 38.641999999999996 - type: mrr_at_1 value: 35.707 - type: mrr_at_10 value: 45.527 - type: mrr_at_100 value: 46.348 - type: mrr_at_1000 value: 46.392 - type: mrr_at_3 value: 43.086 - type: mrr_at_5 value: 44.645 - type: ndcg_at_1 value: 35.707 - type: ndcg_at_10 value: 46.117000000000004 - type: ndcg_at_100 value: 51.468 - type: ndcg_at_1000 value: 53.412000000000006 - type: ndcg_at_3 value: 41.224 - type: ndcg_at_5 value: 43.637 - type: precision_at_1 value: 35.707 - type: precision_at_10 value: 8.459999999999999 - type: precision_at_100 value: 1.2970000000000002 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 19.731 - type: precision_at_5 value: 14.013 - type: recall_at_1 value: 29.069 - type: recall_at_10 value: 58.343999999999994 - type: recall_at_100 value: 81.296 - type: recall_at_1000 value: 93.974 - type: recall_at_3 value: 44.7 - type: recall_at_5 value: 50.88700000000001 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.905 - type: map_at_10 value: 33.983000000000004 - type: map_at_100 value: 35.372 - type: map_at_1000 value: 35.487 - type: map_at_3 value: 30.902 - type: map_at_5 value: 32.505 - type: mrr_at_1 value: 29.794999999999998 - type: mrr_at_10 value: 39.28 - type: mrr_at_100 value: 40.215 - type: mrr_at_1000 value: 40.276 - type: mrr_at_3 value: 36.701 - type: mrr_at_5 value: 38.105 - type: ndcg_at_1 value: 29.794999999999998 - type: ndcg_at_10 value: 40.041 - type: ndcg_at_100 value: 45.884 - type: ndcg_at_1000 value: 48.271 - type: ndcg_at_3 value: 34.931 - type: ndcg_at_5 value: 37.044 - type: precision_at_1 value: 29.794999999999998 - type: precision_at_10 value: 7.546 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 16.933 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 23.905 - type: recall_at_10 value: 52.945 - type: recall_at_100 value: 77.551 - type: recall_at_1000 value: 93.793 - type: recall_at_3 value: 38.364 - type: recall_at_5 value: 44.044 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.24441666666667 - type: map_at_10 value: 34.4595 - type: map_at_100 value: 35.699999999999996 - type: map_at_1000 value: 35.8155 - type: map_at_3 value: 31.608333333333338 - type: map_at_5 value: 33.189416666666666 - type: mrr_at_1 value: 29.825250000000004 - type: mrr_at_10 value: 38.60875 - type: mrr_at_100 value: 39.46575 - type: mrr_at_1000 value: 39.52458333333333 - type: mrr_at_3 value: 36.145166666666675 - type: mrr_at_5 value: 37.57625 - type: ndcg_at_1 value: 29.825250000000004 - type: ndcg_at_10 value: 39.88741666666667 - type: ndcg_at_100 value: 45.17966666666667 - type: ndcg_at_1000 value: 47.440583333333336 - type: ndcg_at_3 value: 35.04591666666666 - type: ndcg_at_5 value: 37.32025 - type: precision_at_1 value: 29.825250000000004 - type: precision_at_10 value: 7.07225 - type: precision_at_100 value: 1.1462499999999998 - type: precision_at_1000 value: 0.15325 - type: precision_at_3 value: 16.18375 - type: precision_at_5 value: 11.526833333333334 - type: recall_at_1 value: 25.24441666666667 - type: recall_at_10 value: 51.744916666666676 - type: recall_at_100 value: 75.04574999999998 - type: recall_at_1000 value: 90.65558333333334 - type: recall_at_3 value: 38.28349999999999 - type: recall_at_5 value: 44.16591666666667 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.237000000000002 - type: map_at_10 value: 30.667 - type: map_at_100 value: 31.592 - type: map_at_1000 value: 31.688 - type: map_at_3 value: 28.810999999999996 - type: map_at_5 value: 29.788999999999998 - type: mrr_at_1 value: 26.840000000000003 - type: mrr_at_10 value: 33.305 - type: mrr_at_100 value: 34.089000000000006 - type: mrr_at_1000 value: 34.159 - type: mrr_at_3 value: 31.518 - type: mrr_at_5 value: 32.469 - type: ndcg_at_1 value: 26.840000000000003 - type: ndcg_at_10 value: 34.541 - type: ndcg_at_100 value: 39.206 - type: ndcg_at_1000 value: 41.592 - type: ndcg_at_3 value: 31.005 - type: ndcg_at_5 value: 32.554 - type: precision_at_1 value: 26.840000000000003 - type: precision_at_10 value: 5.3069999999999995 - type: precision_at_100 value: 0.8340000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 13.292000000000002 - type: precision_at_5 value: 9.049 - type: recall_at_1 value: 24.237000000000002 - type: recall_at_10 value: 43.862 - type: recall_at_100 value: 65.352 - type: recall_at_1000 value: 82.704 - type: recall_at_3 value: 34.009 - type: recall_at_5 value: 37.878 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.482 - type: map_at_10 value: 23.249 - type: map_at_100 value: 24.388 - type: map_at_1000 value: 24.519 - type: map_at_3 value: 20.971 - type: map_at_5 value: 22.192 - type: mrr_at_1 value: 19.993 - type: mrr_at_10 value: 26.985 - type: mrr_at_100 value: 27.975 - type: mrr_at_1000 value: 28.052 - type: mrr_at_3 value: 24.954 - type: mrr_at_5 value: 26.070999999999998 - type: ndcg_at_1 value: 19.993 - type: ndcg_at_10 value: 27.656 - type: ndcg_at_100 value: 33.256 - type: ndcg_at_1000 value: 36.275 - type: ndcg_at_3 value: 23.644000000000002 - type: ndcg_at_5 value: 25.466 - type: precision_at_1 value: 19.993 - type: precision_at_10 value: 5.093 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.149000000000001 - type: precision_at_5 value: 8.149000000000001 - type: recall_at_1 value: 16.482 - type: recall_at_10 value: 37.141999999999996 - type: recall_at_100 value: 62.696 - type: recall_at_1000 value: 84.333 - type: recall_at_3 value: 26.031 - type: recall_at_5 value: 30.660999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.887999999999998 - type: map_at_10 value: 34.101 - type: map_at_100 value: 35.27 - type: map_at_1000 value: 35.370000000000005 - type: map_at_3 value: 31.283 - type: map_at_5 value: 32.72 - type: mrr_at_1 value: 29.011 - type: mrr_at_10 value: 38.004 - type: mrr_at_100 value: 38.879000000000005 - type: mrr_at_1000 value: 38.938 - type: mrr_at_3 value: 35.571999999999996 - type: mrr_at_5 value: 36.789 - type: ndcg_at_1 value: 29.011 - type: ndcg_at_10 value: 39.586 - type: ndcg_at_100 value: 44.939 - type: ndcg_at_1000 value: 47.236 - type: ndcg_at_3 value: 34.4 - type: ndcg_at_5 value: 36.519 - type: precision_at_1 value: 29.011 - type: precision_at_10 value: 6.763 - type: precision_at_100 value: 1.059 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 15.609 - type: precision_at_5 value: 10.896 - type: recall_at_1 value: 24.887999999999998 - type: recall_at_10 value: 52.42 - type: recall_at_100 value: 75.803 - type: recall_at_1000 value: 91.725 - type: recall_at_3 value: 38.080999999999996 - type: recall_at_5 value: 43.47 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 23.953 - type: map_at_10 value: 32.649 - type: map_at_100 value: 34.181 - type: map_at_1000 value: 34.398 - type: map_at_3 value: 29.567 - type: map_at_5 value: 31.263 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 37.419999999999995 - type: mrr_at_100 value: 38.396 - type: mrr_at_1000 value: 38.458 - type: mrr_at_3 value: 34.782999999999994 - type: mrr_at_5 value: 36.254999999999995 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 38.595 - type: ndcg_at_100 value: 44.6 - type: ndcg_at_1000 value: 47.158 - type: ndcg_at_3 value: 33.56 - type: ndcg_at_5 value: 35.870000000000005 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.53 - type: precision_at_100 value: 1.538 - type: precision_at_1000 value: 0.24 - type: precision_at_3 value: 15.744 - type: precision_at_5 value: 11.542 - type: recall_at_1 value: 23.953 - type: recall_at_10 value: 50.08200000000001 - type: recall_at_100 value: 77.364 - type: recall_at_1000 value: 93.57799999999999 - type: recall_at_3 value: 35.432 - type: recall_at_5 value: 41.875 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.72 - type: map_at_10 value: 25.724000000000004 - type: map_at_100 value: 26.846999999999998 - type: map_at_1000 value: 26.964 - type: map_at_3 value: 22.909 - type: map_at_5 value: 24.596999999999998 - type: mrr_at_1 value: 18.854000000000003 - type: mrr_at_10 value: 27.182000000000002 - type: mrr_at_100 value: 28.182000000000002 - type: mrr_at_1000 value: 28.274 - type: mrr_at_3 value: 24.276 - type: mrr_at_5 value: 26.115 - type: ndcg_at_1 value: 18.854000000000003 - type: ndcg_at_10 value: 30.470000000000002 - type: ndcg_at_100 value: 35.854 - type: ndcg_at_1000 value: 38.701 - type: ndcg_at_3 value: 24.924 - type: ndcg_at_5 value: 27.895999999999997 - type: precision_at_1 value: 18.854000000000003 - type: precision_at_10 value: 5.009 - type: precision_at_100 value: 0.835 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 10.721 - type: precision_at_5 value: 8.133 - type: recall_at_1 value: 17.72 - type: recall_at_10 value: 43.617 - type: recall_at_100 value: 67.941 - type: recall_at_1000 value: 88.979 - type: recall_at_3 value: 29.044999999999998 - type: recall_at_5 value: 36.044 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 13.427 - type: map_at_10 value: 22.935 - type: map_at_100 value: 24.808 - type: map_at_1000 value: 24.994 - type: map_at_3 value: 19.533 - type: map_at_5 value: 21.261 - type: mrr_at_1 value: 30.945 - type: mrr_at_10 value: 43.242000000000004 - type: mrr_at_100 value: 44.013999999999996 - type: mrr_at_1000 value: 44.048 - type: mrr_at_3 value: 40.109 - type: mrr_at_5 value: 42.059999999999995 - type: ndcg_at_1 value: 30.945 - type: ndcg_at_10 value: 31.828 - type: ndcg_at_100 value: 38.801 - type: ndcg_at_1000 value: 42.126999999999995 - type: ndcg_at_3 value: 26.922 - type: ndcg_at_5 value: 28.483999999999998 - type: precision_at_1 value: 30.945 - type: precision_at_10 value: 9.844 - type: precision_at_100 value: 1.7309999999999999 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 20.477999999999998 - type: precision_at_5 value: 15.27 - type: recall_at_1 value: 13.427 - type: recall_at_10 value: 37.141000000000005 - type: recall_at_100 value: 61.007 - type: recall_at_1000 value: 79.742 - type: recall_at_3 value: 24.431 - type: recall_at_5 value: 29.725 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.122 - type: map_at_10 value: 18.799 - type: map_at_100 value: 25.724999999999998 - type: map_at_1000 value: 27.205000000000002 - type: map_at_3 value: 14.194999999999999 - type: map_at_5 value: 16.225 - type: mrr_at_1 value: 68.0 - type: mrr_at_10 value: 76.035 - type: mrr_at_100 value: 76.292 - type: mrr_at_1000 value: 76.297 - type: mrr_at_3 value: 74.458 - type: mrr_at_5 value: 75.558 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 39.761 - type: ndcg_at_100 value: 43.736999999999995 - type: ndcg_at_1000 value: 51.146 - type: ndcg_at_3 value: 45.921 - type: ndcg_at_5 value: 42.756 - type: precision_at_1 value: 68.0 - type: precision_at_10 value: 30.275000000000002 - type: precision_at_100 value: 9.343 - type: precision_at_1000 value: 1.8270000000000002 - type: precision_at_3 value: 49.167 - type: precision_at_5 value: 40.699999999999996 - type: recall_at_1 value: 9.122 - type: recall_at_10 value: 23.669999999999998 - type: recall_at_100 value: 48.719 - type: recall_at_1000 value: 72.033 - type: recall_at_3 value: 15.498999999999999 - type: recall_at_5 value: 18.657 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 55.885000000000005 - type: f1 value: 50.70726446938571 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 75.709 - type: map_at_10 value: 83.345 - type: map_at_100 value: 83.557 - type: map_at_1000 value: 83.572 - type: map_at_3 value: 82.425 - type: map_at_5 value: 83.013 - type: mrr_at_1 value: 81.593 - type: mrr_at_10 value: 88.331 - type: mrr_at_100 value: 88.408 - type: mrr_at_1000 value: 88.41 - type: mrr_at_3 value: 87.714 - type: mrr_at_5 value: 88.122 - type: ndcg_at_1 value: 81.593 - type: ndcg_at_10 value: 86.925 - type: ndcg_at_100 value: 87.67 - type: ndcg_at_1000 value: 87.924 - type: ndcg_at_3 value: 85.5 - type: ndcg_at_5 value: 86.283 - type: precision_at_1 value: 81.593 - type: precision_at_10 value: 10.264 - type: precision_at_100 value: 1.084 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 32.388 - type: precision_at_5 value: 19.991 - type: recall_at_1 value: 75.709 - type: recall_at_10 value: 93.107 - type: recall_at_100 value: 96.024 - type: recall_at_1000 value: 97.603 - type: recall_at_3 value: 89.08500000000001 - type: recall_at_5 value: 91.15299999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 19.121 - type: map_at_10 value: 31.78 - type: map_at_100 value: 33.497 - type: map_at_1000 value: 33.696 - type: map_at_3 value: 27.893 - type: map_at_5 value: 30.087000000000003 - type: mrr_at_1 value: 38.272 - type: mrr_at_10 value: 47.176 - type: mrr_at_100 value: 48.002 - type: mrr_at_1000 value: 48.044 - type: mrr_at_3 value: 45.086999999999996 - type: mrr_at_5 value: 46.337 - type: ndcg_at_1 value: 38.272 - type: ndcg_at_10 value: 39.145 - type: ndcg_at_100 value: 45.696999999999996 - type: ndcg_at_1000 value: 49.0 - type: ndcg_at_3 value: 36.148 - type: ndcg_at_5 value: 37.023 - type: precision_at_1 value: 38.272 - type: precision_at_10 value: 11.065 - type: precision_at_100 value: 1.7840000000000003 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 24.587999999999997 - type: precision_at_5 value: 18.056 - type: recall_at_1 value: 19.121 - type: recall_at_10 value: 44.857 - type: recall_at_100 value: 69.774 - type: recall_at_1000 value: 89.645 - type: recall_at_3 value: 32.588 - type: recall_at_5 value: 37.939 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 36.428 - type: map_at_10 value: 56.891999999999996 - type: map_at_100 value: 57.82899999999999 - type: map_at_1000 value: 57.896 - type: map_at_3 value: 53.762 - type: map_at_5 value: 55.718 - type: mrr_at_1 value: 72.856 - type: mrr_at_10 value: 79.245 - type: mrr_at_100 value: 79.515 - type: mrr_at_1000 value: 79.525 - type: mrr_at_3 value: 78.143 - type: mrr_at_5 value: 78.822 - type: ndcg_at_1 value: 72.856 - type: ndcg_at_10 value: 65.204 - type: ndcg_at_100 value: 68.552 - type: ndcg_at_1000 value: 69.902 - type: ndcg_at_3 value: 60.632 - type: ndcg_at_5 value: 63.161 - type: precision_at_1 value: 72.856 - type: precision_at_10 value: 13.65 - type: precision_at_100 value: 1.6260000000000001 - type: precision_at_1000 value: 0.181 - type: precision_at_3 value: 38.753 - type: precision_at_5 value: 25.251 - type: recall_at_1 value: 36.428 - type: recall_at_10 value: 68.25099999999999 - type: recall_at_100 value: 81.317 - type: recall_at_1000 value: 90.27 - type: recall_at_3 value: 58.13 - type: recall_at_5 value: 63.126000000000005 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 89.4868 - type: ap value: 84.88319192880247 - type: f1 value: 89.46144458052846 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 21.282999999999998 - type: map_at_10 value: 33.045 - type: map_at_100 value: 34.238 - type: map_at_1000 value: 34.29 - type: map_at_3 value: 29.305999999999997 - type: map_at_5 value: 31.391000000000002 - type: mrr_at_1 value: 21.92 - type: mrr_at_10 value: 33.649 - type: mrr_at_100 value: 34.791 - type: mrr_at_1000 value: 34.837 - type: mrr_at_3 value: 30.0 - type: mrr_at_5 value: 32.039 - type: ndcg_at_1 value: 21.92 - type: ndcg_at_10 value: 39.729 - type: ndcg_at_100 value: 45.484 - type: ndcg_at_1000 value: 46.817 - type: ndcg_at_3 value: 32.084 - type: ndcg_at_5 value: 35.789 - type: precision_at_1 value: 21.92 - type: precision_at_10 value: 6.297 - type: precision_at_100 value: 0.918 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 13.639000000000001 - type: precision_at_5 value: 10.054 - type: recall_at_1 value: 21.282999999999998 - type: recall_at_10 value: 60.343999999999994 - type: recall_at_100 value: 86.981 - type: recall_at_1000 value: 97.205 - type: recall_at_3 value: 39.452999999999996 - type: recall_at_5 value: 48.333 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 95.47879616963064 - type: f1 value: 95.21800589958251 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.09256725946192 - type: f1 value: 60.554043889452515 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 75.53463349024882 - type: f1 value: 73.14418495756476 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.22663080026899 - type: f1 value: 79.331456217501 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.50316010430136 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.15612040042282 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.36227552557184 - type: mrr value: 33.57901344209811 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.6610000000000005 - type: map_at_10 value: 12.992 - type: map_at_100 value: 16.756999999999998 - type: map_at_1000 value: 18.25 - type: map_at_3 value: 9.471 - type: map_at_5 value: 11.116 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 53.388999999999996 - type: mrr_at_100 value: 53.982 - type: mrr_at_1000 value: 54.033 - type: mrr_at_3 value: 51.858000000000004 - type: mrr_at_5 value: 53.019000000000005 - type: ndcg_at_1 value: 41.641 - type: ndcg_at_10 value: 34.691 - type: ndcg_at_100 value: 32.305 - type: ndcg_at_1000 value: 41.132999999999996 - type: ndcg_at_3 value: 40.614 - type: ndcg_at_5 value: 38.456 - type: precision_at_1 value: 43.344 - type: precision_at_10 value: 25.881999999999998 - type: precision_at_100 value: 8.483 - type: precision_at_1000 value: 2.131 - type: precision_at_3 value: 38.803 - type: precision_at_5 value: 33.87 - type: recall_at_1 value: 5.6610000000000005 - type: recall_at_10 value: 16.826 - type: recall_at_100 value: 32.939 - type: recall_at_1000 value: 65.161 - type: recall_at_3 value: 10.756 - type: recall_at_5 value: 13.331000000000001 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 26.692 - type: map_at_10 value: 41.065000000000005 - type: map_at_100 value: 42.235 - type: map_at_1000 value: 42.27 - type: map_at_3 value: 36.635 - type: map_at_5 value: 39.219 - type: mrr_at_1 value: 30.214000000000002 - type: mrr_at_10 value: 43.443 - type: mrr_at_100 value: 44.326 - type: mrr_at_1000 value: 44.352000000000004 - type: mrr_at_3 value: 39.623999999999995 - type: mrr_at_5 value: 41.898 - type: ndcg_at_1 value: 30.214000000000002 - type: ndcg_at_10 value: 48.692 - type: ndcg_at_100 value: 53.671 - type: ndcg_at_1000 value: 54.522000000000006 - type: ndcg_at_3 value: 40.245 - type: ndcg_at_5 value: 44.580999999999996 - type: precision_at_1 value: 30.214000000000002 - type: precision_at_10 value: 8.3 - type: precision_at_100 value: 1.1079999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 18.521 - type: precision_at_5 value: 13.627 - type: recall_at_1 value: 26.692 - type: recall_at_10 value: 69.699 - type: recall_at_100 value: 91.425 - type: recall_at_1000 value: 97.78099999999999 - type: recall_at_3 value: 47.711 - type: recall_at_5 value: 57.643 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 70.962 - type: map_at_10 value: 84.772 - type: map_at_100 value: 85.402 - type: map_at_1000 value: 85.418 - type: map_at_3 value: 81.89 - type: map_at_5 value: 83.685 - type: mrr_at_1 value: 81.67 - type: mrr_at_10 value: 87.681 - type: mrr_at_100 value: 87.792 - type: mrr_at_1000 value: 87.79299999999999 - type: mrr_at_3 value: 86.803 - type: mrr_at_5 value: 87.392 - type: ndcg_at_1 value: 81.69 - type: ndcg_at_10 value: 88.429 - type: ndcg_at_100 value: 89.66 - type: ndcg_at_1000 value: 89.762 - type: ndcg_at_3 value: 85.75 - type: ndcg_at_5 value: 87.20700000000001 - type: precision_at_1 value: 81.69 - type: precision_at_10 value: 13.395000000000001 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.507000000000005 - type: precision_at_5 value: 24.614 - type: recall_at_1 value: 70.962 - type: recall_at_10 value: 95.339 - type: recall_at_100 value: 99.543 - type: recall_at_1000 value: 99.984 - type: recall_at_3 value: 87.54899999999999 - type: recall_at_5 value: 91.726 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.506631779239555 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.63731341848479 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 4.852 - type: map_at_10 value: 13.175 - type: map_at_100 value: 15.623999999999999 - type: map_at_1000 value: 16.002 - type: map_at_3 value: 9.103 - type: map_at_5 value: 11.068999999999999 - type: mrr_at_1 value: 23.9 - type: mrr_at_10 value: 35.847 - type: mrr_at_100 value: 36.968 - type: mrr_at_1000 value: 37.018 - type: mrr_at_3 value: 32.300000000000004 - type: mrr_at_5 value: 34.14 - type: ndcg_at_1 value: 23.9 - type: ndcg_at_10 value: 21.889 - type: ndcg_at_100 value: 30.903000000000002 - type: ndcg_at_1000 value: 36.992000000000004 - type: ndcg_at_3 value: 20.274 - type: ndcg_at_5 value: 17.773 - type: precision_at_1 value: 23.9 - type: precision_at_10 value: 11.61 - type: precision_at_100 value: 2.4539999999999997 - type: precision_at_1000 value: 0.391 - type: precision_at_3 value: 19.133 - type: precision_at_5 value: 15.740000000000002 - type: recall_at_1 value: 4.852 - type: recall_at_10 value: 23.507 - type: recall_at_100 value: 49.775000000000006 - type: recall_at_1000 value: 79.308 - type: recall_at_3 value: 11.637 - type: recall_at_5 value: 15.947 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 86.03345827446948 - type: cos_sim_spearman value: 80.53174518259549 - type: euclidean_pearson value: 83.44538971660883 - type: euclidean_spearman value: 80.57344324098692 - type: manhattan_pearson value: 83.36528808195459 - type: manhattan_spearman value: 80.48931287157902 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.21363088257881 - type: cos_sim_spearman value: 75.56589127055523 - type: euclidean_pearson value: 82.32868324521908 - type: euclidean_spearman value: 75.31928550664554 - type: manhattan_pearson value: 82.31332875713211 - type: manhattan_spearman value: 75.35376322099196 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 85.09085593258487 - type: cos_sim_spearman value: 86.26355088415221 - type: euclidean_pearson value: 85.49646115361156 - type: euclidean_spearman value: 86.20652472228703 - type: manhattan_pearson value: 85.44084081123815 - type: manhattan_spearman value: 86.1162623448951 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.68250248349368 - type: cos_sim_spearman value: 82.29883673695083 - type: euclidean_pearson value: 84.17633035446019 - type: euclidean_spearman value: 82.19990511264791 - type: manhattan_pearson value: 84.17408410692279 - type: manhattan_spearman value: 82.249873895981 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.31878760045024 - type: cos_sim_spearman value: 88.7364409031183 - type: euclidean_pearson value: 88.230537618603 - type: euclidean_spearman value: 88.76484309646318 - type: manhattan_pearson value: 88.17689071136469 - type: manhattan_spearman value: 88.72809249037928 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.41078559110638 - type: cos_sim_spearman value: 85.27439135411049 - type: euclidean_pearson value: 84.5333571592088 - type: euclidean_spearman value: 85.25645460575957 - type: manhattan_pearson value: 84.38428921610226 - type: manhattan_spearman value: 85.07796040798796 - task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.82374132382576 - type: cos_sim_spearman value: 89.02101343562433 - type: euclidean_pearson value: 89.50729765458932 - type: euclidean_spearman value: 89.04184772869253 - type: manhattan_pearson value: 89.51737904059856 - type: manhattan_spearman value: 89.12925950440676 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.56051823873482 - type: cos_sim_spearman value: 68.50988748185463 - type: euclidean_pearson value: 69.16524346147456 - type: euclidean_spearman value: 68.61859952449579 - type: manhattan_pearson value: 69.10618915706995 - type: manhattan_spearman value: 68.36401769459522 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 85.4159693872625 - type: cos_sim_spearman value: 87.07819121764247 - type: euclidean_pearson value: 87.03013260863153 - type: euclidean_spearman value: 87.06547293631309 - type: manhattan_pearson value: 86.8129744446062 - type: manhattan_spearman value: 86.88494096335627 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 86.47758088996575 - type: mrr value: 96.17891458577733 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 57.538999999999994 - type: map_at_10 value: 66.562 - type: map_at_100 value: 67.254 - type: map_at_1000 value: 67.284 - type: map_at_3 value: 63.722 - type: map_at_5 value: 65.422 - type: mrr_at_1 value: 60.0 - type: mrr_at_10 value: 67.354 - type: mrr_at_100 value: 67.908 - type: mrr_at_1000 value: 67.93299999999999 - type: mrr_at_3 value: 65.056 - type: mrr_at_5 value: 66.43900000000001 - type: ndcg_at_1 value: 60.0 - type: ndcg_at_10 value: 70.858 - type: ndcg_at_100 value: 73.67099999999999 - type: ndcg_at_1000 value: 74.26700000000001 - type: ndcg_at_3 value: 65.911 - type: ndcg_at_5 value: 68.42200000000001 - type: precision_at_1 value: 60.0 - type: precision_at_10 value: 9.4 - type: precision_at_100 value: 1.083 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.444 - type: precision_at_5 value: 17.0 - type: recall_at_1 value: 57.538999999999994 - type: recall_at_10 value: 83.233 - type: recall_at_100 value: 95.667 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 69.883 - type: recall_at_5 value: 76.19399999999999 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.82574257425742 - type: cos_sim_ap value: 95.78722833053911 - type: cos_sim_f1 value: 90.94650205761316 - type: cos_sim_precision value: 93.64406779661016 - type: cos_sim_recall value: 88.4 - type: dot_accuracy value: 99.83366336633664 - type: dot_ap value: 95.89733601612964 - type: dot_f1 value: 91.41981613891727 - type: dot_precision value: 93.42379958246346 - type: dot_recall value: 89.5 - type: euclidean_accuracy value: 99.82574257425742 - type: euclidean_ap value: 95.75227035138846 - type: euclidean_f1 value: 90.96509240246407 - type: euclidean_precision value: 93.45991561181435 - type: euclidean_recall value: 88.6 - type: manhattan_accuracy value: 99.82574257425742 - type: manhattan_ap value: 95.76278266220176 - type: manhattan_f1 value: 91.08409321175279 - type: manhattan_precision value: 92.29979466119097 - type: manhattan_recall value: 89.9 - type: max_accuracy value: 99.83366336633664 - type: max_ap value: 95.89733601612964 - type: max_f1 value: 91.41981613891727 - task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 61.905425988638605 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.159589881679736 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.0605499476397 - type: mrr value: 53.91594516594517 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.202718009067 - type: cos_sim_spearman value: 31.136199912366987 - type: dot_pearson value: 30.66329011927951 - type: dot_spearman value: 30.107664909625107 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.209 - type: map_at_10 value: 1.712 - type: map_at_100 value: 9.464 - type: map_at_1000 value: 23.437 - type: map_at_3 value: 0.609 - type: map_at_5 value: 0.9440000000000001 - type: mrr_at_1 value: 78.0 - type: mrr_at_10 value: 86.833 - type: mrr_at_100 value: 86.833 - type: mrr_at_1000 value: 86.833 - type: mrr_at_3 value: 85.333 - type: mrr_at_5 value: 86.833 - type: ndcg_at_1 value: 74.0 - type: ndcg_at_10 value: 69.14 - type: ndcg_at_100 value: 53.047999999999995 - type: ndcg_at_1000 value: 48.577 - type: ndcg_at_3 value: 75.592 - type: ndcg_at_5 value: 72.509 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 73.0 - type: precision_at_100 value: 54.44 - type: precision_at_1000 value: 21.326 - type: precision_at_3 value: 80.667 - type: precision_at_5 value: 77.2 - type: recall_at_1 value: 0.209 - type: recall_at_10 value: 1.932 - type: recall_at_100 value: 13.211999999999998 - type: recall_at_1000 value: 45.774 - type: recall_at_3 value: 0.644 - type: recall_at_5 value: 1.0290000000000001 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.609 - type: map_at_10 value: 8.334999999999999 - type: map_at_100 value: 14.604000000000001 - type: map_at_1000 value: 16.177 - type: map_at_3 value: 4.87 - type: map_at_5 value: 6.3149999999999995 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 45.047 - type: mrr_at_100 value: 45.808 - type: mrr_at_1000 value: 45.808 - type: mrr_at_3 value: 41.497 - type: mrr_at_5 value: 43.231 - type: ndcg_at_1 value: 30.612000000000002 - type: ndcg_at_10 value: 21.193 - type: ndcg_at_100 value: 34.97 - type: ndcg_at_1000 value: 46.69 - type: ndcg_at_3 value: 24.823 - type: ndcg_at_5 value: 22.872999999999998 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 17.959 - type: precision_at_100 value: 7.4079999999999995 - type: precision_at_1000 value: 1.537 - type: precision_at_3 value: 25.85 - type: precision_at_5 value: 22.448999999999998 - type: recall_at_1 value: 2.609 - type: recall_at_10 value: 13.63 - type: recall_at_100 value: 47.014 - type: recall_at_1000 value: 83.176 - type: recall_at_3 value: 5.925 - type: recall_at_5 value: 8.574 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 72.80239999999999 - type: ap value: 15.497911013214791 - type: f1 value: 56.258411577947285 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 61.00452744765139 - type: f1 value: 61.42228624410908 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 50.00516915962345 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.62317458425225 - type: cos_sim_ap value: 72.95115658063823 - type: cos_sim_f1 value: 66.78976523344764 - type: cos_sim_precision value: 66.77215189873418 - type: cos_sim_recall value: 66.80738786279683 - type: dot_accuracy value: 85.62317458425225 - type: dot_ap value: 73.10385271517778 - type: dot_f1 value: 66.94853829427399 - type: dot_precision value: 61.74242424242424 - type: dot_recall value: 73.11345646437995 - type: euclidean_accuracy value: 85.65893783155511 - type: euclidean_ap value: 72.87428208473992 - type: euclidean_f1 value: 66.70919994896005 - type: euclidean_precision value: 64.5910551025451 - type: euclidean_recall value: 68.97097625329816 - type: manhattan_accuracy value: 85.59933241938367 - type: manhattan_ap value: 72.67282695064966 - type: manhattan_f1 value: 66.67537215983286 - type: manhattan_precision value: 66.00310237849017 - type: manhattan_recall value: 67.36147757255937 - type: max_accuracy value: 85.65893783155511 - type: max_ap value: 73.10385271517778 - type: max_f1 value: 66.94853829427399 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.69096130709822 - type: cos_sim_ap value: 85.30326978668063 - type: cos_sim_f1 value: 77.747088683189 - type: cos_sim_precision value: 75.4491451753115 - type: cos_sim_recall value: 80.189405605174 - type: dot_accuracy value: 88.43870066363954 - type: dot_ap value: 84.62999949222983 - type: dot_f1 value: 77.3074661963551 - type: dot_precision value: 73.93871239808828 - type: dot_recall value: 80.99784416384355 - type: euclidean_accuracy value: 88.70066363953894 - type: euclidean_ap value: 85.34184508966621 - type: euclidean_f1 value: 77.76871756856931 - type: euclidean_precision value: 74.97855917667239 - type: euclidean_recall value: 80.77456113335386 - type: manhattan_accuracy value: 88.68319944114566 - type: manhattan_ap value: 85.3026464242333 - type: manhattan_f1 value: 77.66561049296294 - type: manhattan_precision value: 74.4665818849795 - type: manhattan_recall value: 81.15183246073299 - type: max_accuracy value: 88.70066363953894 - type: max_ap value: 85.34184508966621 - type: max_f1 value: 77.76871756856931 --- <h1 align="center">GIST small Embedding v0</h1> *GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) # Data The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: - Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) - Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`). The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. # Usage The model can be easily loaded using the Sentence Transformers library. ```Python import torch.nn.functional as F from sentence_transformers import SentenceTransformer revision = None # Replace with the specific revision to ensure reproducibility if the model is updated. model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision) texts = [ "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" ] # Compute embeddings embeddings = model.encode(texts, convert_to_tensor=True) # Compute cosine-similarity for each pair of sentences scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) print(scores.cpu().numpy()) ``` # Training Parameters Below are the training parameters used to fine-tune the model: ``` Epochs = 40 Warmup ratio = 0.1 Learning rate = 5e-6 Batch size = 16 Checkpoint step = 102000 Contrastive loss temperature = 0.01 ``` # Evaluation The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. # Citation Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 ``` @article{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, journal={arXiv preprint arXiv:2402.16829}, year={2024}, URL={https://arxiv.org/abs/2402.16829} eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # Acknowledgements This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Darkhn/test-EXL2-3.0bpw-H6

Darkhn

2025-05-27T19:15:34Z

0

exllamav2

[ "exllamav2", "quantized", "license:mit", "region:us" ]

null

2025-05-27T19:14:55Z

--- library_name: exllamav2 license: mit tags: - exllamav2 - quantized --- # test-EXL2-3.0bpw-H6 EXL2 quantized model of `/mnt/test/output/merged_passthrough_20250527_185209_194400` (the original base model). ## Quantization Details - **Bits per weight (bpw):** 3.0 - **Head Bits:** 6 - **Calibration Source:** Measurement derived from model weights (no explicit dataset calibration or provided measurement for this specific quantization pass). Quantized using the [exllamav2 library](https://github.com/turboderp/exllamav2).

bigband/FatherlyAthena

bigband

2025-05-27T19:12:12Z

0

transformers

[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T19:02:00Z

--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - **Model Name:** Bifröst-27B - **Base Architecture:** gemma3 - **Application:** Enterprise Secure Code Generation - **Release Date:** 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - **Security-Focused Training:** Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - **Enterprise-Optimized Performance:** Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - **Compliance-Driven Design:** Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the **Gemma license**. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.

CCTV-Wiring-Cikgu-hd/Bocor.Video.CCTV.wiring.cikgu.video.nur.fadhilah.binti.zainal.guru.part.2.video

CCTV-Wiring-Cikgu-hd

2025-05-27T19:10:21Z

0

null

[ "region:us" ]

null

2025-05-27T19:07:43Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=CCTV-Wiring-Cikgu) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=CCTV-Wiring-Cikgu) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=CCTV-Wiring-Cikgu)

Kikinoking/MNLP_M2_quantized_model

Kikinoking

2025-05-27T19:09:04Z

16

0

null

[ "pytorch", "safetensors", "qwen3", "causal-lm", "qwen", "fine-tuned", "quantized", "mnlp", "8-bit", "compressed-tensors", "region:us" ]

null

2025-05-24T21:37:20Z

--- tags: - causal-lm - qwen - fine-tuned - quantized - mnlp --- # Qwen3-0.6B Full-Precision + W8A8 Quantized MCQA Model **Repository:** [Kikinoking/MNLP_M2_quantized_model](https://huggingface.co/Kikinoking/MNLP_M2_quantized_model) This is a fine-tuned Qwen-3-0.6B causal-LM, trained on a concatenation of multiple MCQA datasets and then quantized to 8-bit weights and activations using the compressed-tensors format. It is designed for multiple-choice QA tasks, evaluated with the LightEval EPFL MNLP suite. --- ## Model Details - **Base architecture:** Qwen-3 (0.6B parameters) - **Pretrained checkpoint:** `Qwen/Qwen3-0.6B-Base` - **Fine-tuning data sources:** - ScienceQA - QASC - OpenBookQA - MathQA - CommonsenseQA - MCQA prompts generated via ChatGPT (labeled `M1_chatgpt`) - **Dataset split:** 95% train / 5% validation - **Tokenization:** - `AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B-Base")` - Left padding, EOS token as pad_token - Sequence length capped at 2048 tokens --- ## Quantization - **Method:** `compressed-tensors` / `naive-quantized` - **Precision:** 8-bit weights + 8-bit activations - **Layers kept in FP32:** Language modeling head - **Checkpoint:** Compatible with CPU and GPU inference --- ## Evaluation Tested using LightEval EPFL MNLP on the MCQA task: ```bash lighteval accelerate --eval-mode lighteval --save-details --override-batch-size 8 --custom-tasks community_tasks/mnlp_mcqa_evals.py --output-dir out/lighteval_quant model_configs/quantized_model.yaml "community|mnlp_mcqa_evals|0|0" Results: Accuracy: 0.30 ± 0.15 Normalized Accuracy: 0.30 ± 0.15 How to Use from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained( "Kikinoking/MNLP_M2_quantized_model", trust_remote_code=True ) model = AutoModelForCausalLM.from_pretrained( "Kikinoking/MNLP_M2_quantized_model", trust_remote_code=True, device_map="auto", ) License Being a 0.6B-parameter model, it may struggle with very long or ambiguous queries. Quantization can introduce a slight drop in accuracy (~5–10%). License: CC BY-NC 4.0 (inherits from the base Qwen-3 license)

Lubna-qureshi-viral/full.lubna.qureshi.viral.video.highway.lubna.qureshi.and.manohar.lal.dhakad.official

Lubna-qureshi-viral

2025-05-27T19:06:58Z

0

null

[ "region:us" ]

null

2025-05-27T18:56:44Z

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Mohamed-Aly/BABYLM-TOKENIZER-CHAR-PHON

Mohamed-Aly

2025-05-27T19:06:35Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T19:06:34Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Danielwu233/Llamma3.1-8B-Qlora

Danielwu233

2025-05-27T19:06:25Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T16:12:35Z

--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Danielwu233 - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

jaisalmer-kaka-hd/18.jaisalmer.kaka.jaisalmer.kaka.viral.jaisalmer.kaka.original.here.TRENDING

jaisalmer-kaka-hd

2025-05-27T19:05:13Z

0

null

[ "region:us" ]

null

2025-05-27T18:57:20Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=jaisalmer-kaka) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=jaisalmer-kaka) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=jaisalmer-kaka)

tylerachang/bigram-subnetworks-gpt2-large

tylerachang

2025-05-27T19:04:43Z

0

null

[ "eng", "arxiv:2504.15471", "license:apache-2.0", "region:us" ]

null

2025-04-21T04:54:16Z

--- license: apache-2.0 language: - eng --- # bigram-subnetworks-gpt2-large We release bigram subnetworks as described in [Chang and Bergen (2025)](https://arxiv.org/abs/2504.15471). These are sparse subsets of model parameters that recreate bigram predictions (next token predictions conditioned only on the current token) in Transformer language models. This repository contains the bigram subnetwork for [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large). ## Format A subnetwork file is a pickled Python dictionary that maps the original model parameter names to numpy binary masks with the same shapes as the original model parameters (1: keep, 0: drop). For details on usage, see: https://github.com/tylerachang/bigram-subnetworks. For details on how these subnetworks were trained, see [Chang and Bergen (2025)](https://arxiv.org/abs/2504.15471). For minimal usage, download the code at https://github.com/tylerachang/bigram-subnetworks (or just the file `circuit_loading_utils.py`) and run in Python: ``` from circuit_loading_utils import load_bigram_subnetwork_dict, load_subnetwork_model mask_dict = load_bigram_subnetwork_dict('openai-community/gpt2-large') model, tokenizer, config = load_subnetwork_model('openai-community/gpt2-large', mask_dict) ``` ## Citation <pre> @article{chang-bergen-2025-bigram, title={Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models}, author={Chang, Tyler A. and Bergen, Benjamin K.}, journal={Preprint}, year={2025}, url={https://arxiv.org/abs/2504.15471}, } </pre>

shaojintian/llaca-0.5B

shaojintian

2025-05-27T19:03:03Z

0

null

[ "safetensors", "ComplexFormer", "license:apache-2.0", "region:us" ]

null

2025-05-27T19:00:25Z

--- license: apache-2.0 ---

BootesVoid/cmb6uw4gb071rlexprcgvwbtx_cmb6uzmsu072plexpgfl5fg4h

BootesVoid

2025-05-27T19:01:36Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-05-27T19:01:34Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: vanessa_ --- # Cmb6Uw4Gb071Rlexprcgvwbtx_Cmb6Uzmsu072Plexpgfl5Fg4H <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `vanessa_` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "vanessa_", "lora_weights": "https://huggingface.co/BootesVoid/cmb6uw4gb071rlexprcgvwbtx_cmb6uzmsu072plexpgfl5fg4h/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb6uw4gb071rlexprcgvwbtx_cmb6uzmsu072plexpgfl5fg4h', weight_name='lora.safetensors') image = pipeline('vanessa_').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb6uw4gb071rlexprcgvwbtx_cmb6uzmsu072plexpgfl5fg4h/discussions) to add images that show off what you’ve made with this LoRA.

PRODRI007/ebooks

PRODRI007

2025-05-27T19:00:04Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-05-27T19:00:04Z

--- license: apache-2.0 ---

amaurypllx/MNLP_M2_quantized_model

amaurypllx

2025-05-27T18:59:25Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]

text-generation

2025-05-27T18:59:14Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

HPLT/hplt2c_eng90-edu_fra10_checkpoints

HPLT

2025-05-27T18:57:59Z

0

null

[ "pytorch", "llama", "HPLT", "decoder", "en", "dataset:HPLT/HPLT2.0_cleaned", "arxiv:2503.10267", "license:apache-2.0", "region:us" ]

null

2025-05-26T08:49:52Z

--- language: - en tags: - HPLT - decoder license: apache-2.0 datasets: - HPLT/HPLT2.0_cleaned --- # HPLT v2.0 - Cleaned - English (90%) <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%> This is one of the decoder-only language models trained on [HPLT2.0_cleaned](https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned). All the HPLT decoder-only models use the same hyper-parameters, roughly following the llama architecture with 2.15B parameters in total: - hidden size: 2048 - attention heads: 32 - layers: 24 - sequence length: 2048 ## Intermediate checkpoints We are releasing intermediate checkpoints for each model at intervals of every 1000 training steps in separate branches. The naming convention is `checkpoint_00xxxx00`: for example, `checkpoint_0005000`. The checkpoints range from checkpoint_0001000 to checkpoint_0047684 and the latter is in the main branch. ## Cite us ```bibtex @misc{burchell2025expandedmassivemultilingualdataset, title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies}, author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu}, year={2025}, eprint={2503.10267}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.10267}, } ```

punith0110/sft-tiny-chatbot

punith0110

2025-05-27T18:53:54Z

0

transformers

[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "endpoints_compatible", "region:us" ]

null

2025-05-27T18:52:31Z

--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: transformers model_name: sft-tiny-chatbot tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for sft-tiny-chatbot This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="punith0110/sft-tiny-chatbot", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.2 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

hjghjgn/hjgjhj

hjghjgn

2025-05-27T18:51:17Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-05-27T18:51:17Z

--- license: bigscience-bloom-rail-1.0 ---

aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs6

aamijar

2025-05-27T18:50:47Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T18:50:45Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dimasik2987/20405430-db49-4bce-a10d-37a0e37de08b

dimasik2987

2025-05-27T18:49:33Z

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:quantized:NousResearch/Nous-Capybara-7B-V1.9", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-05-27T17:25:31Z

--- base_model: NousResearch/Nous-Capybara-7B-V1.9 library_name: transformers model_name: 20405430-db49-4bce-a10d-37a0e37de08b tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 20405430-db49-4bce-a10d-37a0e37de08b This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dimasik2987/20405430-db49-4bce-a10d-37a0e37de08b", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/qgmz9hnx) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

manuross1/nrmmtrfckdfll500

manuross1

2025-05-27T18:48:55Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-05-27T18:32:23Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: nrmmtrfckdfll500 --- # Nrmmtrfckdfll500 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `nrmmtrfckdfll500` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nrmmtrfckdfll500", "lora_weights": "https://huggingface.co/manuross1/nrmmtrfckdfll500/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('manuross1/nrmmtrfckdfll500', weight_name='lora.safetensors') image = pipeline('nrmmtrfckdfll500').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 750 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/manuross1/nrmmtrfckdfll500/discussions) to add images that show off what you’ve made with this LoRA.

beanne-valerie-hd/beanne.scandal.beanne.valerie.dela.cruz.beanne.valerie.dela.cruz.telegram

beanne-valerie-hd

2025-05-27T18:46:36Z

0

null

[ "region:us" ]

null

2025-05-27T18:44:33Z

[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=beanne-valerie) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=beanne-valerie) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=beanne-valerie)

zinec/finetuned-eval-qwen3-0.6B

zinec

2025-05-27T18:46:23Z

0

transformers

[ "transformers", "safetensors", "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T18:41:07Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

plumpyfield/natix3

plumpyfield

2025-05-27T18:44:44Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-05-27T18:44:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

oskdabk/test_model_2

oskdabk

2025-05-27T18:41:57Z

0

transformers

[ "transformers", "pytorch", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]

text-generation

2025-05-27T18:41:23Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RobertoNeglia/pepe_generator_sd2

RobertoNeglia

2025-05-27T18:37:21Z

0

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:stabilityai/stable-diffusion-2", "base_model:adapter:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2025-05-27T13:32:07Z

--- base_model: stabilityai/stable-diffusion-2 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora ---  # LoRA text2image fine-tuning - RobertoNeglia/pepe_generator_sd2 These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the RobertoNeglia/pepe_dataset dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

Nasrin02/Nasrin

Nasrin02

2025-05-27T18:34:34Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-05-27T18:34:34Z

--- license: apache-2.0 ---

BootesVoid/cmb17aape05h4u1cgfybugm82_cmb6tq3zd06rvlexpqoqenle8

BootesVoid

2025-05-27T18:32:57Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-05-27T18:32:56Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: LATINA --- # Cmb17Aape05H4U1Cgfybugm82_Cmb6Tq3Zd06Rvlexpqoqenle8 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `LATINA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LATINA", "lora_weights": "https://huggingface.co/BootesVoid/cmb17aape05h4u1cgfybugm82_cmb6tq3zd06rvlexpqoqenle8/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb17aape05h4u1cgfybugm82_cmb6tq3zd06rvlexpqoqenle8', weight_name='lora.safetensors') image = pipeline('LATINA').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb17aape05h4u1cgfybugm82_cmb6tq3zd06rvlexpqoqenle8/discussions) to add images that show off what you’ve made with this LoRA.

Mohamed-Aly/BABYLM-TOKENIZER-BPE-PHON-SPACELESS

Mohamed-Aly

2025-05-27T18:32:07Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T18:32:04Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BeckerAnas/still-universe-209

BeckerAnas

2025-05-27T18:31:38Z

0

transformers

[ "transformers", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "base_model:facebook/convnextv2-tiny-1k-224", "base_model:finetune:facebook/convnextv2-tiny-1k-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-05-27T10:46:05Z

--- library_name: transformers license: apache-2.0 base_model: facebook/convnextv2-tiny-1k-224 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: still-universe-209 results: [] ---  # still-universe-209 This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5721 - Accuracy: 0.6497 - Precision: 0.6965 - Recall: 0.6497 - F1: 0.6583 - Roc Auc: 0.8795 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| | 1.379 | 1.0 | 17 | 1.3021 | 0.4544 | 0.5543 | 0.4544 | 0.4617 | 0.7377 | | 1.3695 | 2.0 | 34 | 1.2286 | 0.5391 | 0.5315 | 0.5391 | 0.5266 | 0.7806 | | 1.1945 | 3.0 | 51 | 1.1127 | 0.5794 | 0.5651 | 0.5794 | 0.5647 | 0.8098 | | 1.0517 | 4.0 | 68 | 0.8318 | 0.5872 | 0.6134 | 0.5872 | 0.5961 | 0.8273 | | 1.0323 | 5.0 | 85 | 0.8958 | 0.5156 | 0.6297 | 0.5156 | 0.5319 | 0.8189 | | 0.9029 | 6.0 | 102 | 0.7313 | 0.5365 | 0.6126 | 0.5365 | 0.5398 | 0.8267 | | 0.9002 | 7.0 | 119 | 0.7217 | 0.5794 | 0.5998 | 0.5794 | 0.5558 | 0.8445 | | 0.7855 | 8.0 | 136 | 0.6522 | 0.6029 | 0.6629 | 0.6029 | 0.6064 | 0.8581 | | 0.756 | 9.0 | 153 | 0.6371 | 0.5964 | 0.6263 | 0.5964 | 0.5653 | 0.8643 | | 0.7164 | 10.0 | 170 | 0.6291 | 0.5690 | 0.6930 | 0.5690 | 0.5780 | 0.8579 | | 0.6894 | 11.0 | 187 | 0.6194 | 0.5938 | 0.6360 | 0.5938 | 0.5735 | 0.8699 | | 0.6606 | 12.0 | 204 | 0.5834 | 0.6289 | 0.6906 | 0.6289 | 0.6402 | 0.8742 | | 0.6273 | 13.0 | 221 | 0.5766 | 0.6510 | 0.6972 | 0.6510 | 0.6607 | 0.8780 | | 0.6046 | 14.0 | 238 | 0.5732 | 0.6497 | 0.6965 | 0.6497 | 0.6583 | 0.8790 | | 0.6255 | 15.0 | 255 | 0.5721 | 0.6497 | 0.6965 | 0.6497 | 0.6583 | 0.8795 | ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cpu - Datasets 3.6.0 - Tokenizers 0.21.0

Jobz-Hunting-pakistani-viral-videos/EXCLUSIVE.VIDEO.NOW.leaked.Jobz.Hunting.Sajal.Malik.viral.video.original

Jobz-Hunting-pakistani-viral-videos

2025-05-27T18:30:37Z

0

null

[ "region:us" ]

null

2025-05-27T18:30:01Z

<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>

Hsianchengfun/1B-80epoch

Hsianchengfun

2025-05-27T18:29:25Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T18:26:37Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

joshcd/MNLP_M2_document_encoder

joshcd

2025-05-27T18:29:11Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T18:15:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mlx-community/sarvam-m-8bit

mlx-community

2025-05-27T18:29:03Z

2

0

mlx

[ "mlx", "safetensors", "mistral", "text-generation", "conversational", "en", "bn", "hi", "kn", "gu", "mr", "ml", "or", "pa", "ta", "te", "base_model:sarvamai/sarvam-m", "base_model:finetune:sarvamai/sarvam-m", "license:apache-2.0", "8-bit", "region:us" ]

text-generation

2025-05-27T00:12:38Z

--- library_name: mlx license: apache-2.0 language: - en - bn - hi - kn - gu - mr - ml - or - pa - ta - te base_model: sarvamai/sarvam-m base_model_relation: finetune pipeline_tag: text-generation tags: - mlx --- # mlx-community/sarvam-m-8bit This model [mlx-community/sarvam-m-8bit](https://huggingface.co/mlx-community/sarvam-m-8bit) was converted to MLX format from [sarvamai/sarvam-m](https://huggingface.co/sarvamai/sarvam-m) using mlx-lm version **0.24.1**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/sarvam-m-8bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

Krashouse/Flux_nastya

Krashouse

2025-05-27T18:27:31Z

0

null

[ "license:other", "region:us" ]

null

2025-05-27T15:47:51Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

keko24/MNLP_M2_mcqa_model-W4A8-Dynamic-Per-Token

keko24

2025-05-27T18:24:06Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]

text-generation

2025-05-27T18:23:32Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RizhongLin/MNLP_M2_dpo_model

RizhongLin

2025-05-27T18:24:04Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T18:23:39Z

--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MicPulseGh3/MicPulseGH

MicPulseGh3

2025-05-27T18:22:06Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-05-27T18:22:06Z

--- license: apache-2.0 ---

margaritamikhelson/MNLP_M2_mcqa_model

margaritamikhelson

2025-05-27T18:21:54Z

0

transformers

[ "transformers", "safetensors", "qwen3", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2025-05-27T18:20:38Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hopvfds/bhdfgffg

hopvfds

2025-05-27T18:18:13Z

0

null

[ "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2025-05-27T18:18:13Z

--- license: bigscience-bloom-rail-1.0 ---

Bonnief/mbert-am-100k-finetuned-II

Bonnief

2025-05-27T18:14:58Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2025-05-27T11:28:46Z

--- library_name: transformers license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy model-index: - name: mbert-am-100k-finetuned-II results: [] ---  # mbert-am-100k-finetuned-II This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.2069 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 100000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1

katrina-lim-viral-videos/VIDEO.18.Katrina.Lim.Kiffy.Viral.Video.Full.Video.Original.Clip

katrina-lim-viral-videos

2025-05-27T18:14:38Z

0

null

[ "region:us" ]

null

2025-05-27T18:14:06Z

<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>

DoniaGasmii/MNLP_M2_dpo_pure_pref

DoniaGasmii

2025-05-27T18:13:29Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T18:13:27Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

rtl-llm/qwen2.5coder-7b-origen-all-ordered-verilog-first

rtl-llm

2025-05-27T18:12:54Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T18:09:20Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ChrisKalahiki/mt0-large-lora

ChrisKalahiki

2025-05-27T18:11:50Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T18:11:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

vermoney/85fa0ba2-a848-4e57-a3c6-2be4516cf67d

vermoney

2025-05-27T18:11:41Z

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:quantized:NousResearch/Nous-Capybara-7B-V1.9", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-05-27T17:31:28Z

--- base_model: NousResearch/Nous-Capybara-7B-V1.9 library_name: transformers model_name: 85fa0ba2-a848-4e57-a3c6-2be4516cf67d tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 85fa0ba2-a848-4e57-a3c6-2be4516cf67d This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vermoney/85fa0ba2-a848-4e57-a3c6-2be4516cf67d", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-9/runs/05823jmk) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

rtl-llm/qwen2.5coder-7b-origen-vhdl-verilog-vhdl-pymtl

rtl-llm

2025-05-27T18:10:17Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T18:06:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dhintech/marian-ted2020-id-en-lg

dhintech

2025-05-27T18:09:32Z

0

transformers

[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "indonesian", "english", "fine-tuned", "meeting-translation", "domain-adaptation", "enhanced", "id", "en", "dataset:ted_talks_iwslt", "base_model:Helsinki-NLP/opus-mt-id-en", "base_model:finetune:Helsinki-NLP/opus-mt-id-en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2025-05-27T12:51:05Z

--- language: - id - en license: apache-2.0 base_model: Helsinki-NLP/opus-mt-id-en tags: - translation - indonesian - english - marian - fine-tuned - meeting-translation - domain-adaptation - enhanced pipeline_tag: translation datasets: - ted_talks_iwslt library_name: transformers metrics: - bleu - rouge widget: - text: "Selamat pagi semuanya, mari kita mulai rapat hari ini." example_title: "Meeting Opening" - text: "Tim marketing akan bertanggung jawab untuk strategi ini." example_title: "Task Assignment" - text: "Database migration sudah selesai dan berjalan dengan lancar." example_title: "Technical Update" --- # Enhanced MarianMT Indonesian-English Translation (Meeting Domain Adaptation) This model is an **enhanced fine-tuned version** of [Helsinki-NLP/opus-mt-id-en](https://huggingface.co/Helsinki-NLP/opus-mt-id-en) with **domain-specific adaptation** for meeting and business contexts. ## 🎯 Model Highlights - **Domain Adaptation**: Specialized for meeting and business translation - **Enhanced Dataset**: TED2020 + 2000+ meeting-specific sentence pairs - **Improved Performance**: Better BLEU scores on meeting contexts - **Robust Training**: 80% dataset usage with domain mixing - **Production Ready**: Optimized for real-world meeting scenarios ## 📊 Performance Metrics | Metric | Base Model | This Model | Improvement | |--------|------------|------------|-------------| | BLEU Score | 1.467 | **3.736** | **+154.6%** | | Translation Speed | 1.2s | **0.14s** | **-88.2%** | | Meeting Context | Standard | **Enhanced** | **Domain Adapted** | ## 🚀 Model Details - **Base Model**: Helsinki-NLP/opus-mt-id-en - **Training Dataset**: TED2020 (80%) + Meeting Domain (10%) - **Training Strategy**: Domain adaptation with enhanced learning - **Specialization**: Business meetings, technical discussions, formal conversations - **Training Date**: 2025-05-27 - **Languages**: Indonesian (id) → English (en) - **License**: Apache 2.0 ## 🛠️ Usage ```python from transformers import MarianMTModel, MarianTokenizer # Load model and tokenizer model_name = "dhintech/marian-ted2020-id-en-lg" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) # Translate Indonesian to English def translate(text): inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128) outputs = model.generate( **inputs, max_length=128, num_beams=3, early_stopping=True, do_sample=False ) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example usage indonesian_text = "Tim marketing akan bertanggung jawab untuk strategi ini." english_translation = translate(indonesian_text) print(english_translation) # Output: "The marketing team will be responsible for this strategy." ``` ## 📝 Example Translations ### Meeting Context Examples | Indonesian | English | Context | |------------|---------|---------| | Selamat pagi semuanya, mari kita mulai rapat hari ini. | Good morning everyone, let's start today's meeting. | Meeting Opening | | Tim marketing akan bertanggung jawab untuk strategi ini. | The marketing team will be responsible for this strategy. | Task Assignment | | Database migration sudah selesai dan berjalan dengan lancar. | Database migration is complete and running smoothly. | Technical Update | | Budget yang disetujui adalah 500 juta rupiah. | The approved budget is 500 million rupiah. | Financial Discussion | ## 🎯 Intended Use Cases - **Business Meeting Translation**: Real-time translation during meetings - **Technical Documentation**: Translating technical meeting notes - **Corporate Communication**: Formal business correspondence - **Project Management**: Translating project updates and reports - **Training Materials**: Educational and training content translation ## 📊 Training Configuration - **Dataset Size**: 118,626 sentence pairs - **TED2020 Data**: 80% of cleaned dataset - **Meeting Domain Data**: 10% specialized meeting content - **Max Sequence Length**: 128 tokens - **Training Epochs**: 12 - **Learning Rate**: 1e-05 - **Batch Size**: 12 (effective) ## 🔧 Technical Specifications - **Model Architecture**: MarianMT (Transformer-based) - **Parameters**: ~74M (with selective fine-tuning) - **Max Input/Output Length**: 128 tokens - **Inference Time**: ~0.14s per sentence - **Memory Requirements**: - GPU: 3GB VRAM minimum - CPU: 4GB RAM minimum ## 🚨 Limitations - **Domain Specificity**: Optimized for formal business/meeting contexts - **Informal Language**: May not perform optimally on very casual Indonesian - **Regional Dialects**: Trained primarily on standard Indonesian - **Cultural Context**: Some cultural nuances may be lost in translation ## 📚 Citation ```bibtex @misc{enhanced-marian-id-en-2025, title={Enhanced MarianMT Indonesian-English Translation (Meeting Domain Adaptation)}, author={DhinTech}, year={2025}, publisher={Hugging Face}, journal={Hugging Face Model Hub}, howpublished={\url{https://huggingface.co/dhintech/marian-id-en-enhanced}}, note={Enhanced with TED2020 and meeting-specific domain adaptation} } ``` ## 🙏 Acknowledgments - **Base Model**: Helsinki-NLP team for the original opus-mt-id-en model - **Dataset**: TED2020 corpus and custom meeting domain data - **Framework**: Hugging Face Transformers team --- *This model is specifically enhanced for Indonesian business meeting translation scenarios with domain adaptation techniques.*

JorgeTC/electra-corrected-POS

JorgeTC

2025-05-27T18:08:57Z

0

transformers

[ "transformers", "safetensors", "electra", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2025-05-27T18:08:51Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

18-Katrina-Lim-Kiffy-Viral-Video-Link-hd/INDIA.FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official

18-Katrina-Lim-Kiffy-Viral-Video-Link-hd

2025-05-27T18:08:35Z

0

null

[ "region:us" ]

null

2025-05-27T18:08:07Z

<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>

JorgeTC/miniLM-corrected-POS

JorgeTC

2025-05-27T18:03:20Z

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2025-05-27T18:03:10Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hexuan21/Qwen2.5-7B-EnergyQA_lora

hexuan21

2025-05-27T18:01:43Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]

null

2025-05-27T17:16:00Z

--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2

slang88/gemma-sql

slang88

2025-05-27T17:59:33Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]

null

2025-05-22T16:09:42Z

--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-sql tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-sql This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="slang88/gemma-sql", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

phunghuy159/full_model_sft

phunghuy159

2025-05-27T17:59:22Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T17:44:22Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

othoi-113-viral-video-link-hdq/exclusive.link.othoiiii.viral.video.link.othoi.viral.video.link.1.13.second

othoi-113-viral-video-link-hdq

2025-05-27T17:58:04Z

0

null

[ "region:us" ]

null

2025-05-27T17:57:14Z

<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>

maksymveremchuk/deepseek_qwen_32B_v2.1

maksymveremchuk

2025-05-27T17:56:07Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-05-27T17:54:42Z

--- base_model: unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** maksymveremchuk - **License:** apache-2.0 - **Finetuned from model :** unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

luis-orvium/prueba-memo-desde-checkpoint

luis-orvium

2025-05-27T17:54:11Z

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-05-27T17:53:50Z

--- base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** luis-orvium - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-1b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

maksymveremchuk/deepseek_qwen_23B_v2.1

maksymveremchuk

2025-05-27T17:53:12Z

0

transformers

[ "transformers", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-05-27T17:53:12Z

--- base_model: unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** maksymveremchuk - **License:** apache-2.0 - **Finetuned from model :** unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

othoi-113-viral-video-link-4k-hd/Original.othoiiii.viral.video.link.othoi.viral.video.link.1.13.second

othoi-113-viral-video-link-4k-hd

2025-05-27T17:51:46Z

0

null

[ "region:us" ]

null

2025-05-27T17:51:20Z

<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️&ZeroWidthSpace;</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>

BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4

BootesVoid

2025-05-27T17:51:22Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-05-27T17:51:20Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: LIA --- # Cmay2E8B8038Bu1Cguoswiyvb_Cmb6Sbsy206Islexp4Uw5Jtb4 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `LIA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LIA", "lora_weights": "https://huggingface.co/BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4', weight_name='lora.safetensors') image = pipeline('LIA').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4/discussions) to add images that show off what you’ve made with this LoRA.

aldigobbler/smol-moe-360M-v0.1

aldigobbler

2025-05-27T17:51:15Z

0

null

[ "region:us" ]

null

2025-05-27T17:48:52Z

# smol-moe-360M-v0.1 **Experimental Sparse MoE (Mixture of Experts) with 4x 360M Llama model (smollmv2)s** Router is a learned gating network, experts are: - HuggingFaceTB/SmolLM2-360M-Instruct - motexture/SmolLCoder-360M-Instruct - prithivMLmods/SmolLM2-CoT-360M - quwsarohi/SmolThink ## Training - Dataset: [`flytech/python-codes-25k`](https://huggingface.co/datasets/flytech/python-codes-25k) - Each sample is formatted as a chat: ``` [ {"role": "user", "content": instruction}, {"role": "assistant", "content": output} ] ``` - MoE layers at: 8, 12, 16, 20, 24, 28 (out of 32 total) - Top-2 routing (each token activates 2 out of 4 experts) - Trained for a few epochs, batch size 4, gradient accumulation 8, max length 1024 - Used AdamW, linear warmup, and auxiliary load balancing loss ## Model - Total params: ~1.5B (but only 2 experts active per token, so much faster than a dense 4x model) - All expert MLPs are included in the checkpoint, you don’t need the original models - Router and experts are trained end-to-end - Checkpoints include: `pytorch_model.bin` (full model) and `config.json` (architecture info) ## Results ### COME BACK LATER ITS TRAINING ## Notes - This is a real MoE: router is learned, experts are tied into the same model, and routing is sparse (top-2). - For research/experimentation only. - If you make something cool with it, let me know! --- *smol-moe-360M-v0.1: for science, for fun, for smol code*

BootesVoid/cmb68j487037slexpyp14cyxw_cmb69dzvn03avlexphlqqxvt8

BootesVoid

2025-05-27T17:49:43Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-05-27T17:49:41Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: jaylin --- # Cmb68J487037Slexpyp14Cyxw_Cmb69Dzvn03Avlexphlqqxvt8 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `jaylin` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "jaylin", "lora_weights": "https://huggingface.co/BootesVoid/cmb68j487037slexpyp14cyxw_cmb69dzvn03avlexphlqqxvt8/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb68j487037slexpyp14cyxw_cmb69dzvn03avlexphlqqxvt8', weight_name='lora.safetensors') image = pipeline('jaylin').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb68j487037slexpyp14cyxw_cmb69dzvn03avlexphlqqxvt8/discussions) to add images that show off what you’ve made with this LoRA.

Farmerobot/deepseek-r1-among-them

Farmerobot

2025-05-27T17:48:06Z

31

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-04-24T17:16:19Z

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

reza-rgb/MNLP_M2_dpo_model

reza-rgb

2025-05-27T17:47:33Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T17:45:32Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Smxldo/MNLP_M2_document_encoder

Smxldo

2025-05-27T17:44:55Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-05-21T09:33:52Z

--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L12-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L12-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,170,060,424** |

nielsgl/olmOCR-7B-0225-preview-8bit

nielsgl

2025-05-27T17:43:18Z

0

transformers

[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "mlx", "conversational", "en", "dataset:allenai/olmOCR-mix-0225", "base_model:Qwen/Qwen2-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2-VL-7B-Instruct", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

2025-05-27T17:32:28Z

--- language: - en license: apache-2.0 datasets: - allenai/olmOCR-mix-0225 base_model: - Qwen/Qwen2-VL-7B-Instruct library_name: transformers tags: - mlx --- # nielsgl/olmOCR-7B-0225-preview-8bit This model was converted to MLX format from [`allenai/olmOCR-7B-0225-preview`]() using mlx-vlm version **0.1.26**. Refer to the [original model card](https://huggingface.co/allenai/olmOCR-7B-0225-preview) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model nielsgl/olmOCR-7B-0225-preview-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image> ```

09-Sophie-Rain-Sophie-Rain-SpiderMan-Video/Sophie.Rain.Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video

09-Sophie-Rain-Sophie-Rain-SpiderMan-Video

2025-05-27T17:42:59Z

0

null

[ "region:us" ]

null

2025-05-27T17:42:47Z

18 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a> <a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a> Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter

eth-nlped/TutorRL-7B-think

eth-nlped

2025-05-27T17:42:12Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "math-tutor", "grpo", "conversational", "dataset:SynthLabsAI/Big-Math-RL-Verified", "arxiv:2505.15607", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T11:59:52Z

--- library_name: transformers license: apache-2.0 license_link: https://github.com/eth-lre/PedagogicalRL/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen2.5-7B-Instruct tags: - math-tutor - grpo datasets: - SynthLabsAI/Big-Math-RL-Verified --- # TutorRL-7B-think ## Overview **TutorRL-7B-think** is a fine-tuned variant of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), trained to act as a math **tutor** rather than a solver. It is aligned to pedagogical principles using **reinforcement learning (GRPO)** in a synthetic multi-turn classroom setting, without requiring any human-labeled data. This model was developed as part of the research project [*From Problem-Solving to Teaching Problem-Solving*](https://arxiv.org/abs/2505.15607), which proposes a scalable, annotation-free approach to training LLMs as **educational tutors**. Instead of directly answering questions, the model is optimized to scaffold reasoning, guide through Socratic questioning, and withhold final solutions when beneficial for learning. Repository: [https://github.com/eth-lre/PedagogicalRL](https://github.com/eth-lre/PedagogicalRL) ## Intended Use This model is intended for use in: * Interactive math tutoring * Socratic dialogue generation * Research on educational alignment of LLMs * Safe and indirect teaching in problem-solving contexts ## Thinking This model variant allows for hidden thinking. The thinking content is enclosed in tags: `<think> ... </think>`. ## Example Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "eth-nlped/TutorRL-7B-think" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") messages = [ {"role": "user", "content": "Can you help me solve 3x + 5 = 20?"} ] prompt = tokenizer.apply_chat_template(messages, tokenize=False) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Citation If you use this model or build upon the training framework, please cite: ``` @misc{dinucujianu2025problemsolvingteachingproblemsolvingaligning, title={From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning}, author={David Dinucu-Jianu and Jakub Macina and Nico Daheim and Ido Hakimi and Iryna Gurevych and Mrinmaya Sachan}, year={2025}, eprint={2505.15607}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.15607} } ```

ngfh54456/bvcvdfsa

ngfh54456

2025-05-27T17:41:53Z

0

null

[ "license:bigcode-openrail-m", "region:us" ]

null

2025-05-27T17:41:53Z

--- license: bigcode-openrail-m ---

dwi1205/A1B2C3

dwi1205

2025-05-27T17:41:17Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-05-27T17:41:17Z

--- license: apache-2.0 ---

Mohamed-Aly/BABYLM-TOKENIZER-CHAR-TXT

Mohamed-Aly

2025-05-27T17:40:43Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-05-27T17:40:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

07-Sophie-Rain-Sophie-Rain-SpiderMan-Video/Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video

07-Sophie-Rain-Sophie-Rain-SpiderMan-Video

2025-05-27T17:40:07Z

0

null

[ "region:us" ]

null

2025-05-27T17:39:35Z

18 seconds ago <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a> <a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a> <a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a> Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter

jegeblad/poca-SoccerTwos

jegeblad

2025-05-27T17:40:02Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2025-05-27T06:17:59Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jegeblad/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

moelanoby/ALM-Qwen-0.5B-testing

moelanoby

2025-05-27T17:35:41Z

0

1

transformers

[ "transformers", "safetensors", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-05-24T13:55:25Z

--- base_model: - Qwen/Qwen2.5-0.5B-Instruct library_name: transformers --- # ALM-Qwen Model: ALM-Qwen-0.5B-testing This repository contains an Attention-Linked Memory augmented Qwen model (ALM-Qwen). ## Model Components * **AttentionLinkedMemory (ALM)**: A custom PyTorch module for two-level attention-based retrieval from structured memory. (See `ALM.py`) * **QwenGenerator**: Wraps a Hugging Face Qwen model (e.g., Qwen2.5-0.5B-Instruct or Qwen2.5-7B-Instruct) for text generation. * **ALMQwenModel_HF**: The main class orchestrating the ALM retrieval and Qwen generation. (See `alm_qwen.py`) * **Saved Weights & Config**: * `alm_layer_state_dict.pth`: Trained weights for the ALM layer. * `alm_qwen_hf_config.json`: Configuration for the `ALMQwenModel_HF`, including ALM parameters and paths to the Qwen components. * `qwen_generator/`: Contains the saved Hugging Face Qwen model and tokenizer. ## How to Use 1. **Prerequisites**: ```bash pip install torch transformers huggingface_hub sentencepiece accelerate # Add other dependencies if any, e.g., bitsandbytes for quantization ``` 2. **Clone the repository (or download files manually)**: ```bash git lfs install # if large files are used, though typically not for these components directly git clone https://huggingface.co/moelanoby/ALM-Qwen-0.5B-testing cd ALM-Qwen-0.5B-testing ``` 3. **Load the model in Python**: ```python from alm_qwen import ALMQwenModel_HF # Make sure alm_qwen_hf.py and ALM.py are in your PYTHONPATH import torch # Desired device device = "cuda" if torch.cuda.is_available() else "cpu" # Path to the directory where you cloned/downloaded the model model_directory = "." # Or the specific path if you are running from outside the cloned repo # Load the model loaded_model = ALMQwenModel_HF.load_model(model_directory, device=device) print("ALM-Qwen model loaded successfully!") # --- Prepare Dummy Input Data (similar to the example in alm_qwen_hf.py) --- # batch_size = 1 # alm_query_dim = loaded_model.alm_config['query_dim'] # alm_memory_dim = loaded_model.alm_config['memory_dim'] # num_kb_buckets = 3 # Example # max_kb_items_per_bucket = 5 # Example # query_texts = ["What is the capital of France?"] # query_embeddings_for_alm = torch.randn(batch_size, alm_query_dim) # memory_item_embeddings = torch.randn(batch_size, num_kb_buckets, max_kb_items_per_bucket, alm_memory_dim) # memory_text_items = [[["Paris is the capital of France." for _ in range(max_kb_items_per_bucket)] for _ in range(num_kb_buckets)] for _ in range(batch_size)] # memory_mask = torch.ones(batch_size, num_kb_buckets, max_kb_items_per_bucket, dtype=torch.bool) # memory_mask[:, :, -1] = False # Example mask # # Run inference # generated_answers, _, _ = loaded_model( # query_texts, # query_embeddings_for_alm, # memory_item_embeddings, # memory_text_items, # memory_mask # ) # print(f"Query: {query_texts[0]}") # print(f"Answer: {generated_answers[0]}") ``` ## Training The ALM layer (`alm_layer_state_dict.pth`) might have been trained. The Qwen model inside `qwen_generator/` is typically a pre-trained model from Hugging Face, possibly fine-tuned. ## Notes * The Qwen model components can be large. Ensure you have sufficient disk space and network bandwidth. * The `load_model` method in `alm_qwen_hf.py` handles the reconstruction of the composite model. * If any errors happen use alm_qwen.py directly ---

aldigobbler/smollmv2-135Mx3E-MoE-v0.1

aldigobbler

2025-05-27T17:29:57Z

0

null

[ "region:us" ]

null

2025-05-27T15:37:44Z

# !! "moe" - routed inference between 3 different models without any tying experimental MoE with 3 experts totalling 480m~ params router is roughly 70M params no loss chart for this router trained on 15 samples

davanstrien/SmolLM2-360M-tldr-sft-2025-05-27_18-14

davanstrien

2025-05-27T17:29:40Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:HuggingFaceTB/SmolLM2-360M", "base_model:finetune:HuggingFaceTB/SmolLM2-360M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T16:15:27Z

--- base_model: HuggingFaceTB/SmolLM2-360M library_name: transformers model_name: SmolLM2-360M-tldr-sft-2025-05-27_18-14 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for SmolLM2-360M-tldr-sft-2025-05-27_18-14 This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="davanstrien/SmolLM2-360M-tldr-sft-2025-05-27_18-14", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/davanstrien/huggingface/runs/tsp2cqil) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

lisabdunlap/balanced_sft_long_e10

lisabdunlap

2025-05-27T17:29:29Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/Qwen3-8B", "base_model:finetune:unsloth/Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-05-27T17:28:07Z

--- base_model: unsloth/Qwen3-8B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** lisabdunlap - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-8B This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

MarceauBBB/qwen3-0.6B-Base-ORPO-OpenAnswers

MarceauBBB

2025-05-27T17:26:46Z

21

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-26T21:14:37Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

AShi846/MNLP_M2_document_encoder

AShi846

2025-05-27T17:22:55Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2025-05-27T14:33:12Z

--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,170,060,424** |

OlofBen/HeartLM-v4.3

OlofBen

2025-05-27T17:22:46Z

0

transformers

[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2025-05-27T17:05:36Z

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