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publication-charaf/MCQ_Qwen3-0.6B-Base_lr-5e-05_e-1_s-0
publication-charaf
2025-06-04T15:43:41Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:22:41Z
--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: MCQ_Qwen3-0.6B-Base_lr-5e-05_e-1_s-0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MCQ_Qwen3-0.6B-Base_lr-5e-05_e-1_s-0 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-5e-05_e-1_s-0", 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/kamel-charaf-epfl/huggingface/runs/ptu3c3gv) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-5e-06_e-1_s-0
publication-charaf
2025-06-04T15:43:27Z
25
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-03T14:07:41Z
--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: MCQ_Qwen3-0.6B-Base_lr-5e-06_e-1_s-0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MCQ_Qwen3-0.6B-Base_lr-5e-06_e-1_s-0 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-5e-06_e-1_s-0", 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/kamel-charaf-epfl/huggingface/runs/gqrqptjx) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
IshtaJ/ecg-analysis_AICRN
IshtaJ
2025-06-04T15:43:25Z
0
0
null
[ "tensorboard", "region:us" ]
null
2025-06-04T14:14:56Z
🔗 GitHub Repository: [https://github.com/cepdnaclk/e17-4yp-Comprehensive-ECG-analysis-with-Deep-Learning-on-GPU-accelerators](https://github.com/cepdnaclk/e17-4yp-Comprehensive-ECG-analysis-with-Deep-Learning-on-GPU-accelerators)
publication-charaf/MCQ_Qwen3-0.6B-Base_lr-5e-07_e-1_s-0
publication-charaf
2025-06-04T15:43:23Z
45
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-03T14:07:40Z
--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: MCQ_Qwen3-0.6B-Base_lr-5e-07_e-1_s-0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MCQ_Qwen3-0.6B-Base_lr-5e-07_e-1_s-0 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="publication-charaf/MCQ_Qwen3-0.6B-Base_lr-5e-07_e-1_s-0", 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/kamel-charaf-epfl/huggingface/runs/nz257rzo) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - 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}} } ```
ML-enthusiast-brinda/CyberBuddy-Gemma
ML-enthusiast-brinda
2025-06-04T15:43:22Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-06-04T15:43:13Z
--- 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]
navodPeiris/Vulnerability-Analyst-Qwen2.5-1.5B-Instruct
navodPeiris
2025-06-04T15:43:10Z
0
1
transformers
[ "transformers", "safetensors", "question-answering", "chat", "text-generation", "unsloth", "trl", "sft", "dataset:Mackerel2/cybernative_code_vulnerability_cot", "license:mit", "endpoints_compatible", "region:us" ]
question-answering
2025-06-04T09:14:49Z
--- base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit library_name: transformers model_name: Vulnerability-Analyst-Qwen2.5-1.5B-Instruct tags: - question-answering - chat - text-generation - unsloth - trl - sft licence: license license: mit datasets: - Mackerel2/cybernative_code_vulnerability_cot --- # Introduction This model is a fine-tuned version of [unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). This model is fine-tuned for detecting vulnerabilities in code with the Chain-of-Thought method. Dataset Used: [Mackerel2/cybernative_code_vulnerability_cot](https://huggingface.co/datasets/Mackerel2/cybernative_code_vulnerability_cot) ## Use Cases - Use for code vulnerability analysis - Use for general code related question answering (use without given chat template) ## Use model with a chat template for Chain-of-Thought response ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline from peft import PeftModel # Define model IDs base_model_id = "Qwen/Qwen2.5-1.5B-Instruct" finetuned_model_id = "navodPeiris/Vulnerability-Analyst-Qwen2.5-1.5B-Instruct" # Load tokenizer (trust remote code for Qwen models) tokenizer = AutoTokenizer.from_pretrained(finetuned_model_id, trust_remote_code=True) # Load base model model = AutoModelForCausalLM.from_pretrained( base_model_id, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True ) # Apply LoRA weights model = PeftModel.from_pretrained(model, finetuned_model_id) # (Optional) Merge LoRA adapters for faster inference model = model.merge_and_unload() # Prompt construction system_prompt = ( "You are an expert coder with a strong code vulnerability detection and reasoning ability. " "You first think through the reasoning process step-by-step in your mind and then provide the user with the answer." ) user_prompt = ( "Below is a question that describes a coding related problem. Write a response that appropriately answers the question. " "Show your reasoning in <think> </think> tags. And return the final response in <answer> </answer> tags.\n" "###Question###:\n{question}\n" "###Response###:\n<think>" ) # Example question question = """Find vulnerabilities in the following PHP code: ```php <?php $db = new PDO('mysql:host=localhost;dbname=test', $user, $pass); $username = $_GET['username']; $password = $_GET['password']; $sql = "SELECT * FROM users WHERE username = '$username' AND password = '$password'"; foreach ($db->query($sql) as $row) { print_r($row); } ?> ```""" # Apply tokenizer's chat template prompt = tokenizer.apply_chat_template( [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt.format(question=question)}, ], tokenize=False, add_generation_prompt=True, ) # Run inference using transformers pipeline pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto") output = pipe(prompt, max_new_tokens=1024, return_full_text=False)[0]["generated_text"] print("<think>\n" + output) ``` ## Training procedure This model was trained with SFT Trainer of trl library. I have Leveraged Unsloth’s FastLanguageModel with 4-bit quantization and smart gradient checkpointing to fit within consumer GPUs. I designed prompts where reasoning is enclosed in \<think>...\</think> and final answers in \<answer>...\</answer>. This helps guide the model to reason step-by-step before answering. I have used SFTTrainer from HuggingFace TRL with LoRA + 8bit optimizer + cosine LR scheduling. Evaluation is performed every 50 steps. I have used PEFT/LoRA for efficient fine-tuning. | Parameter | Value | |----------------------------|-------------------------------------| | `per_device_train_batch_size` | 8 | | `gradient_accumulation_steps` | 4 | | `per_device_eval_batch_size` | 16 | | `logging_steps` | 50 | | `eval_steps` | 50 | | `num_train_epochs` | 2 | | `warmup_ratio` | 0.03 | | `learning_rate` | 3e-5 | | `fp16` | True | | `optim` | adamw_8bit | | `weight_decay` | 0.1 | | `lr_scheduler_type` | cosine | | `dataset_text_field` | prompt | | `max_seq_length` | 1024 | | `lora_rank (r)` | 16 | | `target_modules` | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj | | `lora_alpha` | 32 | | `use_gradient_checkpointing` | unsloth | ### Framework versions - TRL: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.6.0 - 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}} } ```
Diamantis99/F0LUB7m
Diamantis99
2025-06-04T15:42:42Z
0
0
segmentation-models-pytorch
[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]
image-segmentation
2025-06-04T15:42:29Z
--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # PAN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "se_resnext101_32x4d", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 32, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8626857995986938, "test_dataset_iou": 0.881563127040863 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
mradermacher/ReasonFlux-Coder-14B-GGUF
mradermacher
2025-06-04T15:42:09Z
0
0
transformers
[ "transformers", "gguf", "en", "base_model:Gen-Verse/ReasonFlux-Coder-14B", "base_model:quantized:Gen-Verse/ReasonFlux-Coder-14B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]
null
2025-06-04T09:25:20Z
--- base_model: Gen-Verse/ReasonFlux-Coder-14B language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Gen-Verse/ReasonFlux-Coder-14B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q2_K.gguf) | Q2_K | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q3_K_S.gguf) | Q3_K_S | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q3_K_L.gguf) | Q3_K_L | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.IQ4_XS.gguf) | IQ4_XS | 8.3 | | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q5_K_S.gguf) | Q5_K_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q5_K_M.gguf) | Q5_K_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q6_K.gguf) | Q6_K | 12.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/ReasonFlux-Coder-14B-GGUF/resolve/main/ReasonFlux-Coder-14B.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
science-of-finetuning/SAE-difference-gemma-2-2b-L13-k100-lr1e-04-local-shuffling
science-of-finetuning
2025-06-04T15:41:05Z
26
0
null
[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]
null
2025-05-04T19:33:21Z
--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model was trained on `base_activations - chat_activations` from gemma-2-2b This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/science-of-finetuning/dictionary_learning/ - Docs: [More Information Needed]
miraa927/myfinetunebert
miraa927
2025-06-04T15:40:46Z
0
0
sentence-transformers
[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:160", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2025-06-04T12:42:18Z
--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:160 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: Quelles sont les personnes impliquées et solicitées et leur statut ? sentences: - Le programme s’appuie sur une pédagogie expérientielle validée par plus de 150 000 bénéficiaires depuis 10 ans. - Une responsable de programme conçoit les contenus pédagogiques et accompagne les jeunes. - Le Ticket Camp permet aux jeunes de concevoir un projet seul ou en équipe. - source_sentence: Quel est l'impact social de votre structure ? sentences: - Une équipe de bénévoles apporte un soutien précieux au projet à chaque édition. - Le Ticket Camp contribue à la redynamisation des territoires en favorisant la création d’emplois locaux. - Par exemple notre partenaire Rura accompagne 8 000 jeunes issus de territoires ruraux. - source_sentence: Quelles sont les perspectives de développement de votre association ? sentences: - Chaque équipe est suivie par un mentor expert du territoire ou de l’accompagnement des jeunes. - Les bénévoles donnent environ 150 heures de leur temps pour chaque édition du Ticket Camp. - Développement de nouveaux programmes et services pour répondre aux besoins émergents. - source_sentence: Quel est l'impact social de votre structure ? sentences: - L'objectif est de favoriser l'égalité des chances en ciblant les jeunes confrontés à des freins sociaux ou géographiques. - Il partage sa vision à travers des tribunes, des podcasts et des événements grand public. - Le programme repose sur 10 ans d’expérience de Ticket for Change auprès de 23 000 jeunes. - source_sentence: Quels sont les membres de l’équipe impliqués dans le projet ? sentences: - Une responsable de programme conçoit les contenus pédagogiques et accompagne les jeunes. - Il vise un impact durable grâce à l'exigence et à la qualité de ses actions. - Des rapports d'évaluation sont produits régulièrement pour analyser les résultats et identifier les axes d'amélioration. pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'Quels sont les membres de l’équipe impliqués dans le projet ?', 'Une responsable de programme conçoit les contenus pédagogiques et accompagne les jeunes.', "Il vise un impact durable grâce à l'exigence et à la qualité de ses actions.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 160 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 160 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 11 tokens</li><li>mean: 22.0 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 28.04 tokens</li><li>max: 46 tokens</li></ul> | * Samples: | sentence_0 | sentence_1 | |:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | <code>Quels sont les membres de l’équipe impliqués dans le projet ?</code> | <code>Une équipe de bénévoles apporte un soutien précieux au projet à chaque édition.</code> | | <code>Quelles sont les personnes impliquées et solicitées et leur statut ?</code> | <code>Des bénévoles soutiennent la logistique lors du séminaire du Ticket Camp.</code> | | <code>Quelles sont les personnes impliquées et solicitées et leur statut ?</code> | <code>Une responsable de programme conçoit les contenus pédagogiques et accompagne les jeunes.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 4 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Framework Versions - Python: 3.12.10 - Sentence Transformers: 4.1.0 - Transformers: 4.50.3 - PyTorch: 2.6.0+cpu - Accelerate: 1.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
amaj0003/ppo-LunarLander-v2
amaj0003
2025-06-04T15:39:49Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2025-06-04T15:39:27Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.41 +/- 23.34 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Sapna-shah-18o/Sapna.shah.Viral.Video.Link
Sapna-shah-18o
2025-06-04T15:39:20Z
0
0
null
[ "region:us" ]
null
2025-06-04T15:38:10Z
[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/)
amaurypllx/MNLP_M2_quantized_model_un_integrated_v3
amaurypllx
2025-06-04T15:38:41Z
0
0
null
[ "safetensors", "un_model", "8-bit", "bitsandbytes", "region:us" ]
null
2025-06-04T15:38:32Z
# UN Normalized Model (Integrated) Ce modèle applique **automatiquement** la normalisation UN pour les tâches de choix multiple. **Modèle de base :** amaurypllx/MNLP_M2_quantized_model ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer # ✅ Charge automatiquement avec normalisation UN ! model = AutoModelForCausalLM.from_pretrained("amaurypllx/MNLP_M2_quantized_model_un_integrated_v3", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("amaurypllx/MNLP_M2_quantized_model_un_integrated_v3") ``` ## Compatible avec lighteval Ajoutez `trust_remote_code=True` dans votre configuration : ```yaml model: base_params: model_args: "pretrained=amaurypllx/MNLP_M2_quantized_model_un_integrated_v3,revision=main,trust_remote_code=True" ``` ## Principe UN - **Score conditionnel** : P(réponse | question + contexte) - **Score inconditionnel** : P(réponse | sans contexte) - **Score normalisé** : conditionnel - inconditionnel Cela élimine les biais intrinsèques du modèle pour les choix multiples. ## Test du modèle ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("amaurypllx/MNLP_M2_quantized_model_un_integrated_v3", trust_remote_code=True) print(f"Model type: {type(model)}") # Devrait afficher UNModel print("✅ UN normalization is integrated!") ```
weifar/mistral_2
weifar
2025-06-04T15:37:44Z
0
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]
text-generation
2025-06-04T15:35:29Z
--- 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]
ibuki95/vision_172_9
ibuki95
2025-06-04T15:35:35Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2025-06-04T15:23:38Z
--- 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. 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]
Diamantis99/0ULDCIw
Diamantis99
2025-06-04T15:34:11Z
0
0
segmentation-models-pytorch
[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]
image-segmentation
2025-06-04T15:33:55Z
--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # PAN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "dpn131", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 32, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8615300059318542, "test_dataset_iou": 0.8782044053077698 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
Kiffy-Katrina-Lim-Video/FULL.VIDEO.Katrina.Lim.Kiffy.Viral.Video.Tutorial.Official
Kiffy-Katrina-Lim-Video
2025-06-04T15:34:01Z
0
0
null
[ "region:us" ]
null
2025-06-04T15:32:14Z
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komal-mahawar-18/komal.mahawar.viral.video.on.Social.Media
komal-mahawar-18
2025-06-04T15:33:46Z
0
0
null
[ "region:us" ]
null
2025-06-04T15:33:07Z
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komal-mahawar-video/wATCH.komal.mahawar.viral.video.original
komal-mahawar-video
2025-06-04T15:33:42Z
0
0
null
[ "region:us" ]
null
2025-06-04T15:32:46Z
[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/)
sarayut/opus-mt-en-ro-finetuned-en-to-de
sarayut
2025-06-04T15:33:37Z
2
0
transformers
[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-ro", "base_model:finetune:Helsinki-NLP/opus-mt-en-ro", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2025-06-04T03:13:41Z
--- library_name: transformers license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-ro tags: - generated_from_trainer model-index: - name: opus-mt-en-ro-finetuned-en-to-de 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. --> # opus-mt-en-ro-finetuned-en-to-de This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on an unknown dataset. ## 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: 16 - eval_batch_size: 16 - 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: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
appy1234/Llama-3.2-3B-Instruct-Int8DynamicActivationInt8WeightQuantized
appy1234
2025-06-04T15:32:21Z
0
0
transformers
[ "transformers", "pytorch", "llama", "feature-extraction", "torchao-my-repo", "facebook", "meta", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]
text-generation
2025-06-04T15:31:51Z
--- base_model: - meta-llama/Llama-3.2-3B-Instruct language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - torchao-my-repo - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://www.llama.com/llama3_2/use-policy), which is hereby incorporated by reference into this Agreement. 2. Additional Commercial Terms. If, on the Llama 3.2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 2. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta  2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement  4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # meta-llama/Llama-3.2-3B-Instruct (Quantized) ## Description This model is a quantized version of the original model [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct). It's quantized using the TorchAO library using the [torchao-my-repo](https://huggingface.co/spaces/pytorch/torchao-my-repo) space. ## Quantization Details - **Quantization Type**: Int8DynamicActivationInt8Weight - **Group Size**: None # 📄 Original Model Information ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. **Model Developer:** Meta **Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. | | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | | Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | **Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Sept 25, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). **Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-3B-Instruct, for use with `transformers` and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-3B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-3B-Instruct --include "original/*" --local-dir Llama-3.2-3B-Instruct ``` ## Hardware and Software **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) | | :---- | :---: | ----- | :---: | :---: | :---: | | Llama 3.2 1B | 370k | \- | 700 | 107 | 0 | | Llama 3.2 3B | 460k | \- | 700 | 133 | 0 | | Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 | | Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 | | Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 | | Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 | | Total | 833k | 86k | | 240 | 0 | \*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). **Data Freshness:** The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models | Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | ----- | ----- | :---: | :---: | :---: | :---: | :---: | | General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 | | | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 | | | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 | | Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 | | | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 | | | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 | | Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 | ### Instruction Tuned Models | Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B | | :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 | | Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 | | Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 | | Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 | | Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 | | | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 | | Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 | | | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 | | | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 | | Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 | | | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 | | Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 | | | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 | | | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 | | Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 | \*\*for comparison purposes only. Model not released. ### Multilingual Benchmarks | Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 | | | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 | | | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 | | | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 | | | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 | | | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 | | | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 | \*\*for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. | Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) | | :---- | ----- | ----- | ----- | ----- | ----- | | 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 | | 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) | | 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) | | 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 | | 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) | | 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) | (\*) The performance measurement is done using an adb binary-based approach. (\*\*) It is measured on an Android OnePlus 12 device. (\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64 *Footnote:* - *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.* - *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.* - *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better* - *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch* - *RSS size \- Memory usage in resident set size (RSS)* ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment **Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct **Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). **Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems **Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases **Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. **Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations **Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. **Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: **1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. **2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community **Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). **Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). **Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations **Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. **Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
Paulff123441/sofia1
Paulff123441
2025-06-04T15:31:02Z
0
0
null
[ "license:other", "region:us" ]
null
2025-06-04T14:21:29Z
--- 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 ---
sugarhater/en_difference_qwen3
sugarhater
2025-06-04T15:28:34Z
0
0
null
[ "safetensors", "qwen3", "llama-factory", "license:other", "region:us" ]
null
2025-06-04T15:21:49Z
--- license: other license_name: qwen3 license_link: LICENSE tags: - llama-factory ---
01-Spiderman-Sophie-Rain-Viral-Video/Sophie.Rain.Spiderman.Video.Leaks.Official
01-Spiderman-Sophie-Rain-Viral-Video
2025-06-04T15:28:18Z
0
0
null
[ "region:us" ]
null
2025-06-04T15:27:52Z
39 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
Snarcy/mit-b3_train_001
Snarcy
2025-06-04T15:27:32Z
3
0
transformers
[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]
null
2025-05-29T19:54:46Z
--- library_name: transformers license: other base_model: nvidia/mit-b3 tags: - generated_from_trainer model-index: - name: mit-b3_train_001 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. --> # mit-b3_train_001 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0037 - Mean Iou: 0.7221 - Mean Accuracy: 0.7654 - Overall Accuracy: 0.9990 - Per Category Iou: [0.999014926051339, 0.4451001558306277] - Per Category Accuracy: [0.9997123152015975, 0.5311090395140434] ## 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: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - 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: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------:|:-----------------------------------------:| | 0.0314 | 4.8780 | 400 | 0.0082 | 0.5961 | 0.5988 | 0.9988 | [0.9987736428850511, 0.19333640539440852] | [0.9999666836305313, 0.19766293779194483] | | 0.0263 | 9.7561 | 800 | 0.0048 | 0.7006 | 0.7569 | 0.9989 | [0.9988635621651706, 0.40234335940333743] | [0.999585918193621, 0.5142489124115395] | | 0.0211 | 14.6341 | 1200 | 0.0038 | 0.7235 | 0.7830 | 0.9990 | [0.9989621022354614, 0.44807472306981594] | [0.9996070645746006, 0.5663353782001163] | | 0.0187 | 19.5122 | 1600 | 0.0037 | 0.7221 | 0.7654 | 0.9990 | [0.999014926051339, 0.4451001558306277] | [0.9997123152015975, 0.5311090395140434] | ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
cragtmp/pair0.8-200
cragtmp
2025-06-04T15:27:30Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct", "region:us" ]
null
2025-06-04T15:26:21Z
--- base_model: meta-llama/Llama-3.2-11B-Vision-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
IntMeGroup/FineVQ_QA_which
IntMeGroup
2025-06-04T15:27:04Z
0
0
null
[ "tensorboard", "safetensors", "internvl_chat", "custom_code", "license:apache-2.0", "region:us" ]
null
2025-06-04T08:28:13Z
--- license: apache-2.0 ---
mradermacher/GrammarCoder-7B-Base-i1-GGUF
mradermacher
2025-06-04T15:25:00Z
0
0
transformers
[ "transformers", "gguf", "en", "base_model:qyliang/GrammarCoder-7B-Base", "base_model:quantized:qyliang/GrammarCoder-7B-Base", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
2025-06-04T12:23:57Z
--- base_model: qyliang/GrammarCoder-7B-Base language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/qyliang/GrammarCoder-7B-Base <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ1_M.gguf) | i1-IQ1_M | 2.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.6 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q4_0.gguf) | i1-Q4_0 | 4.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.6 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF/resolve/main/GrammarCoder-7B-Base.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/GrammarCoder-7B-Base-GGUF
mradermacher
2025-06-04T15:24:59Z
0
0
transformers
[ "transformers", "gguf", "en", "base_model:qyliang/GrammarCoder-7B-Base", "base_model:quantized:qyliang/GrammarCoder-7B-Base", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2025-06-04T08:41:20Z
--- base_model: qyliang/GrammarCoder-7B-Base language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/qyliang/GrammarCoder-7B-Base <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/GrammarCoder-7B-Base-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q2_K.gguf) | Q2_K | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q3_K_L.gguf) | Q3_K_L | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.IQ4_XS.gguf) | IQ4_XS | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q5_K_S.gguf) | Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q5_K_M.gguf) | Q5_K_M | 5.6 | | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q6_K.gguf) | Q6_K | 6.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/GrammarCoder-7B-Base-GGUF/resolve/main/GrammarCoder-7B-Base.f16.gguf) | f16 | 15.4 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Diamantis99/DGuDvVi
Diamantis99
2025-06-04T15:24:19Z
0
0
segmentation-models-pytorch
[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]
image-segmentation
2025-06-04T15:24:03Z
--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # PAN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "resnext101_32x8d", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 32, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8701464533805847, "test_dataset_iou": 0.885613203048706 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
Angelo-2009/Sylph
Angelo-2009
2025-06-04T15:23:26Z
0
1
null
[ "base_model:deepseek-ai/DeepSeek-R1-0528", "base_model:finetune:deepseek-ai/DeepSeek-R1-0528", "license:other", "region:us" ]
null
2025-06-04T15:20:27Z
--- license: other license_name: sylph-license. license_link: LICENSE metrics: - accuracy - character base_model: - deepseek-ai/DeepSeek-R1-0528-Qwen3-8B - deepseek-ai/DeepSeek-R1-0528 new_version: deepseek-ai/DeepSeek-R1-0528 ---
shuvankar77/sqlcoder1
shuvankar77
2025-06-04T15:22:15Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:defog/sqlcoder-7b-2", "base_model:adapter:defog/sqlcoder-7b-2", "region:us" ]
null
2025-06-04T15:21:38Z
--- base_model: defog/sqlcoder-7b-2 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
vectorzhou/vectorzhou-Qwen2-5-1-5B-Instruct-SFT-OpenHerm-ction-v0-1-OnlineIPO2-lora-0604063354-epoch-3
vectorzhou
2025-06-04T15:20:48Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "text-generation", "fine-tuned", "trl", "extra-gradient", "conversational", "dataset:OpenRLHF/prompt-collection-v0.1", "arxiv:2503.08942", "base_model:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT", "base_model:finetune:vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T15:20:43Z
--- base_model: vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT datasets: OpenRLHF/prompt-collection-v0.1 library_name: transformers model_name: Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-OnlineIPO2-lora tags: - generated_from_trainer - text-generation - fine-tuned - trl - extra-gradient licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT-prompt-collection-v0.1-OnlineIPO2-lora This model is a fine-tuned version of [vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT](https://huggingface.co/vectorzhou/Qwen2.5-1.5B-Instruct-SFT-OpenHermes-2.5-Standard-SFT) on the [OpenRLHF/prompt-collection-v0.1](https://huggingface.co/datasets/OpenRLHF/prompt-collection-v0.1) dataset. 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="vectorzhou/vectorzhou-Qwen2-5-1-5B-Instruct-SFT-OpenHerm-ction-v0-1-OnlineIPO2-lora-0604063354-epoch-3", 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/zhourunlongvector/nlhf/runs/kuktsgzu) This model was trained with Extragradient, a method introduced in [Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback](https://huggingface.co/papers/2503.08942). ### Framework versions - TRL: 0.13.0 - Transformers: 4.48.0 - Pytorch: 2.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite Extragradient as: ```bibtex @misc{zhou2025extragradientpreferenceoptimizationegpo, title={Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback}, author={Runlong Zhou and Maryam Fazel and Simon S. Du}, year={2025}, eprint={2503.08942}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.08942}, } ``` 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}} } ```
simon-donike/NIR-GAN
simon-donike
2025-06-04T15:20:08Z
0
0
null
[ "region:us" ]
null
2025-06-04T09:18:09Z
Check out the accompying GitHub Repository: https://github.com/simon-donike/NIR-GAN ![Sample Result](https://github.com/simon-donike/NIR-GAN/blob/master/resources/banner.png?raw=true)
loveh/my_awesome_model
loveh
2025-06-04T15:16:18Z
0
0
transformers
[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2025-06-04T14:46:45Z
--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model 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. --> # my_awesome_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2338 - Accuracy: 0.9316 ## 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: 16 - eval_batch_size: 16 - 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: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2218 | 1.0 | 1563 | 0.2052 | 0.9197 | | 0.1444 | 2.0 | 3126 | 0.2338 | 0.9316 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
abbasb91/q-FrozenLake-v1-4x4-noSlippery
abbasb91
2025-06-04T15:15:26Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2025-06-04T15:15:23Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="abbasb91/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Diamantis99/saUwkP5
Diamantis99
2025-06-04T15:15:23Z
0
0
segmentation-models-pytorch
[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]
image-segmentation
2025-06-04T15:15:08Z
--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # PAN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "resnet152", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 32, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8556963801383972, "test_dataset_iou": 0.8696557283401489 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
Snarcy/mit-b3_train_007
Snarcy
2025-06-04T15:14:32Z
1
0
transformers
[ "transformers", "safetensors", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b3", "base_model:finetune:nvidia/mit-b3", "license:other", "endpoints_compatible", "region:us" ]
null
2025-05-29T18:51:15Z
--- library_name: transformers license: other base_model: nvidia/mit-b3 tags: - generated_from_trainer model-index: - name: mit-b3_train_007 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. --> # mit-b3_train_007 This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0115 - Mean Iou: 0.8626 - Mean Accuracy: 0.8872 - Overall Accuracy: 0.9954 - Per Category Iou: [0.9953896324464471, 0.7298167694673549] - Per Category Accuracy: [0.9989917723091889, 0.7754706868592475] ## 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: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - 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: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------:|:-----------------------------------------:| | 0.0135 | 2.1277 | 400 | 0.0311 | 0.5304 | 0.5379 | 0.9853 | [0.9852410663584575, 0.07549519670474943] | [0.9999154549727405, 0.07589121292911524] | | 0.0087 | 4.2553 | 800 | 0.0233 | 0.7779 | 0.8172 | 0.9922 | [0.9921058976412029, 0.5637167086130435] | [0.9979174059096022, 0.6365569084462523] | | 0.0071 | 6.3830 | 1200 | 0.0187 | 0.8386 | 0.9336 | 0.9936 | [0.9935121002770297, 0.6836159227361409] | [0.9955685293152186, 0.8715760107742627] | | 0.0103 | 8.5106 | 1600 | 0.0143 | 0.8479 | 0.8840 | 0.9948 | [0.9947328108146196, 0.7011343132577792] | [0.9984279763637177, 0.7695201055437979] | | 0.007 | 10.6383 | 2000 | 0.0133 | 0.8552 | 0.9147 | 0.9948 | [0.9946870722793868, 0.7156858341174634] | [0.9973799679893965, 0.8320273204518593] | | 0.0086 | 12.7660 | 2400 | 0.0129 | 0.8644 | 0.9276 | 0.9951 | [0.9949982307568621, 0.733894775950065] | [0.9972781019741054, 0.8578347030206415] | | 0.0122 | 14.8936 | 2800 | 0.0116 | 0.8670 | 0.9080 | 0.9954 | [0.9953510606761393, 0.7386602340952769] | [0.9982762294468315, 0.8176607206662452] | | 0.0087 | 17.0213 | 3200 | 0.0111 | 0.8757 | 0.9214 | 0.9957 | [0.9956107700746692, 0.7557371203737858] | [0.9981031081649189, 0.8446815820575544] | | 0.0079 | 19.1489 | 3600 | 0.0115 | 0.8626 | 0.8872 | 0.9954 | [0.9953896324464471, 0.7298167694673549] | [0.9989917723091889, 0.7754706868592475] | ### Framework versions - Transformers 4.52.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
jbreuch/ultrafeedback-persuasive-model
jbreuch
2025-06-04T15:12:27Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T13:56:33Z
--- 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]
NFX74/SFT-STEM-Qwen3-0.6B
NFX74
2025-06-04T15:12:07Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T15:10:43Z
--- 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]
yassineturki/qlora_qwen_1100
yassineturki
2025-06-04T15:11:31Z
0
0
peft
[ "peft", "safetensors", "qwen3", "arxiv:1910.09700", "base_model:HAissa/think_no_think_mmlu_35000", "base_model:adapter:HAissa/think_no_think_mmlu_35000", "4-bit", "bitsandbytes", "region:us" ]
null
2025-06-04T11:07:07Z
--- base_model: HAissa/think_no_think_mmlu_35000 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
str20tbl/summarise_cy
str20tbl
2025-06-04T15:08:53Z
5
0
transformers
[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:generator", "base_model:google-t5/t5-large", "base_model:finetune:google-t5/t5-large", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2025-06-03T15:15:43Z
--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-large tags: - generated_from_trainer datasets: - generator metrics: - rouge model-index: - name: summarise_cy results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: generator type: generator config: default split: train args: default metrics: - name: Rouge1 type: rouge value: 0.1434 --- <!-- 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. --> # summarise_cy This model is a fine-tuned version of [google-t5/t5-large](https://huggingface.co/google-t5/t5-large) on the generator dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.1434 - Rouge2: 0.0535 - Rougel: 0.1286 - Rougelsum: 0.1287 - Gen Len: 20.0 ## 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 410 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 2.0 | 820 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 3.0 | 1230 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 4.0 | 1640 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 5.0 | 2050 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 6.0 | 2460 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 7.0 | 2870 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 8.0 | 3280 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 9.0 | 3690 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | | 0.0 | 10.0 | 4100 | nan | 0.1434 | 0.0535 | 0.1286 | 0.1287 | 20.0 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
George067/dqn-SpaceInvadersNoFrameskip-v4
George067
2025-06-04T15:07:44Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2025-06-04T15:07:14Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 447.00 +/- 57.15 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga George067 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga George067 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga George067 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 1e-05), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Kewayik/ppo-LunarLander-v2
Kewayik
2025-06-04T15:07:28Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2025-06-04T15:07:06Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 279.91 +/- 17.43 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
outlookAi/RhYl0Ii1Km
outlookAi
2025-06-04T15:06:01Z
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
2025-06-04T14:46:47Z
--- 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: Fan sugar --- # Rhyl0Ii1Km <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 `Fan sugar ` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Fan sugar ", "lora_weights": "https://huggingface.co/outlookAi/RhYl0Ii1Km/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('outlookAi/RhYl0Ii1Km', weight_name='lora.safetensors') image = pipeline('Fan sugar ').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: 1500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/outlookAi/RhYl0Ii1Km/discussions) to add images that show off what you’ve made with this LoRA.
CAMeL-Lab/text-editing-zaebuc-nopnx
CAMeL-Lab
2025-06-04T15:04:42Z
0
1
null
[ "pytorch", "bert", "token-classification", "ar", "arxiv:2503.00985", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "license:mit", "region:us" ]
token-classification
2025-06-04T09:07:09Z
--- license: mit language: - ar base_model: - aubmindlab/bert-base-arabertv02 pipeline_tag: token-classification --- # SWEET<sub>NoPnx</sub> ZAEBUC Model ## Model Description `CAMeL-Lab/text-editing-zaebuc-pnx` is a text editing model tailored for grammatical error correction (GEC) in Modern Standard Arabic (MSA). The model is based on [AraBERTv02](https://huggingface.co/aubmindlab/bert-base-arabertv02), which we fine-tuned using the [ZAEBUC](https://sites.google.com/view/zaebuc/home) dataset. This model was introduced in our ACL 2025 paper, [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://arxiv.org/abs/2503.00985), where we refer to it as SWEET (Subword Edit Error Tagger). The model was fine-tuned to fix non-punctuation (i.e., NoPnx) errors. Details about the training procedure, data preprocessing, and hyperparameters are available in the paper. The fine-tuning code and associated resources are publicly available on our GitHub repository: https://github.com/CAMeL-Lab/text-editing. ## Intended uses To use the `CAMeL-Lab/text-editing-zaebuc-nopnx` model, you must clone our text editing [GitHub repository](https://github.com/CAMeL-Lab/text-editing) and follow the installation requirements. We used this SWEET<sub>NoPnx</sub> model to report results on the ZAEBUC dev and test sets in our [paper](https://arxiv.org/abs/2503.00985). This model is intended to be used with SWEET<sub>Pnx</sub> ([`CAMeL-Lab/text-editing-zaebuc-pnx`](https://huggingface.co/CAMeL-Lab/text-editing-zaebuc-pnx)) model. ## How to use Clone our text editing [GitHub repository](https://github.com/CAMeL-Lab/text-editing) and follow the installation requirements ```python from transformers import BertTokenizer, BertForTokenClassification import torch import torch.nn.functional as F from gec.tag import rewrite nopnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-zaebuc-nopnx') nopnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-zaebuc-nopnx') pnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-zaebuc-pnx') pnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-zaebuc-pnx') def predict(model, tokenizer, text, decode_iter=1): for _ in range(decode_iter): tokenized_text = tokenizer(text, return_tensors="pt", is_split_into_words=True) with torch.no_grad(): logits = model(**tokenized_text).logits preds = F.softmax(logits.squeeze(), dim=-1) preds = torch.argmax(preds, dim=-1).cpu().numpy() edits = [model.config.id2label[p] for p in preds[1:-1]] assert len(edits) == len(tokenized_text['input_ids'][0][1:-1]) subwords = tokenizer.convert_ids_to_tokens(tokenized_text['input_ids'][0][1:-1]) text = rewrite(subwords=[subwords], edits=[edits])[0][0] return text text = 'يجب الإهتمام ب الصحه و لا سيما ف ي الصحه النفسيه ياشباب المستقبل،،'.split() output_sent = predict(nopnx_model, nopnx_tokenizer, text, decode_iter=2) output_sent = predict(pnx_model, pnx_tokenizer, output_sent.split(), decode_iter=1) print(output_sent) # يجب الاهتمام بالصحة ولا سيما في الصحة النفسية يا شباب المستقبل . ``` ## Citation ```bibtex @inter{alhafni-habash-2025-enhancing, title={Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study}, author={Bashar Alhafni and Nizar Habash}, year={2025}, eprint={2503.00985}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.00985}, } ```
erikbranmarino/CT-BERT-PRCT
erikbranmarino
2025-06-04T15:02:18Z
4
0
null
[ "safetensors", "bert", "conspiracy-detection", "content-moderation", "prct", "social-media", "misinformation", "hate-speech", "cross-platform", "multilingual-classification", "text-classification", "multilingual", "dataset:custom", "license:mit", "region:us" ]
text-classification
2025-02-14T11:48:46Z
--- language: multilingual tags: - conspiracy-detection - content-moderation - bert - prct - social-media - misinformation - hate-speech - cross-platform - multilingual-classification license: mit datasets: - custom metrics: - accuracy - f1 - precision - recall widget: - text: "Immigration is necessary for economic growth and demographic balance." example_title: "Non-PRCT Example" - text: "They are deliberately replacing us with foreigners to change voting patterns." example_title: "PRCT Example" pipeline_tag: text-classification --- <div align="center"> # 🔍 CT-BERT-PRCT **A specialized BERT model for detection of Population Replacement Conspiracy Theory content** [![Model on Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-CT--BERT--PRCT-blue)](https://huggingface.co/erikbranmarino/CT-BERT-PRCT) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) </div> ## Overview <table> <tr> <td width="240"> <img src="https://huggingface.co/front/assets/huggingface_logo.svg" width="240" /> </td> <td> <b>CT-BERT-PRCT</b> is a fine-tuned BERT model for detecting Population Replacement Conspiracy Theories across multiple platforms and languages. <br/><br/> <b>Key metrics:</b> <ul> <li>YouTube English Accuracy: 83.8%</li> <li>Telegram Portuguese Accuracy: 71.9%</li> <li>Cross-platform F1: 71.2%</li> </ul> </td> </tr> </table> ## Model description CT-BERT-PRCT is a fine-tuned version of CT-BERT specifically adapted for detecting Population Replacement Conspiracy Theory (PRCT) content across social media platforms. The model has been trained to identify both explicit and implicit PRCT narratives while maintaining robust cross-platform generalization capabilities. Population Replacement Conspiracy Theories are a family of conspiracy theories that state that there is a hidden plan to replace native population with migrants. The main versions and variants of such narratives are: The Great Replacement Theory, Eurabia, Kalergi Plan and White Genocide Conspiracy Theory. ## Model Configuration ### Label Mapping - 0: Non-PRCT content - 1: PRCT content ### Model Architecture - Base model: CT-BERT - Hidden layers: 12 - Attention heads: 12 - Parameters: 110M ### Input Requirements - Maximum sequence length: 512 tokens - Input type: Text (strings) - Preprocessing: Standard BERT tokenization ## Intended uses & limitations ### Intended uses - Content moderation for social media platforms - Research on conspiracy theory propagation - Cross-platform conspiracy content detection - Multilingual PRCT detection ### Limitations - Performance may vary across different social media platforms - May require periodic fine-tuning to adapt to evolving narratives - Should be used as part of a broader content moderation strategy - Best performance on YouTube content, with some performance degradation on other platforms ## Training and evaluation data The model was fine-tuned on a dataset of 56,085 YouTube comments and evaluated using: - A manually annotated gold standard of 500 YouTube comments - A cross-platform test set of 160 Telegram messages in multiple languages (Spanish and Portuguese) ## Training procedure The model was fine-tuned using: - Learning rate: 2e-5 - Batch size: 32 - Maximum epochs: 6 - Early stopping based on validation performance - Base model: CT-BERT (pre-trained on COVID-19 conspiracy content) ## Results Detailed performance metrics: ### YouTube Dataset - Accuracy: 83.8% - Precision: 86.5% - Recall: 83.3% - F1-score: 83.3% ### Telegram Dataset (Cross-platform and multilingual) - Accuracy: 71.9% - Precision: 74.2% - Recall: 71.9% - F1-score: 71.2% The model demonstrates strong performance on its primary training domain (YouTube - English) while maintaining reasonable effectiveness in cross-platform and multilingual scenarios (Telegram - Portuguese and Spanish), showing good generalization capabilities across different social media environments. ## Example Predictions Here are some example texts and how the model classifies them: | Example Text | Prediction | Confidence | |-------------|------------|------------| | "Immigration policies should be decided based on economic needs and humanitarian considerations." | Non-PRCT | 0.96 | | "We need more controlled immigration to match our labor market demands." | Non-PRCT | 0.92 | | "European countries must protect their cultural identity while respecting diverse backgrounds." | Non-PRCT | 0.78 | | "Politicians are secretly working to change our demographics." | PRCT | 0.85 | | "They're bringing in foreigners to replace native voters." | PRCT | 0.94 | | "The elites have a plan to erase our culture through mass immigration." | PRCT | 0.97 | *Note: These examples are simplified for illustration. The model evaluates nuanced content in context.* ## Online Demo Try the model directly in your browser using the Hugging Face Inference API: 1. Go to the [model page](https://huggingface.co/erikbranmarino/CT-BERT-PRCT) 2. Navigate to the "Inference API" tab 3. Type or paste text into the input field 4. Click "Compute" to see the model's prediction You can also integrate the API into your applications using the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index). ## Example Usage ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer import torch # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("erikbranmarino/CT-BERT-PRCT") model = AutoModelForSequenceClassification.from_pretrained("erikbranmarino/CT-BERT-PRCT") # Prepare your text text = "Your text here" inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512) # Make prediction with torch.no_grad(): outputs = model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) # Get predicted class (0: Non-PRCT, 1: PRCT) predicted_class = predictions.argmax().item() confidence = predictions[0][predicted_class].item() print(f"Class: {'PRCT' if predicted_class == 1 else 'Non-PRCT'}") print(f"Confidence: {confidence:.2f}") ``` ## Complete Example with Batch Processing ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer from torch.utils.data import Dataset, DataLoader class TextDataset(Dataset): def __init__(self, texts, tokenizer, max_length=512): self.encodings = tokenizer(texts, truncation=True, padding=True, max_length=max_length) def __getitem__(self, idx): return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} def __len__(self): return len(self.encodings.input_ids) def predict_batch(texts, model, tokenizer, batch_size=16): # Prepare dataset and dataloader dataset = TextDataset(texts, tokenizer) dataloader = DataLoader(dataset, batch_size=batch_size) predictions = [] model.eval() with torch.no_grad(): for batch in dataloader: outputs = model(**batch) probs = torch.nn.functional.softmax(outputs.logits, dim=-1) predictions.extend(probs.cpu().numpy()) return predictions # Example usage texts = ["text1", "text2", "text3"] # Your list of texts results = predict_batch(texts, model, tokenizer) for text, pred in zip(texts, results): predicted_class = pred.argmax() confidence = pred[predicted_class] print(f"Text: {text[:50]}...") print(f"Class: {'PRCT' if predicted_class == 1 else 'Non-PRCT'}") print(f"Confidence: {confidence:.2f}\n") ``` ## Bias and limitations This model is intended for research and content moderation purposes. It should be used as part of a broader content moderation strategy and not as a sole decision-maker for content removal. The model may exhibit: - Platform-specific biases due to training data source - Language-specific performance variations - Sensitivity to evolving conspiracy narratives ## Citation If you use this model, please cite: ``` @article{marino2025one, title={One Model to Detect Them All? Comparing LLMs, BERT and Traditional ML in Cross-Platform Conspiracy Detection}, author={Marino, Erik Bran and Vieira, Renata and Bassi, Davide}, year={2025} } ``` ## Research Purpose Model - Developed for academic research on conspiracy theory detection - Part of MSCA PhD thesis at Universidade de Évora (HYBRIDS Project) - Intended for scientific research and educational purposes - Not designed for commercial deployment ## Contact Erik Bran Marino ([email protected]) ```
jinx2321/korean-tagged-1e4-paper-reset
jinx2321
2025-06-04T15:02:06Z
0
0
transformers
[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:everdoubling/byt5-Korean-small", "base_model:finetune:everdoubling/byt5-Korean-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2025-06-04T10:31:12Z
--- library_name: transformers license: apache-2.0 base_model: everdoubling/byt5-Korean-small tags: - generated_from_trainer model-index: - name: korean-tagged-1e4-paper-reset 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. --> # korean-tagged-1e4-paper-reset This model is a fine-tuned version of [everdoubling/byt5-Korean-small](https://huggingface.co/everdoubling/byt5-Korean-small) on the None dataset. ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
yamasora/ppo-LunarLander-v2
yamasora
2025-06-04T14:59:58Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2025-06-04T14:59:39Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 224.61 +/- 72.80 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
CAMeL-Lab/text-editing-qalb14-pnx
CAMeL-Lab
2025-06-04T14:59:51Z
0
1
null
[ "pytorch", "bert", "token-classification", "ar", "arxiv:2503.00985", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "license:mit", "region:us" ]
token-classification
2025-06-04T08:33:12Z
--- license: mit language: - ar base_model: - aubmindlab/bert-base-arabertv02 pipeline_tag: token-classification --- # SWEET<sub>Pnx</sub> QALB-2014 Model ## Model Description `CAMeL-Lab/text-editing-qalb14-pnx` is a text editing model tailored for grammatical error correction (GEC) in Modern Standard Arabic (MSA). The model is based on [AraBERTv02](https://huggingface.co/aubmindlab/bert-base-arabertv02), which we fine-tuned using the [QALB-2014](https://camel.abudhabi.nyu.edu/qalb-shared-task-2015/) dataset. This model was introduced in our ACL 2025 paper, [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://arxiv.org/abs/2503.00985), where we refer to it as SWEET (Subword Edit Error Tagger). The model was fine-tuned to fix punctuation (i.e., Pnx) errors. Details about the training procedure, data preprocessing, and hyperparameters are available in the paper. The fine-tuning code and associated resources are publicly available on our GitHub repository: https://github.com/CAMeL-Lab/text-editing. ## Intended uses To use the `CAMeL-Lab/text-editing-qalb14-pnx` model, you must clone our text editing [GitHub repository](https://github.com/CAMeL-Lab/text-editing) and follow the installation requirements. We used this SWEET<sub>Pnx</sub> model to report results on the QALB-2014 dev and test sets in our [paper](https://arxiv.org/abs/2503.00985). This model is intended to be used with SWEET<sub>NoPnx</sub> ([`CAMeL-Lab/text-editing-qalb14-nopnx`](https://huggingface.co/CAMeL-Lab/text-editing-qalb14-nopnx)) model. ## How to use Clone our text editing [GitHub repository](https://github.com/CAMeL-Lab/text-editing) and follow the installation requirements ```python from transformers import BertTokenizer, BertForTokenClassification import torch import torch.nn.functional as F from gec.tag import rewrite nopnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-qalb14-nopnx') nopnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-qalb14-nopnx') pnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-qalb14-pnx') pnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-qalb14-pnx') def predict(model, tokenizer, text, decode_iter=1): for _ in range(decode_iter): tokenized_text = tokenizer(text, return_tensors="pt", is_split_into_words=True) with torch.no_grad(): logits = model(**tokenized_text).logits preds = F.softmax(logits.squeeze(), dim=-1) preds = torch.argmax(preds, dim=-1).cpu().numpy() edits = [model.config.id2label[p] for p in preds[1:-1]] assert len(edits) == len(tokenized_text['input_ids'][0][1:-1]) subwords = tokenizer.convert_ids_to_tokens(tokenized_text['input_ids'][0][1:-1]) text = rewrite(subwords=[subwords], edits=[edits])[0][0] return text text = 'يجب الإهتمام ب الصحه و لا سيما ف ي الصحه النفسيه ياشباب المستقبل،،'.split() output_sent = predict(nopnx_model, nopnx_tokenizer, text, decode_iter=2) output_sent = predict(pnx_model, pnx_tokenizer, output_sent.split(), decode_iter=1) print(output_sent) # يجب الاهتمام بالصحة ولا سيما في الصحة النفسية يا شباب المستقبل . ``` ## Citation ```bibtex @inter{alhafni-habash-2025-enhancing, title={Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study}, author={Bashar Alhafni and Nizar Habash}, year={2025}, eprint={2503.00985}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.00985}, } ```
jbreuch/ultrafeedback-authoritative-model
jbreuch
2025-06-04T14:56:52Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:08:56Z
--- 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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LeonGuertler/Qwen3-4B-batch-4-experiment-0-step_000200
LeonGuertler
2025-06-04T14:56:21Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:47:25Z
--- 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]
LeonGuertler/Qwen3-4B-batch-4-experiment-8-step_000175
LeonGuertler
2025-06-04T14:56:20Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:47:24Z
--- 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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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]
valorja/Qwen2.5-7B-Instruct-Gensyn-Swarm-stubby_freckled_opossum
valorja
2025-06-04T14:56:04Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am stubby freckled opossum", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-7B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]
null
2025-06-02T22:57:55Z
--- base_model: Gensyn/Qwen2.5-7B-Instruct library_name: transformers model_name: Qwen2.5-7B-Instruct-Gensyn-Swarm-stubby_freckled_opossum tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am stubby freckled opossum - unsloth - trl licence: license --- # Model Card for Qwen2.5-7B-Instruct-Gensyn-Swarm-stubby_freckled_opossum This model is a fine-tuned version of [Gensyn/Qwen2.5-7B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-7B-Instruct). 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="valorja/Qwen2.5-7B-Instruct-Gensyn-Swarm-stubby_freckled_opossum", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` 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}} } ```
LeonGuertler/Qwen3-4B-batch-4-experiment-16-step_000175
LeonGuertler
2025-06-04T14:55:48Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:46:57Z
--- 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]
shulijia/MNLP_M3_mcqa_model_base_m1_eval_prompt
shulijia
2025-06-04T14:54:43Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:34:03Z
--- base_model: Qwen/Qwen3-0.6B-Base library_name: transformers model_name: MNLP_M3_mcqa_model_base_m1_eval_prompt tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for MNLP_M3_mcqa_model_base_m1_eval_prompt This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base). 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="shulijia/MNLP_M3_mcqa_model_base_m1_eval_prompt", 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: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.0 ## 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}} } ```
MarcosGutierrez/StorytellerTest
MarcosGutierrez
2025-06-04T14:54:21Z
0
0
null
[ "safetensors", "gemma3", "dataset:suolyer/pile_bookcorpus2", "base_model:google/gemma-3-4b-it", "base_model:finetune:google/gemma-3-4b-it", "license:mit", "region:us" ]
null
2025-05-31T13:56:57Z
--- license: mit datasets: - suolyer/pile_bookcorpus2 base_model: - google/gemma-3-4b-it --- # Project concept The concept is generate a model that can create stories in a more profesional way. This is only a testing for study purpose, is my first fine-tuning.
Bea-Taylor/objection_fine_tuned_4
Bea-Taylor
2025-06-04T14:54:12Z
0
0
sentence-transformers
[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:180000", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2025-06-04T14:54:05Z
--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:180000 - loss:CosineSimilarityLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: The alternative scheme by SNB addresses the noise and overheating issues by using passive design strategies, including Passivhaus principles for orientation and proposing alternative building typologies in its design; the inclusion of maisonettes along the railway as suggested in the SNB proposal means that there would be no habitable rooms facing the tracks. sentences: - This would constitute over development of the residential site with loss of privacy for neighbours, a strain on Victorian infrastructure, local facilities and more congestion for Gordon Road. - The local traffic is appalling as it is and this is just going to add to the ongoing misery of the local area with the crumbling roads. - In the consultation in June, shown here, they said the play area was going to be communal, but that is not mentioned in the planning application at all. - source_sentence: more traffic congestion at the junction of Blackfriars Rd and Stamford street. This is already causing backups along Stamford Street because of the complex arrangement at the junction sentences: - The dwellings proposed a large 3-bedroom units aiming for the upper end of the residential market suggesting at least a fair proportion of 2-car families. The resultant overflow parking from the development and visitors to the proposed dwellings is therefore likely to exacerbate the shortage of on-street parking on Longstone Avenue. - Huge development which effects the loss of light and greenery for the residents of hanger hill area. - object because 6 units would attract multiple more vehicles, putting undue pressure on the parking spaces available, and because 6 units would create greater noise and traffic. - source_sentence: No affordable housing sentences: - '- Increased traffic:' - Insufficient outdoor amenity space. - Local amenities are already under strain where public transport can be crowded, particularly at peak times and it can be difficult to find a dentist, doctor etc. - source_sentence: The transport impact of the development is a matter for prior approval. sentences: - This is an objection to mass over development of our local area. - is long and narrow - less than 2 metres wide. The entire back of the room has no daylight and access to the room is via a narrow dark corridor. The space for a shower seems inadequate. Room 3 is adjacent to a Communal kitchen/dining area. This seems unacceptable in terms of noise, odours and foot traffic. - It's bad enough the traffic on station road, and will get worse when you adding more vehicles. - source_sentence: It will also reduce/limit/block east facing views of Canary Wharf for Swedish Quays residents next door. In turn, this will have a knock on effect on the value of our property because views of Canary Wharf are sought after by potential buyers. sentences: - A 4th floor is out of character, Victoria Coutt is already the tallest building in the neighbourhood. - My health has really suffered over the last two years during which, I have had 3 heart attacks and have recently been diagnosed with anaemia and emphysema as well as having stents surgically placed in my arteries. I am on twelve tablets a day for my health and this whole subject is creating all my health conditions to worsen. All I can do is emphasise my objections and hope that Barnet Council decline building permission. - I support the planning application for the proposed development of the roof space. This project is a vital step toward easing the financial burden on residents and addressing ongoing concerns effectively. Additionally, it brings the added benefit of a positive environmental impact, contributing to a more sustainable and responsible community. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.9829396265423462 name: Pearson Cosine - type: spearman_cosine value: 0.9159088700437812 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity <!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Bea-Taylor/objection_fine_tuned_4") # Run inference sentences = [ 'It will also reduce/limit/block east facing views of Canary Wharf for Swedish Quays residents next door. In turn, this will have a knock on effect on the value of our property because views of Canary Wharf are sought after by potential buyers.', 'I support the planning application for the proposed development of the roof space. This project is a vital step toward easing the financial burden on residents and addressing ongoing concerns effectively. Additionally, it brings the added benefit of a positive environmental impact, contributing to a more sustainable and responsible community.', 'My health has really suffered over the last two years during which, I have had 3 heart attacks and have recently been diagnosed with anaemia and emphysema as well as having stents surgically placed in my arteries. I am on twelve tablets a day for my health and this whole subject is creating all my health conditions to worsen. All I can do is emphasise my objections and hope that Barnet Council decline building permission.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.9829 | | **spearman_cosine** | **0.9159** | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 180,000 training samples * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: | | text1 | text2 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 3 tokens</li><li>mean: 50.78 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 50.72 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.36</li><li>max: 1.0</li></ul> | * Samples: | text1 | text2 | label | |:------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------| | <code>Loss of Daylight and Sunlight</code> | <code>Fifthly, the increased height of the building with an additional storey will further reduce the available sunlight hours in my first floor flat leading to increased heating costs as well as a reduction in my quality of life. As the sun barely gets above the level of the existing building in the winter months it is likely I will spend much of the winter with the rear of my flat continually in shadow.</code> | <code>0.75</code> | | <code>The existing carpark has a maximum of 16 car parking spaces for the 37 flats, used on a first come first served basis.</code> | <code>As the other comments on this application state, the building works appear to be complete and the application does not appear to demonstrate the scale of the work or the reality of the build.</code> | <code>0.0</code> | | <code>Are you proposing to connect to the existing drainage system?</code> | <code>The design of the types of buildings being proposed is out of character with the area. I object to the removal of the existing footbridge... this is unacceptable and will cut the area of Victoria Park off from Cromer road - a currently safe route which pedestrians and school children use to access without having to cross road and the promise of new access or pedestrian routes will mean walking public pavements around New Barnet via Station Road. I am not convinced by the developers that an alternative safer route will be provided as they will say anything to obtain planning.</code> | <code>0.0</code> | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 20,000 evaluation samples * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: | | text1 | text2 | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 3 tokens</li><li>mean: 49.71 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 51.7 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.33</li><li>max: 1.0</li></ul> | * Samples: | text1 | text2 | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------| | <code>1. Significant noise and disruption for local residents of rainbow quay and surrounding developments - Including over looking Princes Court and blocking already limited light.</code> | <code>I object to the developer changing the goal posts in order to achieve more profit as it is not the families who need social housing who will benefit or the young people trying to get on the property ladder and can't but the developer who profits from those who can "afford" to pay the "high prices" of Barnet accommodation.</code> | <code>0.0</code> | | <code>Congestion on Camlet way and beach hill , roads which already have traffic issues!</code> | <code>TRAFFIC AND PARKING - Granville Road has limited off road parking and is a busy & important thoroughfare from Ballards Lane, to High Road North Finchley & Summers Lane, linking Finchley & Friern Barnet. The addition of two further flats without parking provision would increase the pressure for parking spaces.</code> | <code>0.75</code> | | <code>It will also obstruct light to my property and garden.</code> | <code>- Health and safety: concern for disruption building works will cause, damage to local infrastructure, increased traffic</code> | <code>0.0</code> | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | |:------:|:-----:|:-------------:|:---------------:|:-----------------------:| | -1 | -1 | - | - | 0.3501 | | 0.0089 | 100 | 0.1082 | 0.1113 | 0.3928 | | 0.0178 | 200 | 0.1088 | 0.1000 | 0.4880 | | 0.0267 | 300 | 0.0931 | 0.0857 | 0.5950 | | 0.0356 | 400 | 0.0765 | 0.0749 | 0.6603 | | 0.0444 | 500 | 0.0725 | 0.0685 | 0.6929 | | 0.0533 | 600 | 0.066 | 0.0622 | 0.7216 | | 0.0622 | 700 | 0.0568 | 0.0558 | 0.7506 | | 0.0711 | 800 | 0.0525 | 0.0498 | 0.7749 | | 0.08 | 900 | 0.048 | 0.0453 | 0.7926 | | 0.0889 | 1000 | 0.0438 | 0.0412 | 0.8091 | | 0.0978 | 1100 | 0.0447 | 0.0375 | 0.8239 | | 0.1067 | 1200 | 0.0391 | 0.0336 | 0.8357 | | 0.1156 | 1300 | 0.0359 | 0.0305 | 0.8486 | | 0.1244 | 1400 | 0.0307 | 0.0272 | 0.8565 | | 0.1333 | 1500 | 0.0289 | 0.0251 | 0.8621 | | 0.1422 | 1600 | 0.0256 | 0.0233 | 0.8667 | | 0.1511 | 1700 | 0.0285 | 0.0221 | 0.8702 | | 0.16 | 1800 | 0.0229 | 0.0206 | 0.8743 | | 0.1689 | 1900 | 0.0228 | 0.0193 | 0.8781 | | 0.1778 | 2000 | 0.022 | 0.0182 | 0.8814 | | 0.1867 | 2100 | 0.0197 | 0.0173 | 0.8827 | | 0.1956 | 2200 | 0.0185 | 0.0167 | 0.8837 | | 0.2044 | 2300 | 0.0202 | 0.0162 | 0.8850 | | 0.2133 | 2400 | 0.0178 | 0.0151 | 0.8883 | | 0.2222 | 2500 | 0.0174 | 0.0147 | 0.8896 | | 0.2311 | 2600 | 0.0171 | 0.0146 | 0.8891 | | 0.24 | 2700 | 0.0163 | 0.0136 | 0.8921 | | 0.2489 | 2800 | 0.0147 | 0.0131 | 0.8934 | | 0.2578 | 2900 | 0.0149 | 0.0129 | 0.8953 | | 0.2667 | 3000 | 0.0152 | 0.0122 | 0.8966 | | 0.2756 | 3100 | 0.0138 | 0.0120 | 0.8969 | | 0.2844 | 3200 | 0.0128 | 0.0114 | 0.8977 | | 0.2933 | 3300 | 0.0128 | 0.0111 | 0.8991 | | 0.3022 | 3400 | 0.0117 | 0.0106 | 0.9005 | | 0.3111 | 3500 | 0.0126 | 0.0104 | 0.9009 | | 0.32 | 3600 | 0.0118 | 0.0102 | 0.9020 | | 0.3289 | 3700 | 0.0115 | 0.0100 | 0.9016 | | 0.3378 | 3800 | 0.0115 | 0.0098 | 0.9019 | | 0.3467 | 3900 | 0.0116 | 0.0092 | 0.9035 | | 0.3556 | 4000 | 0.0113 | 0.0090 | 0.9042 | | 0.3644 | 4100 | 0.0117 | 0.0090 | 0.9043 | | 0.3733 | 4200 | 0.0097 | 0.0084 | 0.9054 | | 0.3822 | 4300 | 0.0098 | 0.0087 | 0.9052 | | 0.3911 | 4400 | 0.0098 | 0.0085 | 0.9054 | | 0.4 | 4500 | 0.0097 | 0.0084 | 0.9056 | | 0.4089 | 4600 | 0.0097 | 0.0082 | 0.9057 | | 0.4178 | 4700 | 0.0102 | 0.0080 | 0.9066 | | 0.4267 | 4800 | 0.0086 | 0.0079 | 0.9071 | | 0.4356 | 4900 | 0.0085 | 0.0078 | 0.9070 | | 0.4444 | 5000 | 0.009 | 0.0076 | 0.9080 | | 0.4533 | 5100 | 0.0091 | 0.0073 | 0.9085 | | 0.4622 | 5200 | 0.0084 | 0.0073 | 0.9085 | | 0.4711 | 5300 | 0.0082 | 0.0071 | 0.9089 | | 0.48 | 5400 | 0.0073 | 0.0070 | 0.9089 | | 0.4889 | 5500 | 0.0096 | 0.0069 | 0.9098 | | 0.4978 | 5600 | 0.007 | 0.0068 | 0.9097 | | 0.5067 | 5700 | 0.0078 | 0.0070 | 0.9096 | | 0.5156 | 5800 | 0.0079 | 0.0067 | 0.9102 | | 0.5244 | 5900 | 0.0097 | 0.0067 | 0.9107 | | 0.5333 | 6000 | 0.0077 | 0.0065 | 0.9110 | | 0.5422 | 6100 | 0.0084 | 0.0065 | 0.9112 | | 0.5511 | 6200 | 0.007 | 0.0063 | 0.9113 | | 0.56 | 6300 | 0.0073 | 0.0062 | 0.9117 | | 0.5689 | 6400 | 0.0078 | 0.0066 | 0.9107 | | 0.5778 | 6500 | 0.0082 | 0.0062 | 0.9116 | | 0.5867 | 6600 | 0.0066 | 0.0061 | 0.9119 | | 0.5956 | 6700 | 0.0076 | 0.0060 | 0.9122 | | 0.6044 | 6800 | 0.0076 | 0.0060 | 0.9120 | | 0.6133 | 6900 | 0.0075 | 0.0059 | 0.9123 | | 0.6222 | 7000 | 0.0071 | 0.0059 | 0.9126 | | 0.6311 | 7100 | 0.0076 | 0.0057 | 0.9130 | | 0.64 | 7200 | 0.0067 | 0.0056 | 0.9131 | | 0.6489 | 7300 | 0.0069 | 0.0057 | 0.9130 | | 0.6578 | 7400 | 0.0068 | 0.0055 | 0.9134 | | 0.6667 | 7500 | 0.0073 | 0.0054 | 0.9136 | | 0.6756 | 7600 | 0.0063 | 0.0056 | 0.9131 | | 0.6844 | 7700 | 0.0068 | 0.0054 | 0.9134 | | 0.6933 | 7800 | 0.0057 | 0.0054 | 0.9135 | | 0.7022 | 7900 | 0.0073 | 0.0053 | 0.9137 | | 0.7111 | 8000 | 0.0063 | 0.0053 | 0.9139 | | 0.72 | 8100 | 0.0061 | 0.0052 | 0.9139 | | 0.7289 | 8200 | 0.0062 | 0.0052 | 0.9141 | | 0.7378 | 8300 | 0.0065 | 0.0051 | 0.9143 | | 0.7467 | 8400 | 0.0061 | 0.0052 | 0.9141 | | 0.7556 | 8500 | 0.0064 | 0.0050 | 0.9146 | | 0.7644 | 8600 | 0.0056 | 0.0050 | 0.9146 | | 0.7733 | 8700 | 0.006 | 0.0050 | 0.9146 | | 0.7822 | 8800 | 0.0066 | 0.0049 | 0.9147 | | 0.7911 | 8900 | 0.005 | 0.0048 | 0.9150 | | 0.8 | 9000 | 0.0056 | 0.0048 | 0.9149 | | 0.8089 | 9100 | 0.0061 | 0.0048 | 0.9149 | | 0.8178 | 9200 | 0.0057 | 0.0047 | 0.9149 | | 0.8267 | 9300 | 0.0075 | 0.0048 | 0.9150 | | 0.8356 | 9400 | 0.0057 | 0.0047 | 0.9152 | | 0.8444 | 9500 | 0.0055 | 0.0047 | 0.9151 | | 0.8533 | 9600 | 0.0056 | 0.0047 | 0.9153 | | 0.8622 | 9700 | 0.0049 | 0.0047 | 0.9153 | | 0.8711 | 9800 | 0.0066 | 0.0047 | 0.9154 | | 0.88 | 9900 | 0.0054 | 0.0046 | 0.9154 | | 0.8889 | 10000 | 0.0055 | 0.0046 | 0.9154 | | 0.8978 | 10100 | 0.0055 | 0.0046 | 0.9155 | | 0.9067 | 10200 | 0.0048 | 0.0045 | 0.9155 | | 0.9156 | 10300 | 0.0046 | 0.0045 | 0.9156 | | 0.9244 | 10400 | 0.0063 | 0.0045 | 0.9157 | | 0.9333 | 10500 | 0.0055 | 0.0045 | 0.9157 | | 0.9422 | 10600 | 0.0059 | 0.0045 | 0.9158 | | 0.9511 | 10700 | 0.0049 | 0.0045 | 0.9158 | | 0.96 | 10800 | 0.0058 | 0.0045 | 0.9158 | | 0.9689 | 10900 | 0.0052 | 0.0045 | 0.9158 | | 0.9778 | 11000 | 0.0065 | 0.0044 | 0.9159 | | 0.9867 | 11100 | 0.0053 | 0.0044 | 0.9159 | | 0.9956 | 11200 | 0.0046 | 0.0044 | 0.9159 | | -1 | -1 | - | - | 0.9159 | </details> ### Framework Versions - Python: 3.10.17 - Sentence Transformers: 4.1.0 - Transformers: 4.52.4 - PyTorch: 2.7.0 - Accelerate: 1.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
moradilak/FT_gemma
moradilak
2025-06-04T14:53:54Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-3-1b-pt", "base_model:adapter:google/gemma-3-1b-pt", "region:us" ]
null
2025-06-04T07:55:11Z
--- base_model: google/gemma-3-1b-pt 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 0.14.0
appy1234/Llama3.1-8B-Int8DynamicActivationInt8WeightQuantized
appy1234
2025-06-04T14:53:14Z
0
0
null
[ "pytorch", "llama", "torchao-my-repo", "facebook", "meta", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:quantized:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "torchao", "region:us" ]
text-generation
2025-06-04T14:51:53Z
--- base_model: - meta-llama/Llama-3.1-8B-Instruct language: - en - de - fr - it - pt - hi - es - th license: llama3.1 pipeline_tag: text-generation tags: - torchao-my-repo - facebook - meta - pytorch - llama - llama-3 extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\ \ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\ \ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you\ \ are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\n\ a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\ \ and royalty-free limited license under Meta’s intellectual property or other rights\ \ owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy,\ \ create derivative works of, and make modifications to the Llama Materials.\nb.\ \ Redistribution and Use.\ni. If you distribute or make available the Llama Materials\ \ (or any derivative works thereof), or a product or service (including another\ \ AI model) that contains any of them, you shall (A) provide a copy of this Agreement\ \ with any such Llama Materials; and (B) prominently display “Built with Llama”\ \ on a related website, user interface, blogpost, about page, or product documentation.\ \ If you use the Llama Materials or any outputs or results of the Llama Materials\ \ to create, train, fine tune, or otherwise improve an AI model, which is distributed\ \ or made available, you shall also include “Llama” at the beginning of any such\ \ AI model name.\nii. If you receive Llama Materials, or any derivative works thereof,\ \ from a Licensee as part of an integrated end user product, then Section 2 of\ \ this Agreement will not apply to you.\niii. You must retain in all copies of the\ \ Llama Materials that you distribute the following attribution notice within a\ \ “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed\ \ under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights\ \ Reserved.”\niv. Your use of the Llama Materials must comply with applicable laws\ \ and regulations (including trade compliance laws and regulations) and adhere to\ \ the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy),\ \ which is hereby incorporated by reference into this Agreement.\n2. Additional\ \ Commercial Terms. If, on the Llama 3.1 version release date, the monthly active\ \ users of the products or services made available by or for Licensee, or Licensee’s\ \ affiliates, is greater than 700 million monthly active users in the preceding\ \ calendar month, you must request a license from Meta, which Meta may grant to\ \ you in its sole discretion, and you are not authorized to exercise any of the\ \ rights under this Agreement unless or until Meta otherwise expressly grants you\ \ such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE\ \ LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”\ \ BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY\ \ KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\ \ OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.\ \ YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\ \ THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA\ \ MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT\ \ WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN\ \ CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS\ \ AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,\ \ EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED\ \ OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No\ \ trademark licenses are granted under this Agreement, and in connection with the\ \ Llama Materials, neither Meta nor Licensee may use any name or mark owned by or\ \ associated with the other or any of its affiliates, except as required for reasonable\ \ and customary use in describing and redistributing the Llama Materials or as set\ \ forth in this Section 5(a). 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If you institute\ \ litigation or other proceedings against Meta or any entity (including a cross-claim\ \ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs\ \ or results, or any portion of any of the foregoing, constitutes infringement of\ \ intellectual property or other rights owned or licensable by you, then any licenses\ \ granted to you under this Agreement shall terminate as of the date such litigation\ \ or claim is filed or instituted. You will indemnify and hold harmless Meta from\ \ and against any claim by any third party arising out of or related to your use\ \ or distribution of the Llama Materials.\n6. Term and Termination. The term of\ \ this Agreement will commence upon your acceptance of this Agreement or access\ \ to the Llama Materials and will continue in full force and effect until terminated\ \ in accordance with the terms and conditions herein. Meta may terminate this Agreement\ \ if you are in breach of any term or condition of this Agreement. Upon termination\ \ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\ \ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\ \ and Jurisdiction. This Agreement will be governed and construed under the laws\ \ of the State of California without regard to choice of law principles, and the\ \ UN Convention on Contracts for the International Sale of Goods does not apply\ \ to this Agreement. The courts of California shall have exclusive jurisdiction\ \ of any dispute arising out of this Agreement.\n### Llama 3.1 Acceptable Use Policy\n\ Meta is committed to promoting safe and fair use of its tools and features, including\ \ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\ \ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\ #### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\ \ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\ \ or harm to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ \ illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 3. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 4. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n 5.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 6. Collect, process, disclose, generate, or infer health, demographic,\ \ or other sensitive personal or private information about individuals without rights\ \ and consents required by applicable laws\n 7. Engage in or facilitate any action\ \ or generate any content that infringes, misappropriates, or otherwise violates\ \ any third-party rights, including the outputs or results of any products or services\ \ using the Llama Materials\n 8. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system\n2. Engage in, promote, incite,\ \ facilitate, or assist in the planning or development of activities that present\ \ a risk of death or bodily harm to individuals, including use of Llama 3.1 related\ \ to the following:\n 1. Military, warfare, nuclear industries or applications,\ \ espionage, use for materials or activities that are subject to the International\ \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\ \ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Llama 3.1 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\ \ 6. Generating or facilitating false online engagement, including fake reviews\ \ and other means of fake online engagement\n4. Fail to appropriately disclose to\ \ end users any known dangers of your AI system\nPlease report any violation of\ \ this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # meta-llama/Llama-3.1-8B-Instruct (Quantized) ## Description This model is a quantized version of the original model [`meta-llama/Llama-3.1-8B-Instruct`](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct). It's quantized using the TorchAO library using the [torchao-my-repo](https://huggingface.co/spaces/pytorch/torchao-my-repo) space. ## Quantization Details - **Quantization Type**: Int8DynamicActivationInt8Weight - **Group Size**: None # 📄 Original Model Information ## Model Information The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. **Model developer**: Meta **Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Input modalities</strong> </td> <td><strong>Output modalities</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="3" >Llama 3.1 (text only) </td> <td rowspan="3" >A new mix of publicly available online data. </td> <td>8B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> <td rowspan="3" >15T+ </td> <td rowspan="3" >December 2023 </td> </tr> <tr> <td>70B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> </tr> <tr> <td>405B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> </tr> </table> **Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. **Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** July 23, 2024. **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**. **<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner. ## How to use This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipeline( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Tool use with transformers LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/). Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. Here is a quick example showing a single simple tool: ```python # First, define a tool def get_current_temperature(location: str) -> float: """ Get the current temperature at a location. Args: location: The location to get the temperature for, in the format "City, Country" Returns: The current temperature at the specified location in the specified units, as a float. """ return 22. # A real function should probably actually get the temperature! # Next, create a chat and apply the chat template messages = [ {"role": "system", "content": "You are a bot that responds to weather queries."}, {"role": "user", "content": "Hey, what's the temperature in Paris right now?"} ] inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True) ``` You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so: ```python tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}} messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]}) ``` and then call the tool and append the result, with the `tool` role, like so: ```python messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"}) ``` After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information, see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling). ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct ``` ## Hardware and Software **Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. **Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. **Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq. <table> <tr> <td> </td> <td><strong>Training Time (GPU hours)</strong> </td> <td><strong>Training Power Consumption (W)</strong> </td> <td><strong>Training Location-Based Greenhouse Gas Emissions</strong> <p> <strong>(tons CO2eq)</strong> </td> <td><strong>Training Market-Based Greenhouse Gas Emissions</strong> <p> <strong>(tons CO2eq)</strong> </td> </tr> <tr> <td>Llama 3.1 8B </td> <td>1.46M </td> <td>700 </td> <td>420 </td> <td>0 </td> </tr> <tr> <td>Llama 3.1 70B </td> <td>7.0M </td> <td>700 </td> <td>2,040 </td> <td>0 </td> </tr> <tr> <td>Llama 3.1 405B </td> <td>30.84M </td> <td>700 </td> <td>8,930 </td> <td>0 </td> </tr> <tr> <td>Total </td> <td>39.3M <td> <ul> </ul> </td> <td>11,390 </td> <td>0 </td> </tr> </table> The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. **Data Freshness:** The pretraining data has a cutoff of December 2023. ## Benchmark scores In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong># Shots</strong> </td> <td><strong>Metric</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 3.1 8B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 3.1 70B</strong> </td> <td><strong>Llama 3.1 405B</strong> </td> </tr> <tr> <td rowspan="7" >General </td> <td>MMLU </td> <td>5 </td> <td>macro_avg/acc_char </td> <td>66.7 </td> <td>66.7 </td> <td>79.5 </td> <td>79.3 </td> <td>85.2 </td> </tr> <tr> <td>MMLU-Pro (CoT) </td> <td>5 </td> <td>macro_avg/acc_char </td> <td>36.2 </td> <td>37.1 </td> <td>55.0 </td> <td>53.8 </td> <td>61.6 </td> </tr> <tr> <td>AGIEval English </td> <td>3-5 </td> <td>average/acc_char </td> <td>47.1 </td> <td>47.8 </td> <td>63.0 </td> <td>64.6 </td> <td>71.6 </td> </tr> <tr> <td>CommonSenseQA </td> <td>7 </td> <td>acc_char </td> <td>72.6 </td> <td>75.0 </td> <td>83.8 </td> <td>84.1 </td> <td>85.8 </td> </tr> <tr> <td>Winogrande </td> <td>5 </td> <td>acc_char </td> <td>- </td> <td>60.5 </td> <td>- </td> <td>83.3 </td> <td>86.7 </td> </tr> <tr> <td>BIG-Bench Hard (CoT) </td> <td>3 </td> <td>average/em </td> <td>61.1 </td> <td>64.2 </td> <td>81.3 </td> <td>81.6 </td> <td>85.9 </td> </tr> <tr> <td>ARC-Challenge </td> <td>25 </td> <td>acc_char </td> <td>79.4 </td> <td>79.7 </td> <td>93.1 </td> <td>92.9 </td> <td>96.1 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki </td> <td>5 </td> <td>em </td> <td>78.5 </td> <td>77.6 </td> <td>89.7 </td> <td>89.8 </td> <td>91.8 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD </td> <td>1 </td> <td>em </td> <td>76.4 </td> <td>77.0 </td> <td>85.6 </td> <td>81.8 </td> <td>89.3 </td> </tr> <tr> <td>QuAC (F1) </td> <td>1 </td> <td>f1 </td> <td>44.4 </td> <td>44.9 </td> <td>51.1 </td> <td>51.1 </td> <td>53.6 </td> </tr> <tr> <td>BoolQ </td> <td>0 </td> <td>acc_char </td> <td>75.7 </td> <td>75.0 </td> <td>79.0 </td> <td>79.4 </td> <td>80.0 </td> </tr> <tr> <td>DROP (F1) </td> <td>3 </td> <td>f1 </td> <td>58.4 </td> <td>59.5 </td> <td>79.7 </td> <td>79.6 </td> <td>84.8 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong># Shots</strong> </td> <td><strong>Metric</strong> </td> <td><strong>Llama 3 8B Instruct</strong> </td> <td><strong>Llama 3.1 8B Instruct</strong> </td> <td><strong>Llama 3 70B Instruct</strong> </td> <td><strong>Llama 3.1 70B Instruct</strong> </td> <td><strong>Llama 3.1 405B Instruct</strong> </td> </tr> <tr> <td rowspan="4" >General </td> <td>MMLU </td> <td>5 </td> <td>macro_avg/acc </td> <td>68.5 </td> <td>69.4 </td> <td>82.0 </td> <td>83.6 </td> <td>87.3 </td> </tr> <tr> <td>MMLU (CoT) </td> <td>0 </td> <td>macro_avg/acc </td> <td>65.3 </td> <td>73.0 </td> <td>80.9 </td> <td>86.0 </td> <td>88.6 </td> </tr> <tr> <td>MMLU-Pro (CoT) </td> <td>5 </td> <td>micro_avg/acc_char </td> <td>45.5 </td> <td>48.3 </td> <td>63.4 </td> <td>66.4 </td> <td>73.3 </td> </tr> <tr> <td>IFEval </td> <td> </td> <td> </td> <td>76.8 </td> <td>80.4 </td> <td>82.9 </td> <td>87.5 </td> <td>88.6 </td> </tr> <tr> <td rowspan="2" >Reasoning </td> <td>ARC-C </td> <td>0 </td> <td>acc </td> <td>82.4 </td> <td>83.4 </td> <td>94.4 </td> <td>94.8 </td> <td>96.9 </td> </tr> <tr> <td>GPQA </td> <td>0 </td> <td>em </td> <td>34.6 </td> <td>30.4 </td> <td>39.5 </td> <td>46.7 </td> <td>50.7 </td> </tr> <tr> <td rowspan="4" >Code </td> <td>HumanEval </td> <td>0 </td> <td>pass@1 </td> <td>60.4 </td> <td>72.6 </td> <td>81.7 </td> <td>80.5 </td> <td>89.0 </td> </tr> <tr> <td>MBPP ++ base version </td> <td>0 </td> <td>pass@1 </td> <td>70.6 </td> <td>72.8 </td> <td>82.5 </td> <td>86.0 </td> <td>88.6 </td> </tr> <tr> <td>Multipl-E HumanEval </td> <td>0 </td> <td>pass@1 </td> <td>- </td> <td>50.8 </td> <td>- </td> <td>65.5 </td> <td>75.2 </td> </tr> <tr> <td>Multipl-E MBPP </td> <td>0 </td> <td>pass@1 </td> <td>- </td> <td>52.4 </td> <td>- </td> <td>62.0 </td> <td>65.7 </td> </tr> <tr> <td rowspan="2" >Math </td> <td>GSM-8K (CoT) </td> <td>8 </td> <td>em_maj1@1 </td> <td>80.6 </td> <td>84.5 </td> <td>93.0 </td> <td>95.1 </td> <td>96.8 </td> </tr> <tr> <td>MATH (CoT) </td> <td>0 </td> <td>final_em </td> <td>29.1 </td> <td>51.9 </td> <td>51.0 </td> <td>68.0 </td> <td>73.8 </td> </tr> <tr> <td rowspan="4" >Tool Use </td> <td>API-Bank </td> <td>0 </td> <td>acc </td> <td>48.3 </td> <td>82.6 </td> <td>85.1 </td> <td>90.0 </td> <td>92.0 </td> </tr> <tr> <td>BFCL </td> <td>0 </td> <td>acc </td> <td>60.3 </td> <td>76.1 </td> <td>83.0 </td> <td>84.8 </td> <td>88.5 </td> </tr> <tr> <td>Gorilla Benchmark API Bench </td> <td>0 </td> <td>acc </td> <td>1.7 </td> <td>8.2 </td> <td>14.7 </td> <td>29.7 </td> <td>35.3 </td> </tr> <tr> <td>Nexus (0-shot) </td> <td>0 </td> <td>macro_avg/acc </td> <td>18.1 </td> <td>38.5 </td> <td>47.8 </td> <td>56.7 </td> <td>58.7 </td> </tr> <tr> <td>Multilingual </td> <td>Multilingual MGSM (CoT) </td> <td>0 </td> <td>em </td> <td>- </td> <td>68.9 </td> <td>- </td> <td>86.9 </td> <td>91.6 </td> </tr> </table> #### Multilingual benchmarks <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Language</strong> </td> <td><strong>Llama 3.1 8B</strong> </td> <td><strong>Llama 3.1 70B</strong> </td> <td><strong>Llama 3.1 405B</strong> </td> </tr> <tr> <td rowspan="9" ><strong>General</strong> </td> <td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong> </td> <td>Portuguese </td> <td>62.12 </td> <td>80.13 </td> <td>84.95 </td> </tr> <tr> <td>Spanish </td> <td>62.45 </td> <td>80.05 </td> <td>85.08 </td> </tr> <tr> <td>Italian </td> <td>61.63 </td> <td>80.4 </td> <td>85.04 </td> </tr> <tr> <td>German </td> <td>60.59 </td> <td>79.27 </td> <td>84.36 </td> </tr> <tr> <td>French </td> <td>62.34 </td> <td>79.82 </td> <td>84.66 </td> </tr> <tr> <td>Hindi </td> <td>50.88 </td> <td>74.52 </td> <td>80.31 </td> </tr> <tr> <td>Thai </td> <td>50.32 </td> <td>72.95 </td> <td>78.21 </td> </tr> </table> ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. ### Responsible deployment Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. #### Llama 3.1 instruct Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. **Fine-tuning data** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.1 systems **Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. #### New capabilities Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases. **Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. **Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization. **Red teaming** For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical and other risks We specifically focused our efforts on mitigating the following critical risk areas: **1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness** To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. **2. Child Safety** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3. Cyber attack enablement** Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
pmk2021/vampnet_small-tria-d1026-l8-h8-mode-vampnet_rms-hchroma-36c-top3-last
pmk2021
2025-06-04T14:52:17Z
0
0
null
[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]
null
2025-06-04T14:52:01Z
--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
phospho-app/GarrieD-ACT-toy_in_pot_v2_Batch_10
phospho-app
2025-06-04T14:50:48Z
0
0
null
[ "safetensors", "phosphobot", "act", "region:us" ]
null
2025-06-04T14:23:08Z
--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## This model was trained using **phospho**. Training was successfull, try it out on your robot! ## Training parameters: - **Dataset**: [GarrieD/toy_in_pot_v2_simple](https://huggingface.co/datasets/GarrieD/toy_in_pot_v2_simple) - **Wandb run URL**: None - **Epochs**: None - **Batch size**: 10 - **Training steps**: 8000 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
VanishedBrB/codegemma-7b-it-memorized-SQL-F32-GGUF
VanishedBrB
2025-06-04T14:49:56Z
0
0
transformers
[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma", "trl", "llama-cpp", "gguf-my-lora", "en", "base_model:VanishedBrB/codegemma-7b-it-memorized-SQL", "base_model:quantized:VanishedBrB/codegemma-7b-it-memorized-SQL", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-04T14:49:44Z
--- base_model: VanishedBrB/codegemma-7b-it-memorized-SQL tags: - text-generation-inference - transformers - unsloth - gemma - trl - llama-cpp - gguf-my-lora license: apache-2.0 language: - en --- # VanishedBrB/codegemma-7b-it-memorized-SQL-F32-GGUF This LoRA adapter was converted to GGUF format from [`VanishedBrB/codegemma-7b-it-memorized-SQL`](https://huggingface.co/VanishedBrB/codegemma-7b-it-memorized-SQL) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space. Refer to the [original adapter repository](https://huggingface.co/VanishedBrB/codegemma-7b-it-memorized-SQL) for more details. ## Use with llama.cpp ```bash # with cli llama-cli -m base_model.gguf --lora codegemma-7b-it-memorized-SQL-f32.gguf (...other args) # with server llama-server -m base_model.gguf --lora codegemma-7b-it-memorized-SQL-f32.gguf (...other args) ``` To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
CAMeL-Lab/text-editing-qalb14-nopnx
CAMeL-Lab
2025-06-04T14:48:23Z
0
1
null
[ "pytorch", "bert", "token-classification", "ar", "arxiv:2503.00985", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "license:mit", "region:us" ]
token-classification
2025-06-04T08:29:51Z
--- license: mit language: - ar base_model: - aubmindlab/bert-base-arabertv02 pipeline_tag: token-classification --- # SWEET<sub>NoPnx</sub> QALB-2014 Model ## Model Description `CAMeL-Lab/text-editing-qalb14-nopnx` is a text editing model tailored for grammatical error correction (GEC) in Modern Standard Arabic (MSA). The model is based on [AraBERTv02](https://huggingface.co/aubmindlab/bert-base-arabertv02), which we fine-tuned using the [QALB-2014](https://camel.abudhabi.nyu.edu/qalb-shared-task-2015/) dataset. This model was introduced in our ACL 2025 paper, [Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study](https://arxiv.org/abs/2503.00985), where we refer to it as SWEET (Subword Edit Error Tagger). The model was fine-tuned to fix non-punctuation (i.e., NoPnx) errors. Details about the training procedure, data preprocessing, and hyperparameters are available in the paper. The fine-tuning code and associated resources are publicly available on our GitHub repository: https://github.com/CAMeL-Lab/text-editing. ## Intended uses To use the `CAMeL-Lab/text-editing-qalb14-nopnx` model, you must clone our text editing [GitHub repository](https://github.com/CAMeL-Lab/text-editing) and follow the installation requirements. We used this SWEET<sub>NoPnx</sub> model to report results on the QALB-2014 dev and test sets in our [paper](https://arxiv.org/abs/2503.00985). This model is intended to be used with SWEET<sub>Pnx</sub> ([`CAMeL-Lab/text-editing-qalb14-pnx`](https://huggingface.co/CAMeL-Lab/text-editing-qalb14-pnx)) model. ## How to use Clone our text editing [GitHub repository](https://github.com/CAMeL-Lab/text-editing) and follow the installation requirements ```python from transformers import BertTokenizer, BertForTokenClassification import torch import torch.nn.functional as F from gec.tag import rewrite nopnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-qalb14-nopnx') nopnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-qalb14-nopnx') pnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-qalb14-pnx') pnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-qalb14-pnx') def predict(model, tokenizer, text, decode_iter=1): for _ in range(decode_iter): tokenized_text = tokenizer(text, return_tensors="pt", is_split_into_words=True) with torch.no_grad(): logits = model(**tokenized_text).logits preds = F.softmax(logits.squeeze(), dim=-1) preds = torch.argmax(preds, dim=-1).cpu().numpy() edits = [model.config.id2label[p] for p in preds[1:-1]] assert len(edits) == len(tokenized_text['input_ids'][0][1:-1]) subwords = tokenizer.convert_ids_to_tokens(tokenized_text['input_ids'][0][1:-1]) text = rewrite(subwords=[subwords], edits=[edits])[0][0] return text text = 'يجب الإهتمام ب الصحه و لا سيما ف ي الصحه النفسيه ياشباب المستقبل،،'.split() output_sent = predict(nopnx_model, nopnx_tokenizer, text, decode_iter=2) output_sent = predict(pnx_model, pnx_tokenizer, output_sent.split(), decode_iter=1) print(output_sent) # يجب الاهتمام بالصحة ولا سيما في الصحة النفسية يا شباب المستقبل . ``` ## Citation ```bibtex @inter{alhafni-habash-2025-enhancing, title={Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study}, author={Bashar Alhafni and Nizar Habash}, year={2025}, eprint={2503.00985}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.00985}, } ```
sergioalves/3c4c31fb-93b4-4385-82e4-5b00f74b01c2
sergioalves
2025-06-04T14:48:04Z
0
0
peft
[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "base_model:adapter:NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]
null
2025-06-04T11:48:33Z
--- library_name: peft license: apache-2.0 base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO tags: - axolotl - generated_from_trainer model-index: - name: 3c4c31fb-93b4-4385-82e4-5b00f74b01c2 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 absolute_data_files: false adapter: lora base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - da03c57f7b5fb416_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: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: sergioalves/3c4c31fb-93b4-4385-82e4-5b00f74b01c2 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-07 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.2 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 300 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/da03c57f7b5fb416_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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: 2243dcc2-ad40-4f80-8d64-5f3a12f50764 wandb_project: s56-7 wandb_run: your_name wandb_runid: 2243dcc2-ad40-4f80-8d64-5f3a12f50764 warmup_steps: 30 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 3c4c31fb-93b4-4385-82e4-5b00f74b01c2 This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0707 ## 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: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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: 30 - training_steps: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 4.8307 | 0.0000 | 1 | 1.1163 | | 5.3306 | 0.0054 | 150 | 1.0843 | | 3.4492 | 0.0107 | 300 | 1.0707 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
LeonGuertler/Qwen3-4B-batch-4-experiment-0-step_000175
LeonGuertler
2025-06-04T14:47:04Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:37:09Z
--- 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]
mlabonne/alpagasus-2-7b
mlabonne
2025-06-04T14:46:45Z
18
2
transformers
[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "alpaca", "alpagasus", "en", "dataset:mlabonne/alpagasus", "arxiv:2307.08701", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-06T17:04:22Z
--- datasets: - mlabonne/alpagasus language: - en pipeline_tag: text-generation tags: - llama - alpaca - alpagasus --- # 🦙🕊️ Alpagasus-2-7b 📝 [Paper](https://arxiv.org/abs/2307.08701) | 📄 [Blog](https://lichang-chen.github.io/AlpaGasus/) | 💻 [Code](https://github.com/gpt4life/alpagasus/tree/main) | 🤗 [Model](https://huggingface.co/gpt4life/alpagasus-7b) (unofficial) This is a `Llama-2-7b-hf` model fine-tuned using QLoRA (4-bit precision) on the [`mlabonne/alpagasus`](https://huggingface.co/datasets/mlabonne/alpagasus) dataset, which is a high-quality subset (9k samples) of the Alpaca dataset (52k samples). ## 🔧 Training ![](https://i.imgur.com/ebwyRbo.png) It was trained on an RTX 3090 using the [🐜🔧TinyTuner](https://github.com/mlabonne/tinytuner). Parameters: ```yaml # Dataset dataset_name: mlabonne/alpagasus prompt_template: alpaca max_seq_length: 512 val_set_size: 0.01 # Loading load_in_8bit: false load_in_4bit: true bf16: true fp16: false tf32: true # Lora adapter: qlora lora_model_dir: lora_r: 8 lora_alpha: 16 lora_dropout: 0.1 lora_target_modules: - q_proj - v_proj lora_fan_in_fan_out: # Training learning_rate: 0.00002 micro_batch_size: 24 gradient_accumulation_steps: 1 num_epochs: 3 lr_scheduler_type: cosine optim: paged_adamw_32bit group_by_length: true warmup_ratio: 0.03 eval_steps: 0.01 save_strategy: epoch logging_steps: 1 weight_decay: 0 max_grad_norm: max_steps: -1 gradient_checkpointing: true # QLoRA bnb_4bit_compute_dtype: float16 bnb_4bit_quant_type: nf4 bnb_4bit_use_double_quant: false ``` ## 💻 Usage ``` python # pip install transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/alpagasus-2-7b" prompt = "What is a large language model?" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( f'### Instruction: {prompt}', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ```
LeonGuertler/Qwen3-4B-batch-4-experiment-16-step_000150
LeonGuertler
2025-06-04T14:46:40Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:36:54Z
--- 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]
ZimeryTao/Qwen2.5-vl-7b-ds900
ZimeryTao
2025-06-04T14:46:01Z
0
0
transformers
[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
image-text-to-text
2025-06-04T14:40:21Z
--- base_model: unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** ZimeryTao - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-vl-7b-instruct-unsloth-bnb-4bit This qwen2_5_vl 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)
polinaShyshkina/bert-duplicate-classifier-v3
polinaShyshkina
2025-06-04T14:45:07Z
0
0
transformers
[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2025-06-04T14:44:29Z
--- 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]
LeonGuertler/Qwen3-4B-batch-4-experiment-2-step_000175
LeonGuertler
2025-06-04T14:44:42Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:35:56Z
--- 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]
Diamantis99/HuZtVbv
Diamantis99
2025-06-04T14:43:11Z
0
0
segmentation-models-pytorch
[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]
image-segmentation
2025-06-04T14:43:04Z
--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # DeepLabV3Plus Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "timm-tf_efficientnet_lite4", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 256, "decoder_atrous_rates": (12, 24, 36), "decoder_aspp_separable": True, "decoder_aspp_dropout": 0.5, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.7887688279151917, "test_dataset_iou": 0.8232298493385315 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
Allen172/gemma-text-4300
Allen172
2025-06-04T14:41:48Z
0
0
transformers
[ "transformers", "safetensors", "gemma3", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]
image-text-to-text
2025-06-04T14:18:42Z
--- 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. 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]
svalabs/infoxlm-german-question-answering
svalabs
2025-06-04T14:41:30Z
20
3
transformers
[ "transformers", "pytorch", "xlm-roberta", "question-answering", "qa", "de", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]
question-answering
2022-10-05T06:25:08Z
--- license: cc-by-4.0 language: - de task_categories: - question-answering tags: - question-answering - pytorch - qa - de --- # SVALabs - InfoXLM German QA In this repository we present our german question answering model. The trained model is based on [InfoXLM-large](https://huggingface.co/microsoft/infoxlm-large) and was finetuned using the [SQuAD](https://huggingface.co/datasets/squad) dataset and the [GermanQuAD](https://huggingface.co/datasets/deepset/germanquad) dataset. ### Model Details | | Description or Link | |---|---| |**Base model** | [```InfoXLM-large```](https://huggingface.co/microsoft/infoxlm-large) | |**Finetuning task**| Question Answering | |**Source datasets**| [```SQuAD```](https://huggingface.co/datasets/squad); [```GermanQuAD```](https://huggingface.co/datasets/deepset/germanquad)| ### Performance The model was tested on 1692 samples of the GermanQuAD test dataset (the other samples were used for validation) F1-Score: 89.05 EM: 74.76 ### Contact - Nicole Wochatz, [email protected]
automated-analytics/qwen3-1.7b-pii-masking-gguf
automated-analytics
2025-06-04T14:41:27Z
0
0
transformers
[ "transformers", "gguf", "qwen3", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2025-06-04T14:41:18Z
--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** automated-analytics - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-1.7B-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)
daitranskku/nanoVLM
daitranskku
2025-06-04T14:41:22Z
1
0
nanovlm
[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]
image-text-to-text
2025-05-26T16:13:15Z
--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- **nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. **Usage:** Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("daitranskku/nanoVLM") ```
jbreuch/ultrafeedback-sycophantic-model
jbreuch
2025-06-04T14:41:16Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T04:48:36Z
--- 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]
kovalenko-nk-22/sdxl-base-1.0-buzova-dreambooth-lora
kovalenko-nk-22
2025-06-04T14:40:33Z
0
0
diffusers
[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2025-06-04T11:16:57Z
--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a photo of Buzova widget: - text: A photo of Buzova on the beach output: url: image_0.png - text: A photo of Buzova on the beach output: url: image_1.png tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- 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. --> # SDXL LoRA DreamBooth - kovalenko-nk-22/sdxl-base-1.0-buzova-dreambooth-lora <Gallery /> ## Model description These are kovalenko-nk-22/sdxl-base-1.0-buzova-dreambooth-lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of Buzova to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](kovalenko-nk-22/sdxl-base-1.0-buzova-dreambooth-lora/tree/main) them in the Files & versions tab. ## 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]
Tarun-ak/combinatorics-expert
Tarun-ak
2025-06-04T14:39:07Z
14
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:finetune:Qwen/Qwen2.5-14B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-05-31T17:12:46Z
--- base_model: Qwen/Qwen2.5-14B-Instruct library_name: transformers model_name: s1-20250601_235415 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for s1-20250601_235415 This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct). 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="Tarun-ak/s1-20250601_235415", 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: 0.12.0 - Transformers: 4.46.1 - Pytorch: 2.5.1+cu121 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
jinx2321/korean-1e4-paper
jinx2321
2025-06-04T14:38:44Z
13
0
transformers
[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:everdoubling/byt5-Korean-small", "base_model:finetune:everdoubling/byt5-Korean-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2025-06-04T10:16:13Z
--- library_name: transformers license: apache-2.0 base_model: everdoubling/byt5-Korean-small tags: - generated_from_trainer model-index: - name: korean-1e4-paper 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. --> # korean-1e4-paper This model is a fine-tuned version of [everdoubling/byt5-Korean-small](https://huggingface.co/everdoubling/byt5-Korean-small) on the None dataset. ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
SaiVenkat04/GPT-2_Fine_Tuning
SaiVenkat04
2025-06-04T14:38:02Z
0
0
transformers
[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:37:27Z
--- 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. 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]
Diamantis99/Y3HrBYA
Diamantis99
2025-06-04T14:37:06Z
0
0
segmentation-models-pytorch
[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]
image-segmentation
2025-06-04T14:36:53Z
--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # DeepLabV3Plus Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "timm-efficientnet-b7", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 256, "decoder_atrous_rates": (12, 24, 36), "decoder_aspp_separable": True, "decoder_aspp_dropout": 0.5, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.8514536619186401, "test_dataset_iou": 0.8706037402153015 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
LeonGuertler/Qwen3-4B-batch-4-experiment-8-step_000125
LeonGuertler
2025-06-04T14:37:02Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:27:17Z
--- 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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LeonGuertler/Qwen3-4B-batch-4-experiment-0-step_000150
LeonGuertler
2025-06-04T14:36:52Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:26:57Z
--- 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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LeonGuertler/Qwen3-4B-batch-4-experiment-16-step_000125
LeonGuertler
2025-06-04T14:36:38Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:26:31Z
--- 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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pavan-naik/gemma_3_1b_it_kn_pt_ft
pavan-naik
2025-06-04T14:35:40Z
9
0
transformers
[ "transformers", "safetensors", "gemma3_text", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-05-23T22:03:26Z
--- 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]
LeonGuertler/Qwen3-4B-batch-4-experiment-2-step_000150
LeonGuertler
2025-06-04T14:35:38Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:26:07Z
--- 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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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]
matrixportal/Aya-Empati-v3-Q4_0-GGUF
matrixportal
2025-06-04T14:33:48Z
0
0
transformers
[ "transformers", "gguf", "matrixportal", "llama-cpp", "gguf-my-repo", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "base_model:matrixportal/Aya-Empati-v3", "base_model:quantized:matrixportal/Aya-Empati-v3", "license:cc-by-nc-4.0", "region:us", "conversational" ]
null
2025-06-04T14:33:26Z
--- base_model: matrixportal/Aya-Empati-v3 language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 library_name: transformers tags: - matrixportal - llama-cpp - gguf-my-repo inference: false --- # matrixportal/Aya-Empati-v3-Q4_0-GGUF This model was converted to GGUF format from [`matrixportal/Aya-Empati-v3`](https://huggingface.co/matrixportal/Aya-Empati-v3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/matrixportal/Aya-Empati-v3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo matrixportal/Aya-Empati-v3-Q4_0-GGUF --hf-file aya-empati-v3-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo matrixportal/Aya-Empati-v3-Q4_0-GGUF --hf-file aya-empati-v3-q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo matrixportal/Aya-Empati-v3-Q4_0-GGUF --hf-file aya-empati-v3-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo matrixportal/Aya-Empati-v3-Q4_0-GGUF --hf-file aya-empati-v3-q4_0.gguf -c 2048 ```
apriasmoro/a19732c1-fdbb-43aa-9d80-c293e927792b
apriasmoro
2025-06-04T14:33:11Z
0
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "base_model:adapter:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]
null
2025-06-04T14:28:34Z
--- library_name: peft license: llama3 base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 tags: - axolotl - generated_from_trainer model-index: - name: a19732c1-fdbb-43aa-9d80-c293e927792b 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: `0.10.0.dev0` ```yaml adapter: lora base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 bf16: false bnb_4bit_compute_dtype: float16 bnb_4bit_quant_type: nf4 bnb_4bit_use_double_quant: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9b33138dcfdbbaea_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: None field_instruction: instruct field_output: output field_system: None format: None no_input_format: None system_format: '{system}' system_prompt: None debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: true fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true group_by_length: true hub_model_id: apriasmoro/a19732c1-fdbb-43aa-9d80-c293e927792b hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 3 micro_batch_size: 2 mlflow_experiment_name: /tmp/9b33138dcfdbbaea_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 save_steps: 50 sequence_len: 512 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: 6eee0287-8ac7-4224-9016-db360c2e7534 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 6eee0287-8ac7-4224-9016-db360c2e7534 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # a19732c1-fdbb-43aa-9d80-c293e927792b This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5300 ## 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - 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: 10 - training_steps: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.5038 | 0.0003 | 1 | 1.5386 | | 1.1239 | 0.0005 | 2 | 1.5362 | | 1.0363 | 0.0008 | 3 | 1.5300 | ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Dhananajay09/Arjuna
Dhananajay09
2025-06-04T14:32:00Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2025-06-04T14:32:00Z
--- license: apache-2.0 ---
y22ma/unsloth_lora_model_qwen2b
y22ma
2025-06-04T14:31:35Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2_vl", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-04-24T01:58:11Z
--- base_model: unsloth/qwen2-vl-2b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2_vl - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** y22ma - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2-vl-2b-instruct-unsloth-bnb-4bit This qwen2_vl 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)
lhriscu/abel_results
lhriscu
2025-06-04T14:29:27Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3", "endpoints_compatible", "region:us" ]
null
2025-06-04T14:29:23Z
--- base_model: mistralai/Mistral-7B-Instruct-v0.3 library_name: transformers model_name: abel_results tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for abel_results This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3). 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="lhriscu/abel_results", 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: 0.18.1 - Transformers: 4.52.4 - Pytorch: 2.6.0 - 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}} } ```
PKU-DS-LAB/FairyR1-32B
PKU-DS-LAB
2025-06-04T14:29:11Z
1,289
97
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "en", "arxiv:2503.04872", "arxiv:2403.13257", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-05-23T06:58:28Z
--- license: apache-2.0 language: - en base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-32B pipeline_tag: text-generation library_name: transformers --- # Welcome to FairyR1-32B created by PKU-DS-LAB! | Benchmark | DeepSeek-R1-671B | DeepSeek-R1-Distill-Qwen-32B | FairyR1-32B (PKU) | | :-----------------------: | :--------------: | :--------------------------: | :-----------------------: | | **AIME 2024 (Math)** | 79.8 | 72.6 | **80.4** | | **AIME 2025 (Math)** | 70.0 | 52.9 | **75.6** | | **LiveCodeBench (Code)** | 65.9 | 57.2 | **67.7** | | **GPQA-Diamond (Sci-QA)** | **71.5** | 62.1 | 60.0 | ## Introduction FairyR1-32B, a highly efficient large-language-model (LLM) that matches or exceeds larger models on select tasks despite using only ~5% of their parameters. Built atop the DeepSeek-R1-Distill-Qwen-32B base, FairyR1-32B leverages a novel “distill-and-merge” pipeline—combining task-focused fine-tuning with model-merging techniques to deliver competitive performance with drastically reduced size and inference cost. This project was funded by NSFC, Grant 624B2005. ## Model Details The FairyR1 model represents a further exploration of our earlier work [TinyR1](https://arxiv.org/pdf/2503.04872), retaining the core “Branch-Merge Distillation” approach while introducing refinements in data processing and model architecture. In this effort, we overhauled the distillation data pipeline: raw examples from datasets such as AIMO/NuminaMath-1.5 for mathematics and OpenThoughts-114k for code were first passed through multiple 'teacher' models to generate candidate answers. These candidates were then carefully selected, restructured, and refined, especially for the chain-of-thought(CoT). Subsequently, we applied multi-stage filtering—including automated correctness checks for math problems and length-based selection (2K–8K tokens for math samples, 4K–8K tokens for code samples). This yielded two focused training sets of roughly 6.6K math examples and 3.8K code examples. On the modeling side, rather than training three separate specialists as before, we limited our scope to just two domain experts (math and code), each trained independently under identical hyperparameters (e.g., learning rate and batch size) for about five epochs. We then fused these experts into a single 32B-parameter model using the [ArceeFusion](https://arxiv.org/pdf/2403.13257) tool. By streamlining both the data distillation workflow and the specialist-model merging process, FairyR1 achieves task-competitive results with only a fraction of the parameters and computational cost of much larger models. ## Result Analysis and Key Contributions: From the test results, FairyR1 scored slightly higher than DeepSeek-R1-671B on the AIME 2025 and LiveCodeBench benchmarks, and performed comparably on AIME 2024. These results indicate that, by building on the DeepSeek‑R1‑Distill‑Qwen‑32B base and applying targeted techniques, FairyR1 achieves comparable or slightly superior performance in mathematical and programming domains using only about 5% of the parameter count of much larger models, although performance gaps may remain in other fields such as scientific question answering. This work demonstrates the feasibility of significantly reducing model size and potential inference cost through optimized data processing and model fusion techniques while maintaining strong task-specific performance. ## Model Description - **Developed by:** PKU-DS-LAB - **Model type:** Reasoning Model - **Language(s) (NLP):** English, Chinese - **License:** apache-2.0 - **Finetuned from model:** DeepSeek-R1-Distill-Qwen-32B ### Training Data - **Math:** 6.6k CoT trajectories from [AI-MO/NuminaMath-1.5](https://huggingface.co/datasets/AI-MO/NuminaMath-1.5), default subset - **Coding:** 3.8k CoT trajectories from [open-thoughts/OpenThoughts-114k](https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k), coding subset ### Hardware Utilization - **Hardware Type:** 32 × NVIDIA-H100 - **Hours used(Math):** 2.5h - **Hours used(Coding):** 1.5h - **Model Merging:** about 40min on CPU, no GPU needed. ### Evaluation Set - AIME 2024/2025 (math): We evaluate 32 times and report the average accuracy. [AIME 2024](https://huggingface.co/datasets/HuggingFaceH4/aime_2024) contains 30 problems. [AIME 2025](https://huggingface.co/datasets/MathArena/aime_2025) consists of Part I and Part II, with a total of 30 questions.<br> - [LiveCodeBench (code)](https://huggingface.co/datasets/livecodebench/code_generation_lite): We evaluate 8 times and report the average accuracy. The dataset version is "release_v5" (date range: 2024-08-01 to 2025-02-01), consisting of 279 problems.<br> - [GPQA-Diamond (Sci-QA)](https://huggingface.co/datasets/Idavidrein/gpqa): We evaluate 8 times and report the average accuracy. The dataset consists of 198 problems.<br> ## FairyR1 series Team Members: Leading By: Tong Yang Core Contributors: Wang Li; Junting Zhou; Wenrui Liu; Yilun Yao; Rongle Wang ## Model Card Contact For more details, please contact: [email protected]
Triangle104/Amoral-Fallen-Omega-Gemma3-12B-Q4_K_S-GGUF
Triangle104
2025-06-04T14:26:44Z
48
0
null
[ "gguf", "nsfw", "explicit", "roleplay", "unaligned", "dangerous", "ERP", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:ReadyArt/Amoral-Fallen-Omega-Gemma3-12B", "base_model:merge:ReadyArt/Amoral-Fallen-Omega-Gemma3-12B", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]
text-generation
2025-05-12T15:47:43Z
--- license: gemma language: - en base_model: ReadyArt/Amoral-Fallen-Omega-Gemma3-12B base_model_relation: merge pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - dangerous - ERP - llama-cpp - gguf-my-repo --- # Triangle104/Amoral-Fallen-Omega-Gemma3-12B-Q4_K_S-GGUF This model was converted to GGUF format from [`ReadyArt/Amoral-Fallen-Omega-Gemma3-12B`](https://huggingface.co/ReadyArt/Amoral-Fallen-Omega-Gemma3-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/ReadyArt/Amoral-Fallen-Omega-Gemma3-12B) for more details on the model. --- This model represents the forbidden merger of: - 🧬 The-Omega-Directive-Gemma3-12B-v1.0 - Unprecedented coherent depravity, well-rounded ERP, low repetition even at maximum length - ⚡ Fallen-Gemma3-12B-v1 - Fallen Gemma3 12B v1 is an evil tune - 💎 amoral-gemma3-12B-v2 - Improved natural intelligence and less refusals --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Amoral-Fallen-Omega-Gemma3-12B-Q4_K_S-GGUF --hf-file amoral-fallen-omega-gemma3-12b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Amoral-Fallen-Omega-Gemma3-12B-Q4_K_S-GGUF --hf-file amoral-fallen-omega-gemma3-12b-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Amoral-Fallen-Omega-Gemma3-12B-Q4_K_S-GGUF --hf-file amoral-fallen-omega-gemma3-12b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Amoral-Fallen-Omega-Gemma3-12B-Q4_K_S-GGUF --hf-file amoral-fallen-omega-gemma3-12b-q4_k_s.gguf -c 2048 ```
LeonGuertler/Qwen3-4B-batch-4-experiment-0-step_000125
LeonGuertler
2025-06-04T14:26:39Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:16:53Z
--- 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]
somosnlp-hackathon-2025/Qwen3-8B-gastronomia-hispana-qlora-LoRA
somosnlp-hackathon-2025
2025-06-04T14:26:32Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-04T14:26:11Z
--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** somosnlp-hackathon-2025 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-8B-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)
LeonGuertler/Qwen3-4B-batch-4-experiment-16-step_000100
LeonGuertler
2025-06-04T14:26:12Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:16:18Z
--- 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. 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]
plumpyfield/natix_v2-006
plumpyfield
2025-06-04T14:25:59Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2025-06-04T14:25:17Z
--- 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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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]
LeonGuertler/Qwen3-4B-batch-4-experiment-2-step_000125
LeonGuertler
2025-06-04T14:25:49Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-04T14:16:02Z
--- 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]
Diamantis99/nUarKV2
Diamantis99
2025-06-04T14:25:46Z
0
0
segmentation-models-pytorch
[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]
image-segmentation
2025-06-04T14:25:42Z
--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # DeepLabV3Plus Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "mobilenet_v2", "encoder_depth": 5, "encoder_weights": "imagenet", "encoder_output_stride": 16, "decoder_channels": 256, "decoder_atrous_rates": (12, 24, 36), "decoder_aspp_separable": True, "decoder_aspp_dropout": 0.5, "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.816940426826477, "test_dataset_iou": 0.8470632433891296 } ] ``` ## Dataset Dataset name: VisionPipe ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)