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Furqan118/llama-lora-peft	Furqan118	2025-04-22T22:07:19Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2025-04-22T21:30:19Z	--- base_model: meta-llama/Llama-2-7b-hf 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 0.15.1
wriothsly/whisper-small-as	wriothsly	2025-04-22T22:02:27Z	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "as", "dataset:wriothsly/main1", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-19T16:11:44Z	--- library_name: transformers language: - as license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - wriothsly/main1 metrics: - wer model-index: - name: Whisper Small as - Debojitdutta results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: main1 type: wriothsly/main1 args: 'config: as, split: test' metrics: - name: Wer type: wer value: 0.6648326455754241 --- <!-- 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. --> # Whisper Small as - Debojitdutta This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the main1 dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.6648 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 0.0055 \| 6.6225 \| 1000 \| 0.0060 \| 2.8886 \| \| 0.0009 \| 13.2450 \| 2000 \| 0.0037 \| 1.5589 \| \| 0.0003 \| 19.8675 \| 3000 \| 0.0002 \| 0.6878 \| \| 0.0 \| 26.4901 \| 4000 \| 0.0000 \| 0.6648 \| \| 0.0 \| 33.1126 \| 5000 \| 0.0000 \| 0.6648 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
A-keven/A-keven-textgen	A-keven	2025-04-22T22:00:34Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T22:00:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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xw17/Llama-3.2-3B-Instruct_finetuned_1_optimized1	xw17	2025-04-22T22:00:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T21:56:27Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
a-imantha/qwen_2_5_xray_7b	a-imantha	2025-04-22T21:59:52Z	4	0	null	[ "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	2025-03-10T22:33:32Z	--- license: apache-2.0 ---
ashourzadeh7/deepseek-sft2-deception	ashourzadeh7	2025-04-22T21:59:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-22T21:58:51Z	--- base_model: unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ashourzadeh7 - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
corygong/stt_en_conformer_ctc_small_v2	corygong	2025-04-22T21:57:38Z	0	0	null	[ "region:us" ]	null	2024-07-10T03:39:45Z	--- {} --- ## Model Overview <DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("corygong/stt_en_conformer_ctc_small") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="corygong/stt_en_conformer_ctc_small" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 KHz Mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture <ADD SOME INFORMATION ABOUT THE ARCHITECTURE> ## Training <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC> ### Datasets <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)> ## Performance <LIST THE SCORES OF THE MODEL - OR USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS> ## Limitations <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL> Eg: Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## References <ADD ANY REFERENCES HERE AS NEEDED> [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) ## Original Model Name: ABC ## Repo ID: nvidia/ABC_XYZ
MrezaPRZ/qwen2.5-Coder-7B-Instruct-sql-judge-7b-acc	MrezaPRZ	2025-04-22T21:57:37Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-22T21:57:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
fbaldassarri/ibm-granite_granite-3.3-2b-base-autoround-int4-gs64-sym	fbaldassarri	2025-04-22T21:57:36Z	0	0	transformers	[ "transformers", "safetensors", "granite", "text-generation", "granite-3.3", "autoround", "auto-round", "intel-autoround", "intel", "woq", "pytorch", "ibm", "granite-3", "en", "es", "fr", "de", "pt", "ja", "it", "zh", "ko", "ar", "cs", "nl", "base_model:ibm-granite/granite-3.3-2b-base", "base_model:quantized:ibm-granite/granite-3.3-2b-base", "license:apache-2.0", "autotrain_compatible", "4-bit", "intel/auto-round", "region:us" ]	text-generation	2025-04-22T21:56:41Z	--- language: - en - es - fr - de - pt - ja - it - zh - ko - ar - cs - nl pipeline_tag: text-generation license: apache-2.0 library_name: transformers tags: - granite-3.3 - autoround - auto-round - intel-autoround - intel - woq - pytorch - ibm - granite - granite-3 model_name: Granite 3.3 2b base base_model: - ibm-granite/granite-3.3-2b-base inference: false model_creator: ibm-granite prompt_template: '{prompt}' quantized_by: fbaldassarri --- ## Model Information Quantized version of [ibm-granite/granite-3.3-2b-base](https://huggingface.co/fbaldassarri/ibm-granite/granite-3.3-2b-base) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Symmetrical Quantization - Method WoQ (AutoRound format) Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.7 Note: this INT4 version of granite-3.3-2b-base has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz tar -xvzf v0.4.7.tar.gz cd auto-round-0.4.7 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "ibm-granite/granite-3.3-2b-base" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device = 4, 64, True, 'cpu' autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device) autoround.quantize() output_dir = "./AutoRound/ibm-granite_granite-3.3-2b-base-autoround-int4-gs64-sym" autoround.save_quantized(output_dir, format='auto_round', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
mjpsm/family_history	mjpsm	2025-04-22T21:57:20Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-22T21:55:56Z	--- 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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fbaldassarri/ibm-granite_granite-3.3-2b-instruct-autoawq-int4-gs64-sym	fbaldassarri	2025-04-22T21:54:40Z	0	0	transformers	[ "transformers", "safetensors", "granite", "text-generation", "granite-3.3", "autoround", "auto-round", "intel-autoround", "intel", "awq", "auto-awq", "autoawq", "woq", "pytorch", "ibm", "granite-3", "conversational", "en", "es", "fr", "de", "pt", "ja", "it", "zh", "ko", "ar", "cs", "nl", "base_model:ibm-granite/granite-3.3-2b-instruct", "base_model:quantized:ibm-granite/granite-3.3-2b-instruct", "license:apache-2.0", "autotrain_compatible", "4-bit", "region:us" ]	text-generation	2025-04-22T21:53:39Z	--- language: - en - es - fr - de - pt - ja - it - zh - ko - ar - cs - nl pipeline_tag: text-generation license: apache-2.0 library_name: transformers tags: - granite-3.3 - autoround - auto-round - intel-autoround - intel - awq - auto-awq - autoawq - woq - pytorch - ibm - granite - granite-3 model_name: Granite 3.3 2b instruct base_model: - ibm-granite/granite-3.3-2b-instruct inference: false model_creator: ibm-granite prompt_template: '{prompt}' quantized_by: fbaldassarri --- ## Model Information Quantized version of [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/fbaldassarri/ibm-granite/granite-3.3-2b-instruct) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Symmetrical Quantization - Method AutoAWQ format Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.7 Note: this INT4 version of granite-3.3-2b-instruct has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz tar -xvzf v0.4.7.tar.gz cd auto-round-0.4.7 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "ibm-granite/granite-3.3-2b-instruct" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device = 4, 64, True, 'cpu' autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device) autoround.quantize() output_dir = "./AutoRound/ibm-granite_granite-3.3-2b-instruct-autoawq-int4-gs64-sym" autoround.save_quantized(output_dir, format='auto_awq', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
straikerinc/Qwen2.5-VL-3B-Instruct-argus-384	straikerinc	2025-04-22T21:52:50Z	21	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-21T05:56:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dgambettaphd/M_llm3_gen4_run0_W_doc1000_synt64_tot128_SYNLAST	dgambettaphd	2025-04-22T21:52:18Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-22T21:52:02Z	--- 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. 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akseljoonas/oR1-Qwen-3B-Agentic-e14-lr5-b8-check	akseljoonas	2025-04-22T21:49:29Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:smolagents/training-traces", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T21:30:40Z	--- base_model: Qwen/Qwen2.5-3B-Instruct datasets: smolagents/training-traces library_name: transformers model_name: oR1-Qwen-3B-Agentic-e14-lr5-b8-check tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for oR1-Qwen-3B-Agentic-e14-lr5-b8-check This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [smolagents/training-traces](https://huggingface.co/datasets/smolagents/training-traces) 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="akseljoonas/oR1-Qwen-3B-Agentic-e14-lr5-b8-check", 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/akseljoonas-university-of-groningen/huggingface/runs/p5em9c84) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
fbaldassarri/ibm-granite_granite-3.3-2b-instruct-autoround-int4-gs64-sym	fbaldassarri	2025-04-22T21:48:35Z	0	0	transformers	[ "transformers", "safetensors", "granite", "text-generation", "granite-3.3", "autoround", "auto-round", "intel-autoround", "intel", "woq", "pytorch", "ibm", "granite-3", "conversational", "en", "es", "fr", "de", "pt", "ja", "it", "zh", "ko", "ar", "cs", "nl", "base_model:ibm-granite/granite-3.3-2b-instruct", "base_model:quantized:ibm-granite/granite-3.3-2b-instruct", "license:apache-2.0", "autotrain_compatible", "4-bit", "intel/auto-round", "region:us" ]	text-generation	2025-04-22T21:47:36Z	--- language: - en - es - fr - de - pt - ja - it - zh - ko - ar - cs - nl pipeline_tag: text-generation license: apache-2.0 library_name: transformers tags: - granite-3.3 - autoround - auto-round - intel-autoround - intel - woq - pytorch - ibm - granite - granite-3 model_name: Granite 3.3 2b instruct base_model: - ibm-granite/granite-3.3-2b-instruct inference: false model_creator: ibm-granite prompt_template: '{prompt}' quantized_by: fbaldassarri --- ## Model Information Quantized version of [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/fbaldassarri/ibm-granite/granite-3.3-2b-instruct) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Symmetrical Quantization - Method WoQ (AutoRound format) Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.7 Note: this INT4 version of granite-3.3-2b-instruct has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz tar -xvzf v0.4.7.tar.gz cd auto-round-0.4.7 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "ibm-granite/granite-3.3-2b-instruct" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device = 4, 64, True, 'cpu' autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device) autoround.quantize() output_dir = "./AutoRound/ibm-granite_granite-3.3-2b-instruct-autoround-int4-gs64-sym" autoround.save_quantized(output_dir, format='auto_round', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
genki10/BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold1	genki10	2025-04-22T21:46:26Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T21:26:58Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold1 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. --> # BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4079 - Qwk: 0.1994 - Mse: 1.4065 - Rmse: 1.1860 ## 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: 64 - eval_batch_size: 64 - 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: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 5 \| 7.8727 \| 0.0 \| 7.8702 \| 2.8054 \| \| No log \| 2.0 \| 10 \| 3.5683 \| 0.0079 \| 3.5664 \| 1.8885 \| \| No log \| 3.0 \| 15 \| 1.9814 \| 0.0210 \| 1.9799 \| 1.4071 \| \| No log \| 4.0 \| 20 \| 1.3103 \| 0.0 \| 1.3088 \| 1.1440 \| \| No log \| 5.0 \| 25 \| 1.6863 \| 0.0669 \| 1.6848 \| 1.2980 \| \| No log \| 6.0 \| 30 \| 0.8926 \| 0.2128 \| 0.8914 \| 0.9441 \| \| No log \| 7.0 \| 35 \| 1.9076 \| 0.1347 \| 1.9059 \| 1.3805 \| \| No log \| 8.0 \| 40 \| 0.8566 \| 0.2620 \| 0.8553 \| 0.9248 \| \| No log \| 9.0 \| 45 \| 1.0669 \| 0.1656 \| 1.0653 \| 1.0321 \| \| No log \| 10.0 \| 50 \| 1.4453 \| 0.1298 \| 1.4434 \| 1.2014 \| \| No log \| 11.0 \| 55 \| 0.7431 \| 0.3442 \| 0.7422 \| 0.8615 \| \| No log \| 12.0 \| 60 \| 1.0965 \| 0.2839 \| 1.0953 \| 1.0466 \| \| No log \| 13.0 \| 65 \| 1.0576 \| 0.3084 \| 1.0565 \| 1.0278 \| \| No log \| 14.0 \| 70 \| 0.8851 \| 0.3658 \| 0.8840 \| 0.9402 \| \| No log \| 15.0 \| 75 \| 0.8894 \| 0.3650 \| 0.8882 \| 0.9424 \| \| No log \| 16.0 \| 80 \| 1.1155 \| 0.2604 \| 1.1141 \| 1.0555 \| \| No log \| 17.0 \| 85 \| 1.4762 \| 0.2193 \| 1.4745 \| 1.2143 \| \| No log \| 18.0 \| 90 \| 1.2488 \| 0.2263 \| 1.2472 \| 1.1168 \| \| No log \| 19.0 \| 95 \| 1.3504 \| 0.2067 \| 1.3487 \| 1.1614 \| \| No log \| 20.0 \| 100 \| 1.5741 \| 0.1904 \| 1.5723 \| 1.2539 \| \| No log \| 21.0 \| 105 \| 1.4310 \| 0.2004 \| 1.4292 \| 1.1955 \| \| No log \| 22.0 \| 110 \| 1.4192 \| 0.1818 \| 1.4174 \| 1.1905 \| \| No log \| 23.0 \| 115 \| 0.9856 \| 0.2915 \| 0.9842 \| 0.9921 \| \| No log \| 24.0 \| 120 \| 1.2399 \| 0.2474 \| 1.2384 \| 1.1129 \| \| No log \| 25.0 \| 125 \| 1.5714 \| 0.1911 \| 1.5694 \| 1.2528 \| \| No log \| 26.0 \| 130 \| 2.0421 \| 0.1045 \| 2.0397 \| 1.4282 \| \| No log \| 27.0 \| 135 \| 1.1564 \| 0.2502 \| 1.1545 \| 1.0745 \| \| No log \| 28.0 \| 140 \| 1.8893 \| 0.1296 \| 1.8873 \| 1.3738 \| \| No log \| 29.0 \| 145 \| 1.2473 \| 0.2062 \| 1.2456 \| 1.1161 \| \| No log \| 30.0 \| 150 \| 1.2450 \| 0.2254 \| 1.2432 \| 1.1150 \| \| No log \| 31.0 \| 155 \| 1.7124 \| 0.1645 \| 1.7103 \| 1.3078 \| \| No log \| 32.0 \| 160 \| 1.7210 \| 0.1676 \| 1.7191 \| 1.3111 \| \| No log \| 33.0 \| 165 \| 1.1376 \| 0.2134 \| 1.1360 \| 1.0659 \| \| No log \| 34.0 \| 170 \| 1.3979 \| 0.1625 \| 1.3963 \| 1.1817 \| \| No log \| 35.0 \| 175 \| 1.3155 \| 0.1591 \| 1.3139 \| 1.1462 \| \| No log \| 36.0 \| 180 \| 1.3612 \| 0.1629 \| 1.3596 \| 1.1660 \| \| No log \| 37.0 \| 185 \| 1.1239 \| 0.2252 \| 1.1224 \| 1.0594 \| \| No log \| 38.0 \| 190 \| 1.3851 \| 0.1724 \| 1.3836 \| 1.1762 \| \| No log \| 39.0 \| 195 \| 1.4702 \| 0.1672 \| 1.4686 \| 1.2119 \| \| No log \| 40.0 \| 200 \| 1.3435 \| 0.1781 \| 1.3420 \| 1.1584 \| \| No log \| 41.0 \| 205 \| 1.5186 \| 0.1702 \| 1.5172 \| 1.2318 \| \| No log \| 42.0 \| 210 \| 1.3982 \| 0.1594 \| 1.3968 \| 1.1819 \| \| No log \| 43.0 \| 215 \| 1.0965 \| 0.2424 \| 1.0953 \| 1.0466 \| \| No log \| 44.0 \| 220 \| 1.4079 \| 0.1994 \| 1.4065 \| 1.1860 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
naveen1991/ppo-Huggy	naveen1991	2025-04-22T21:46:18Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-04-22T21:46:11Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: naveen1991/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
fbaldassarri/ibm-granite_granite-3.3-2b-instruct-autoround-int4-gs64-asym	fbaldassarri	2025-04-22T21:45:49Z	0	0	transformers	[ "transformers", "safetensors", "granite", "text-generation", "granite-3.3", "autoround", "auto-round", "intel-autoround", "intel", "woq", "pytorch", "ibm", "granite-3", "conversational", "en", "es", "fr", "de", "pt", "ja", "it", "zh", "ko", "ar", "cs", "nl", "base_model:ibm-granite/granite-3.3-2b-instruct", "base_model:quantized:ibm-granite/granite-3.3-2b-instruct", "license:apache-2.0", "autotrain_compatible", "4-bit", "intel/auto-round", "region:us" ]	text-generation	2025-04-22T21:44:56Z	--- language: - en - es - fr - de - pt - ja - it - zh - ko - ar - cs - nl pipeline_tag: text-generation license: apache-2.0 library_name: transformers tags: - granite-3.3 - autoround - auto-round - intel-autoround - intel - woq - pytorch - ibm - granite - granite-3 model_name: Granite 3.3 2b instruct base_model: - ibm-granite/granite-3.3-2b-instruct inference: false model_creator: ibm-granite prompt_template: '{prompt}' quantized_by: fbaldassarri --- ## Model Information Quantized version of [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/fbaldassarri/ibm-granite/granite-3.3-2b-instruct) using torch.float32 for quantization tuning. - 4 bits (INT4) - group size = 64 - Asymmetrical Quantization - Method WoQ (AutoRound format) Fast and low memory, 2-3X speedup (slight accuracy drop at W4G64) Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.7 Note: this INT4 version of granite-3.3-2b-instruct has been quantized to run inference through CPU. ## Replication Recipe ### Step 1 Install Requirements I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. ``` wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz tar -xvzf v0.4.7.tar.gz cd auto-round-0.4.7 pip install -r requirements-cpu.txt --upgrade ``` ### Step 2 Build Intel AutoRound wheel from sources ``` pip install -vvv --no-build-isolation -e .[cpu] ``` ### Step 3 Script for Quantization ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "ibm-granite/granite-3.3-2b-instruct" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) from auto_round import AutoRound bits, group_size, sym, device = 4, 64, False, 'cpu' autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device) autoround.quantize() output_dir = "./AutoRound/ibm-granite_granite-3.3-2b-instruct-autoround-int4-gs64-asym" autoround.save_quantized(output_dir, format='auto_round', inplace=True) ``` ## License [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/) ## Disclaimer This quantized model comes with no warrenty. It has been developed only for research purposes.
Bisher/train_run-Qwen2.5-0.5B-Instruct-fadel-full-arabic-diacritization	Bisher	2025-04-22T21:44:53Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-04-22T11:09:05Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - generated_from_trainer model-index: - name: train_run-Qwen2.5-0.5B-Instruct-fadel-full-arabic-diacritization 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. --> # train_run-Qwen2.5-0.5B-Instruct-fadel-full-arabic-diacritization This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0488 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.1216 \| 0.0623 \| 500 \| 0.1203 \| \| 0.0872 \| 0.1247 \| 1000 \| 0.0907 \| \| 0.072 \| 0.1870 \| 1500 \| 0.0786 \| \| 0.0662 \| 0.2493 \| 2000 \| 0.0746 \| \| 0.0667 \| 0.3117 \| 2500 \| 0.0667 \| \| 0.0635 \| 0.3740 \| 3000 \| 0.0654 \| \| 0.0626 \| 0.4364 \| 3500 \| 0.0619 \| \| 0.0606 \| 0.4987 \| 4000 \| 0.0574 \| \| 0.0612 \| 0.5610 \| 4500 \| 0.0547 \| \| 0.0557 \| 0.6234 \| 5000 \| 0.0553 \| \| 0.0548 \| 0.6857 \| 5500 \| 0.0554 \| \| 0.0516 \| 0.7480 \| 6000 \| 0.0538 \| \| 0.0475 \| 0.8104 \| 6500 \| 0.0515 \| \| 0.0515 \| 0.8727 \| 7000 \| 0.0508 \| \| 0.0513 \| 0.9350 \| 7500 \| 0.0496 \| \| 0.0542 \| 0.9974 \| 8000 \| 0.0488 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
ricemonster/deepseek-1.3B-nosave	ricemonster	2025-04-22T21:43:39Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-04-22T21:40:37Z	--- license: apache-2.0 ---
Jonjew/JoannaLumley1976	Jonjew	2025-04-22T21:40:26Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-04-22T21:40:09Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- <lora:purdeyv01:1> a woman, purdey01, walking in a park with the sunset behind her. She wear a class heighties outfit. output: url: images/00000-3264230134.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: purdey01 license: unknown --- # Purdey (Joanna Lumley - 1976) by guyvdw001833 <Gallery /> ## Model description FROM https://civitai.com/models/1499324/purdey-joanna-lumley-1976?modelVersionId=1696075 Please support the creator by donating buzz and liking at the page above! Trigger purdey01 Purdey is a fictional character in the British television series The Avengers, portrayed by English actress Joanna Lumley. The seventh partner of Constable John Steed (and fifth female partner), she appears in seasons 7 and 8 (color) of the series, appearing in 26 episodes. ## Trigger words You should use `purdey01` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/JoannaLumley1976/tree/main) them in the Files & versions tab.
Hartunka/distilbert_rand_10_v2_wnli	Hartunka	2025-04-22T21:37:43Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:Hartunka/distilbert_rand_10_v2", "base_model:finetune:Hartunka/distilbert_rand_10_v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T21:37:15Z	--- library_name: transformers language: - en base_model: Hartunka/distilbert_rand_10_v2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_rand_10_v2_wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.4225352112676056 --- <!-- 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. --> # distilbert_rand_10_v2_wnli This model is a fine-tuned version of [Hartunka/distilbert_rand_10_v2](https://huggingface.co/Hartunka/distilbert_rand_10_v2) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.7056 - Accuracy: 0.4225 ## 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-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.716 \| 1.0 \| 3 \| 0.7056 \| 0.4225 \| \| 0.7053 \| 2.0 \| 6 \| 0.7251 \| 0.2817 \| \| 0.6934 \| 3.0 \| 9 \| 0.7280 \| 0.4507 \| \| 0.6861 \| 4.0 \| 12 \| 0.7464 \| 0.3239 \| \| 0.6893 \| 5.0 \| 15 \| 0.7720 \| 0.2394 \| \| 0.6942 \| 6.0 \| 18 \| 0.7827 \| 0.3099 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.21.1
mlx-community/Llama-OuteTTS-1.0-1B-bf16	mlx-community	2025-04-22T21:37:29Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-to-speech", "en", "ar", "zh", "nl", "fr", "de", "it", "ja", "ko", "lt", "ru", "es", "pt", "be", "bn", "ka", "hu", "lv", "fa", "pl", "sw", "ta", "uk", "license:cc-by-nc-sa-4.0", "region:us" ]	text-to-speech	2025-04-22T21:22:01Z	--- license: cc-by-nc-sa-4.0 language: - en - ar - zh - nl - fr - de - it - ja - ko - lt - ru - es - pt - be - bn - ka - hu - lv - fa - pl - sw - ta - uk pipeline_tag: text-to-speech tags: - mlx --- # mlx-community/Llama-OuteTTS-1.0-1B-bf16 This model was converted to MLX format from [`OuteAI/Llama-OuteTTS-1.0-1B`](https://huggingface.co/OuteAI/Llama-OuteTTS-1.0-1B) using mlx-audio version 0.1.0. Refer to the [original model card](https://huggingface.co/OuteAI/Llama-OuteTTS-1.0-1B) for more details on the model. ## Use with mlx ```bash pip install -U mlx-audio ``` ```bash python -m mlx_audio.tts.generate --model mlx-community/Llama-OuteTTS-1.0-1B-bf16 --text "Describe this image." ```
Hartunka/distilbert_rand_10_v2_stsb	Hartunka	2025-04-22T21:37:09Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:Hartunka/distilbert_rand_10_v2", "base_model:finetune:Hartunka/distilbert_rand_10_v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T21:35:41Z	--- library_name: transformers language: - en base_model: Hartunka/distilbert_rand_10_v2 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: distilbert_rand_10_v2_stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.3015093929726957 --- <!-- 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. --> # distilbert_rand_10_v2_stsb This model is a fine-tuned version of [Hartunka/distilbert_rand_10_v2](https://huggingface.co/Hartunka/distilbert_rand_10_v2) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2101 - Pearson: 0.3094 - Spearmanr: 0.3015 - Combined Score: 0.3055 ## 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-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\|:--------------:\| \| 2.7267 \| 1.0 \| 23 \| 2.4802 \| 0.1260 \| 0.1086 \| 0.1173 \| \| 1.9118 \| 2.0 \| 46 \| 2.4211 \| 0.2286 \| 0.2110 \| 0.2198 \| \| 1.6277 \| 3.0 \| 69 \| 2.2101 \| 0.3094 \| 0.3015 \| 0.3055 \| \| 1.3088 \| 4.0 \| 92 \| 2.2704 \| 0.3073 \| 0.3050 \| 0.3062 \| \| 1.0113 \| 5.0 \| 115 \| 2.4404 \| 0.3233 \| 0.3195 \| 0.3214 \| \| 0.7442 \| 6.0 \| 138 \| 2.2811 \| 0.3766 \| 0.3775 \| 0.3770 \| \| 0.5763 \| 7.0 \| 161 \| 2.3778 \| 0.3448 \| 0.3449 \| 0.3449 \| \| 0.4382 \| 8.0 \| 184 \| 2.5305 \| 0.3544 \| 0.3537 \| 0.3541 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.21.1
hadimian/Smart-TA	hadimian	2025-04-22T21:34:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T21:28:19Z	--- 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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Bedovyy/c4ai-command-a-03-2025-gptqmodel-4bit	Bedovyy	2025-04-22T21:34:50Z	10	0	transformers	[ "transformers", "safetensors", "cohere2", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "arxiv:2504.00698", "base_model:CohereLabs/c4ai-command-a-03-2025", "base_model:quantized:CohereLabs/c4ai-command-a-03-2025", "license:cc-by-nc-4.0", "autotrain_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2025-04-11T07:29:16Z	--- inference: false library_name: transformers 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 extra_gated_prompt: >- By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and acknowledge that the information you provide will be collected, used, and shared in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy). You’ll receive email updates about C4AI and Cohere research, events, products and services. You can unsubscribe at any time. extra_gated_fields: Name: text Affiliation: text Country: country I agree to use this model for non-commercial use ONLY: checkbox base_model: - CohereLabs/c4ai-command-a-03-2025 --- # GPTQ quantization of Command-A I made this for running on vLLM. Non-english performance may be significantly dropped. Recommend to set `temperature` to 0.6~0.8. ## Quantization method Quantized using - Tool: [GPTQModel 2.3.0-dev (bafda24)](https://github.com/ModelCloud/GPTQModel/commit/bafda2489f9582b2cdbf6a5fb8b242aa38a02cda). - System: 2x3090, DDR4 128GB + swap 192GB - Time taken: 14 hours (wall time) ```py import sys from datasets import load_dataset, concatenate_datasets from gptqmodel import GPTQModel, QuantizeConfig model_id = sys.argv[1] quant_path = sys.argv[2] calibration_dataset = load_dataset( "allenai/c4", data_files={"train": "en/c4-train.00001-of-01024.json.gz"}, split="train", ).select(range(2048))["text"] quant_config = QuantizeConfig( bits=4, group_size=128, parallel_packing=False, ) model = GPTQModel.load( model_id, quant_config, ) model.quantize( calibration_dataset, batch_size=1, calibration_enable_gpu_cache=False ) model.save(quant_path) ``` --- # Model Card for C4AI Command A ## Model Summary C4AI Command A is an open weights research release of a 111 billion parameter model optimized for demanding enterprises that require fast, secure, and high-quality AI. Compared to other leading proprietary and open-weights models Command A delivers maximum performance with minimum hardware costs, excelling on business-critical agentic and multilingual tasks while‬ being deployable on just two GPUs. Developed by: [Cohere](https://cohere.com/) and [Cohere For AI](https://cohere.for.ai/) * Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) * License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) * Model: c4ai-command-a-03-2025 * Model Size: 111 billion parameters * Context length: 256K Note: The model supports a context length of 256K but it is configured in Hugging Face for 128K. This value can be updated in the configuration if needed. Try C4AI Command A You can try out C4AI Command A before downloading the weights in our hosted [Hugging Face Space](https://cohereforai-c4ai-command.hf.space/models/command-a-03-2025). Usage Please install transformers from the source repository that includes the necessary changes for this model. ```py # pip install transformers from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/c4ai-command-a-03-2025" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the c4ai-command-a-03-2025 chat template messages = [{"role": "user", "content": "Hello, how are you?"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety. The model features three layers with sliding window attention (window size 4096\) and RoPE for efficient local context modeling and relative positional encoding. A fourth layer uses global attention without positional embeddings, enabling unrestricted token interactions across the entire sequence. Languages covered: The model has been trained on 23 languages: English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Arabic, Chinese, Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian. Context Length: Command A supports a context length of 256K. ### ### Chat Capabilities: By default, Command A is configured as a conversational model. A preamble conditions the model on interactive behaviour, meaning it is expected to reply in a conversational fashion, provides introductory statements and follow-up questions, and uses Markdown as well as LaTeX where appropriate. This is desired for interactive experiences, such as chatbots, where the model engages in dialogue. In other use cases, a non-interactive model behavior might be more desired (e.g. task-focused use cases like extracting information, summarizing text, translation, and categorization). Learn how system messages can be used to achieve such non-interactive behavior [here](https://docs.cohere.com/docs/command-a-hf#obtaining-non-interactive-behavior). Besides, Command A can be configured with two safety modes, which enable users to set guardrails that are both safe and suitable to their needs: contextual mode, or strict mode. Contextual mode is appropriate for wide-ranging interactions with fewer constraints on output, while maintaining core protections by rejecting harmful or illegal suggestions. Command A is configured to contextual mode by default. Strict mode aims to avoid all sensitive topics, such as violent or sexual acts and profanity. For more information, see the [Command A prompt format docs](https://docs.cohere.com/docs/command-a-hf). ### ### RAG Capabilities: Command A has been trained specifically for tasks like the final step of Retrieval Augmented Generation (RAG). RAG with Command A is supported through [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-retrieval-augmented-generation) in Transformers. The model takes a conversation as input (with an optional user-supplied system preamble), along with a list of document snippets. <details> <summary><b>RAG Example [CLICK TO EXPAND]</b></summary> ```py # Define conversation input conversation = [{"role": "user", "content": "What has Man always dreamed of?"}] # Define documents for retrieval-based generation documents = [ {"heading": "The Moon: Our Age-Old Foe", "body": "Man has always dreamed of destroying the moon. In this essay, I shall..."}, {"heading": "Love is all you need", "body": "Man's dream has always been to find love. This profound lesson..."}, ] # Get the RAG prompt input_prompt = tokenizer.apply_chat_template( conversation=conversation, documents=documents, tokenize=False, add_generation_prompt=True, return_tensors="pt", ) # Tokenize the prompt input_ids = tokenizer.encode_plus(input_prompt, return_tensors="pt") ``` You can then generate text from this input as normal. Document snippets should be short chunks, rather than long documents, typically around 100-400 words per chunk, formatted as key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured. You may find that simply including relevant documents directly in a user message works just as well, or better than using the documents parameter to render the special RAG template. The RAG template is generally a strong default and is ideal for users wanting citations. We encourage users to play with both, and to evaluate which mode works best for their specific use case. </details> Note that this was a very brief introduction to RAG \- for more information, see the Command A prompt format docs and the Transformers [RAG documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-retrieval-augmented-generation). <details> <summary><b>RAG with citations [CLICK TO EXPAND]</b></summary> Optionally, one can ask the model to include grounding spans (citations) in its response to indicate the source of the information. The code is the same as before, except for this line. ```py # Get the Grounded Generation prompt, with citations input_prompt = tokenizer.apply_chat_template( conversation=conversation, documents=documents, tokenize=False, add_generation_prompt=True, return_tensors="pt", enable_citations=True, ) # There are two answers to this question. Man has dreamed of <co>destroying the moon</co: 0:[0]> and <co>finding love.</co: 0:[1]> ``` The output looks like this: the model will associate pieces of texts (called "spans") with specific document snippets that support them (called "sources"). Command A uses a pair of tags "\<co\>" and "\</co\>" to indicate when a span can be grounded onto a list of sources. For example, "\<co\>span\</co: 0:\[0,1\]\>" means that "span" is supported by documents snippets 0 and 1 that were provided in the last message. </details> ### Tool Use Capabilities: Command A has been specifically trained with conversational tool use capabilities. This allows the model to interact with external tools like APIs, databases, or search engines. Tool use with Command A is supported through [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling) in Transformers. We recommend providing tool descriptions using JSON schema. <details> <summary><b>Tool Use Example [CLICK TO EXPAND]</b></summary> ```py # Define tools tools = [{ "type": "function", "function": { "name": "query_daily_sales_report", "description": "Connects to a database to retrieve overall sales volumes and sales information for a given day.", "parameters": { "type": "object", "properties": { "day": { "description": "Retrieves sales data for this day, formatted as YYYY-MM-DD.", "type": "string", } }, "required": ["day"] }, } }] # Define conversation input conversation = [{"role": "user", "content": "Can you provide a sales summary for 29th September 2023?"}] # Get the Tool Use prompt input_prompt = tokenizer.apply_chat_template(conversation=conversation, tools=tools, tokenize=False, add_generation_prompt=True, return_tensors="pt")) # Tokenize the prompt input_ids = tokenizer.encode_plus(input_prompt, return_tensors="pt") ``` You can then generate from this input as normal. If the model generates a plan and tool calls, you should add them to the chat history like so: ```py tool_call = {"name": "query_daily_sales_report", "arguments": {"day": "2023-09-29"}} tool_plan = "I will use the query_daily_sales_report tool to find the sales summary for 29th September 2023." conversation.append({"role": "assistant", "tool_calls": [{"id": "0", "type": "function", "function": tool_call}], "tool_plan": tool_plan}) ``` and then call the tool and append the result, as a dictionary, with the tool role, like so: ```py api_response_query_daily_sales_report = {"date": "2023-09-29", "summary": "Total Sales Amount: 10000, Total Units Sold: 250"} # this needs to be a dictionary!! # Append tool results conversation.append({"role": "tool", "tool_call_id": "0", "content": api_response_query_daily_sales_report}) ``` After that, you can generate() again to let the model use the tool result in the chat. </details> Note that this was a very brief introduction to tool calling \- for more information, see [the Command A prompt format docs](https://docs.cohere.com/docs/command-a-hf&sa=D&source=docs&ust=1741857329583678&usg=AOvVaw3sS-2eIfLzShS6c9VWXJWa) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling). <details> <summary><b>Tool Use with citations [CLICK TO EXPAND]</b></summary> Optionally, one can ask the model to include grounding spans (citations) in its response to indicate the source of the information, by using enable\_citations=True in tokenizer.apply\_chat\_template(). The generation would look like this: ``` On 29th September 2023, the total sales amount was <co>10000</co: 0:[0]> and the total units sold were <co>250.</co: 0:[0]> ``` When citations are turned on, the model associates pieces of texts (called "spans") with those specific tool results that support them (called "sources"). Command A uses a pair of tags "\<co\>" and "\</co\>" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. For example, "\<co\>span\</co: 0:\[1,2\],1:\[0\]\>" means that "span" is supported by result 1 and 2 from "tool\_call\_id=0" as well as result 0 from "tool\_call\_id=1". Sources from the same tool call are grouped together and listed as "{tool\_call\_id}:\[{list of result indices}\]", before they are joined together by ",". </details> ### ### Code Capabilities: Command A has meaningfully improved on code capabilities. In addition to academic code benchmarks, we have evaluated it on enterprise-relevant scenarios, including SQL generation and code translation, where it outperforms other models of similar size. Try these out by requesting code snippets, code explanations, or code rewrites. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions. ## Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ## Terms of Use: We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 111 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License (Non-Commercial) with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)If you are interested in commercial use, please contact [Cohere’s Sales team](https://cohere.com/contact-sales). ## Try Chat: You can try Command A chat in the playground [here](https://dashboard.cohere.com/playground/chat?model=command-a-03-2025). You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/c4ai-command). ## Citation: ``` @misc{cohere2025commandaenterprisereadylarge, title={Command A: An Enterprise-Ready Large Language Model}, author={Team Cohere and Aakanksha and Arash Ahmadian and Marwan Ahmed and Jay Alammar and Yazeed Alnumay and Sophia Althammer and Arkady Arkhangorodsky and Viraat Aryabumi and Dennis Aumiller and Raphaël Avalos and Zahara Aviv and Sammie Bae and Saurabh Baji and Alexandre Barbet and Max Bartolo and Björn Bebensee and Neeral Beladia and Walter Beller-Morales and Alexandre Bérard and Andrew Berneshawi and Anna Bialas and Phil Blunsom and Matt Bobkin and Adi Bongale and Sam Braun and Maxime Brunet and Samuel Cahyawijaya and David Cairuz and Jon Ander Campos and Cassie Cao and Kris Cao and Roman Castagné and Julián Cendrero and Leila Chan Currie and Yash Chandak and Diane Chang and Giannis Chatziveroglou and Hongyu Chen and Claire Cheng and Alexis Chevalier and Justin T. Chiu and Eugene Cho and Eugene Choi and Eujeong Choi and Tim Chung and Volkan Cirik and Ana Cismaru and Pierre Clavier and Henry Conklin and Lucas Crawhall-Stein and Devon Crouse and Andres Felipe Cruz-Salinas and Ben Cyrus and Daniel D'souza and Hugo Dalla-Torre and John Dang and William Darling and Omar Darwiche Domingues and Saurabh Dash and Antoine Debugne and Théo Dehaze and Shaan Desai and Joan Devassy and Rishit Dholakia and Kyle Duffy and Ali Edalati and Ace Eldeib and Abdullah Elkady and Sarah Elsharkawy and Irem Ergün and Beyza Ermis and Marzieh Fadaee and Boyu Fan and Lucas Fayoux and Yannis Flet-Berliac and Nick Frosst and Matthias Gallé and Wojciech Galuba and Utsav Garg and Matthieu Geist and Mohammad Gheshlaghi Azar and Seraphina Goldfarb-Tarrant and Tomas Goldsack and Aidan Gomez and Victor Machado Gonzaga and Nithya Govindarajan and Manoj Govindassamy and Nathan Grinsztajn and Nikolas Gritsch and Patrick Gu and Shangmin Guo and Kilian Haefeli and Rod Hajjar and Tim Hawes and Jingyi He and Sebastian Hofstätter and Sungjin Hong and Sara Hooker and Tom Hosking and Stephanie Howe and Eric Hu and Renjie Huang and Hemant Jain and Ritika Jain and Nick Jakobi and Madeline Jenkins and JJ Jordan and Dhruti Joshi and Jason Jung and Trushant Kalyanpur and Siddhartha Rao Kamalakara and Julia Kedrzycki and Gokce Keskin and Edward Kim and Joon Kim and Wei-Yin Ko and Tom Kocmi and Michael Kozakov and Wojciech Kryściński and Arnav Kumar Jain and Komal Kumar Teru and Sander Land and Michael Lasby and Olivia Lasche and Justin Lee and Patrick Lewis and Jeffrey Li and Jonathan Li and Hangyu Lin and Acyr Locatelli and Kevin Luong and Raymond Ma and Lukas Mach and Marina Machado and Joanne Magbitang and Brenda Malacara Lopez and Aryan Mann and Kelly Marchisio and Olivia Markham and Alexandre Matton and Alex McKinney and Dominic McLoughlin and Jozef Mokry and Adrien Morisot and Autumn Moulder and Harry Moynehan and Maximilian Mozes and Vivek Muppalla and Lidiya Murakhovska and Hemangani Nagarajan and Alekhya Nandula and Hisham Nasir and Shauna Nehra and Josh Netto-Rosen and Daniel Ohashi and James Owers-Bardsley and Jason Ozuzu and Dennis Padilla and Gloria Park and Sam Passaglia and Jeremy Pekmez and Laura Penstone and Aleksandra Piktus and Case Ploeg and Andrew Poulton and Youran Qi and Shubha Raghvendra and Miguel Ramos and Ekagra Ranjan and Pierre Richemond and Cécile Robert-Michon and Aurélien Rodriguez and Sudip Roy and Laura Ruis and Louise Rust and Anubhav Sachan and Alejandro Salamanca and Kailash Karthik Saravanakumar and Isha Satyakam and Alice Schoenauer Sebag and Priyanka Sen and Sholeh Sepehri and Preethi Seshadri and Ye Shen and Tom Sherborne and Sylvie Chang Shi and Sanal Shivaprasad and Vladyslav Shmyhlo and Anirudh Shrinivason and Inna Shteinbuk and Amir Shukayev and Mathieu Simard and Ella Snyder and Ava Spataru and Victoria Spooner and Trisha Starostina and Florian Strub and Yixuan Su and Jimin Sun and Dwarak Talupuru and Eugene Tarassov and Elena Tommasone and Jennifer Tracey and Billy Trend and Evren Tumer and Ahmet Üstün and Bharat Venkitesh and David Venuto and Pat Verga and Maxime Voisin and Alex Wang and Donglu Wang and Shijian Wang and Edmond Wen and Naomi White and Jesse Willman and Marysia Winkels and Chen Xia and Jessica Xie and Minjie Xu and Bowen Yang and Tan Yi-Chern and Ivan Zhang and Zhenyu Zhao and Zhoujie Zhao}, year={2025}, eprint={2504.00698}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.00698}, } ```
Jonjew/FullMetalLadyArmorStyleMk1	Jonjew	2025-04-22T21:32:22Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-04-22T21:30:38Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- a woman in pure white armor with mirrored surfaces reflecting a crowd in a grand hall, her posture proud and untouchable — photorealistic, high detail, regal energy fmlas-mk.1, R3alisticF output: url: images/white armor.png - text: >- A dwarven battle queen stands proudly in her mountain fortress, her armor a masterwork of dark iron and gold inlays. Her battle-worn plates are adorned with fierce lion motifs, the symbols of her clan. Her broad shoulders are covered with a thick fur cloak, and her heavy, steel greaves glimmer with age-old craftsmanship. The queen grips a warhammer, her weapon of choice, as she surveys her dwarven stronghold, the heart of the mountain. The backdrop shows the great halls of her fortress, with warm, golden torchlight reflecting off the stone walls. Her short, fiery red hair is framed by her metal helmet, and her determined expression exudes strength and leadership. photorealistic, dwarven queen, battle-worn armor, dark iron and gold, lion motifs, warhammer, fur-lined cloak, mountain fortress, stronghold interior, warm lighting, determined expression, leadership fmlas-mk.1, R3alisticF output: url: images/grey.png - text: '-' output: url: images/Epochtester_00019_.png - text: '-' output: url: images/Epochtester_00005_.png - text: '-' output: url: images/Epochtester_00006_.png - text: '-' output: url: images/Epochtester_00008_.png - text: '-' output: url: images/Epochtester_00010_.png - text: '-' output: url: images/Epochtester_00011_.png - text: '-' output: url: images/Epochtester_00013_.png - text: '-' output: url: images/Epochtester_00016_.png - text: '-' output: url: images/Epochtester_00022_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: fmlas-mk.1 license: unknown --- # FullMetalLady - Armor Style Mark 1 by Razane <Gallery /> ## Model description FROM https://civitai.com/models/1478307/fullmetallady-armor-style?modelVersionId=1672136 Please support the creator by donating buzz and liking at the page above! Trigger fmlas-mk.1 🎶 FullMetalLadyArmorStyle Mk.1 A LoRA forged in lust, leather, and legendary steel Trigger word: fmlas-mk.1 “She strode in — breastplate gleaming, hips swaying, and not a damn man in sight. The bards wept. The dragons swooned. The armor? Oh, it fit like a prayer answered by steel.” — Last words of Bardorin the Enamored ⚠️ DISCLAIMER: This LoRA has a strong signature style and can easily dominate other LoRAs in a mix. If you're combining it with other LoRAs, set FullMetalLadyArmorStyle-Mk.1 to a lower strength — around 0.4 to 0.7 — to keep things balanced and prevent it from overpowering the blend. Tweak as needed depending on your prompt and goals. 🔧🔥 💋 What in the Nine Realms Is This? This magical artifact of a LoRA was forged for one purpose: To make sexy, badass armor for equally sexy, badass warrior women. No boxy tin cans here. No clunky medieval relics. This baby gives you elegant, powerful, fantasy-style full metal lady armor that hugs curves, glints like it’s been kissed by the gods, and looks like it was made by a legendary blacksmith who took one look at a gorgeous woman and said, “Yes. That. But with more cleavage.” To activate this enchantment, whisper the trigger word: fmlas-mk.1 🧙‍♀️ Training Rituals & Forbidden Knowledge 100% Women. This is a ladies-only saga. If you're hoping for a man in shining armor... he’ll have to sit this one out. Faces? We tossed in a mix of beautiful women from all over — Swedish, Japanese, Korean, British, Mexican, etc. — but this LoRA doesn’t lock down faces. It’s armor-first, baby. Want a perfect face? Summon your beauty LoRA or cast an inpainting spell. 🩸 Known Curses (aka "quirks") Metal nipples — yep, they’re like goblins at a tavern: unwanted but persistent at high strength. May bless or mess with your backgrounds — lighting, composition, detail, etc. Stronger LoRA = stronger effects = possibly your scene looking like a heavy metal album cover. Works best with Flux model only — trying it elsewhere is like putting a dragon saddle on a pig. You can do it... but why? 🔧 Bard’s Settings (use these or perish) Sampler: Euler Scheduler: Beta CFG: 2.3 to 3.0 (sweetest note is 2.5) Steps: 30 to 60 (40 hits the harmony just right) 🎛️ LoRA Strength Mood Guide 0.25–0.50 → Gentle caress of steel. Pretty armor, lovely lighting, still realistic. 0.75+ → Behold! All hell breaks loose. Lighting blooms, details explode, realism takes a vacation. Armor turns epic. Backgrounds glow. Your warrior becomes a goddess painted by divine hands. ⚠️ At 0.80 and up, the realism dips. You may start hearing guitars wail softly in the distance. 📸 Visual Spellbook See included examples for 25% / 50% / 75% / 100% strength. Yes, I tested them. No, I’m not okay. Yes, it was worth it. 🍻 A Toast to You, Brave Adventurer! If this LoRA made your warrior women shine a little brighter (and sexier), leave a like, drop a buzz, or download it thrice and shout “By the Nine, this is glorious!” Your support is the magic potion that fuels more madness like this. So thanks for stopping by, brave knight of the prompt — may your generations be ever epic, and your armor always snug in the right places. ## Trigger words You should use `fmlas-mk.1` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/FullMetalLadyArmorStyleMk1/tree/main) them in the Files & versions tab.
B2gan/BertDistance-pt-1024-4096-24-16	B2gan	2025-04-22T21:30:59Z	0	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-22T17:34:09Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Hartunka/distilbert_rand_10_v2_rte	Hartunka	2025-04-22T21:28:47Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:Hartunka/distilbert_rand_10_v2", "base_model:finetune:Hartunka/distilbert_rand_10_v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T21:28:04Z	--- library_name: transformers language: - en base_model: Hartunka/distilbert_rand_10_v2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_rand_10_v2_rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.48014440433212996 --- <!-- 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. --> # distilbert_rand_10_v2_rte This model is a fine-tuned version of [Hartunka/distilbert_rand_10_v2](https://huggingface.co/Hartunka/distilbert_rand_10_v2) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7072 - Accuracy: 0.4801 ## 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-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7299 \| 1.0 \| 10 \| 0.7072 \| 0.4801 \| \| 0.6804 \| 2.0 \| 20 \| 0.7083 \| 0.5126 \| \| 0.6441 \| 3.0 \| 30 \| 0.7338 \| 0.5487 \| \| 0.5634 \| 4.0 \| 40 \| 0.8555 \| 0.5054 \| \| 0.434 \| 5.0 \| 50 \| 0.9623 \| 0.5379 \| \| 0.3059 \| 6.0 \| 60 \| 1.2856 \| 0.5307 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.21.1
ad6398/colqwen-mpdocvqa-21-4-1k	ad6398	2025-04-22T21:28:27Z	0	0	null	[ "safetensors", "base_model:Qwen/Qwen2.5-VL-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct", "region:us" ]	null	2025-04-21T13:55:35Z	--- base_model: - Qwen/Qwen2.5-VL-3B-Instruct - vidore/colqwen2.5-v0.2 --- Finetune on MPDocVQA for one epoch with following parameters on H200 QUANTIZATION_STRATEGY ="bf16" SEED = 42 # Training hyperparameters EPOCHS = 1 BATCH_SIZE_TRAIN = 16 BATCH_SIZE_EVAL = 16 GRADIENT_ACCUM_STEPS = 2 WARMUP_STEPS = 20 LEARNING_RATE = 5e-5 SAVE_STEPS = 100 EVAL_STEPS = 10 LOGGING_STEPS = 1 SAVE_TOTAL_LIMIT = 5 REPORT_TO = ["wandb"] GRADIENT_CHECKPOINTING = False EVAL_STRATEGY = "steps" [Find training logs here](https://wandb.ai/ak11089/my-ms-thesis/runs/bnyuni4o?nw=nwuserak11089)
Amitkrdas386/amitkrdas	Amitkrdas386	2025-04-22T21:28:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-22T21:28:01Z	--- license: apache-2.0 ---
Flo0620/Qwen2_5_7B_r8_a8_d0_1	Flo0620	2025-04-22T21:27:25Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-22T18:12:16Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5_7B_r8_a8_d0_1 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5_7B_r8_a8_d0_1 This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-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="Flo0620/Qwen2_5_7B_r8_a8_d0_1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
alejandroajhr/dqn-SpaceInvadersNoFrameskip-v4	alejandroajhr	2025-04-22T21:25:51Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-22T21:25:07Z	--- 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: 730.50 +/- 256.08 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 alejandroajhr -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 alejandroajhr -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 alejandroajhr ``` ## 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', 0.0001), ('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'} ```
Soorajkumar36/butterflies-exp1	Soorajkumar36	2025-04-22T21:22:46Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-04-22T21:20:04Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Soorajkumar36/butterflies-exp1') image = pipeline().images[0] image ```
xw17/Llama-3.2-1B-Instruct_finetuned_3_optimized1	xw17	2025-04-22T21:22:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T21:20:38Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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amin2231/HISOKA-AI	amin2231	2025-04-22T21:21:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-22T21:21:42Z	--- license: apache-2.0 ---
edwindn/whisper-tags-finetuned	edwindn	2025-04-22T21:21:35Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-22T21:18:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Jonjew/gemst0ned	Jonjew	2025-04-22T21:21:34Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-04-22T21:21:16Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- A resplendent feminine entity emerges from the fusion of exquisite minerals and gemstones, each contributing its unique beauty to her form. Her skin is a tapestry of shimmering Morganite, its pink to orange-pink hues casting a warm, gentle glow across her surface. The hexagonal prismatic crystals of Morganite create a delicate, faceted texture, reflecting light with a vitreous luster that dances across her form. Her eyes, a mesmerizing array of Alexandrite, shift colors from a deep forest green in daylight to a rich, incandescent red under softer lighting, embodying the magic of transformation. The orthorhombic prismatic crystals of Alexandrite lend an enchanting depth to her gaze, occasionally catching the light with a cat's eye effect that adds a mysterious allure. Her hair cascades like a river of Tanzanite, its vibrant blue to violet hues flowing in prismatic waves. The orthorhombic crystals of Tanzanite exhibit a strong trichroism, shifting between blue, purple, and bronze, creating a dynamic interplay of colors with every movement. Her attire is woven from the rich, royal blue of Sodalite, interspersed with white calcite veining that adds a striking contrast. The cubic crystal system of Sodalite manifests in a massive form, with a greasy to vitreous luster that enhances her ethereal presence. The backdrop is a celestial expanse of Larimar, sky-blue hues blending with green-blue tones to form a tranquil, serene environment. The triclinic crystal system of Larimar, with its vitreous to silky luster, creates a soft, inviting atmosphere, punctuated by white streaks and clouds that drift lazily across the scene. This surreal entity, encrusted with jewels and precious minerals, stands as a testament to nature's artistry, her form a brilliant mosaic of color and light, each element harmonizing to create a hyperrealistic vision of beauty and wonder. output: url: images/gemst0ned_e000025_01_20250421224011.png - text: >- An extremely super hyper detailed hyperrealistic weird surreal trippy psychedelic entity emerges from the depths of imagination, adorned with lots and lots of crazy psychedelic compound human eyes, meticulously placed in a mesmerizing Penrose Tiling pattern. This non-repeating, intricate design creates an endless array of eye shapes and colors, each eye pulsating with vibrant hues, mirroring the mysteries of the universe. Rows upon rows of psychedelic teeth form a surreal smile that stretches infinitely, each tooth a fractal of swirling colors and patterns, echoing the chaotic beauty of the Mandelbrot Set. The entity's skin is a cosmic tapestry woven from the microscopic elegance of Ammonia tepida's spiral shells, and the shimmering, iridescent beauty of C60 Buckminsterfullerene structures. These elements create a surface that is both smooth and textured, reflecting light in unpredictable, kaleidoscopic ways. The entity's form is further embellished with the quantum mystique of Quantum Foam, giving it an ethereal, ever-changing silhouette that flickers between dimensions. Incorporating elements from the mineral world, the entity's core glows with the translucent brilliance of Quartz, casting prismatic rainbows across its surroundings. The metallic sheen of Pyrite veins runs through its body, adding a touch of ancient, earthy solidity to its otherwise fluid form. Fluorite's vivid colors dance across its surface, shifting with every movement, creating an ever-evolving display of spectral wonder. The background scene is a celestial landscape, a fractal forest of Menger Sponges rising from a ground patterned with Voronoi Tiling, evoking the organic chaos of nature. Above, a sky filled with spiraling Fibonacci sequences stretches into infinity, each spiral a gateway to another realm. This dreamscape is bathed in the soft glow of distant stars, their light refracted through the entity's crystalline elements, illuminating the scene with a serene, otherworldly radiance. This psychedelic entity stands as a testament to the boundless creativity of the cosmos, a living tapestry of mathematical precision, natural beauty, and quantum enigma, inviting all who gaze upon it to explore the infinite possibilities of the universe. output: url: images/gemst0ned_e000025_02_20250421224023.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: gemstoned license: unknown --- # gemst0ned by merrypranxter <Gallery /> ## Model description FROM https://civitai.com/models/1494165/gemst0ned?modelVersionId=1690261 Please support the creator by donating buzz and liking at the page above! Trigger gemstoned ## Trigger words You should use `gemstoned` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/gemst0ned/tree/main) them in the Files & versions tab.
xw17/Llama-3.2-1B-Instruct_finetuned_2_optimized1	xw17	2025-04-22T21:16:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T21:14:47Z	--- library_name: transformers tags: - trl - sft --- # 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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HehealthVision/PenileScreen-ViT	HehealthVision	2025-04-22T21:15:37Z	0	0	null	[ "image-classification", "en", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:creativeml-openrail-m", "region:us" ]	image-classification	2025-04-16T17:36:33Z	--- license: creativeml-openrail-m language: - en metrics: - accuracy base_model: - google/vit-base-patch16-224-in21k pipeline_tag: image-classification --- # PenileScreen-ViT > Built upon: > ➤ [The Development and Performance of a Machine‑Learning Based Mobile Platform for Visually Determining the Etiology of 5 Penile Diseases](https://www.mcpdigitalhealth.org/article/S2949-7612(24)00035-X/fulltext) — Allan‑Blitz LT, Ambepitiya S, Tirupathi R, & Klausner JD. Digital Health, 2024. > (Implementation and adaptation by our team.) A Vision Transformer-based model for multi-class classification of penile-region dermatological images, focusing on visual patterns commonly associated with sexually transmitted conditions. Developed for research, academic study, and digital health tool prototyping. ## 🧠 Model Overview The PenileScreen-ViT model categorizes input images into the following three visual classes: - `Genital_warts` - `HSV (Herpes Simplex Virus)` - `Syphilis` It is fine-tuned from `google/vit-base-patch16-224-in21k` using the TensorFlow and `vit-keras` frameworks and trained on a curated collection of de-identified dermatological images for academic and analytical purposes. ## 📦 Model Metadata \| Field \| Value \| \|---------------------\|----------------------------------------------------------\| \| License \| CreativeML Open RAIL-M \| \| Base model \| `google/vit-base-patch16-224-in21k` \| \| Model type \| Vision Transformer (ViT-B16) \| \| Pipeline tag \| `image-classification` \| \| Trained by \| Yudara Kularathne, Janitha Prathapa, Thanveer Ahamad \| \| Repository \| [GitHub Repo](https://github.com/HH-Care/Penile-Screen-ViT) \| \| Demo \| Available on request \| ## 🧠 Model Architecture This project uses: - ViT-B16 pre-trained on ImageNet21k - Custom classification head: `Flatten -> Dense(3, softmax)` - Fine-tuned on a specialized, de-identified dataset of penile-region dermatological images - Trained with educational and research use cases in mind ## 🎯 Purpose and Use This model is intended for: - Academic and AI research in visual pattern recognition - Development of experimental digital health tools - Exploration of visual features associated with selected STD-related dermatological cases - Educational visualization in the field of medical AI and image classification > ❗ This model is not intended for clinical use, diagnostic support, or real-world patient decision-making. ## 👨‍💻 Authors - Janitha Prathapa - Yudara Kularathne - Thanveer Ahamad ## 📬 License This project is licensed under the [CC BY-NC 4.0 License](https://creativecommons.org/licenses/by-nc/4.0/). Commercial use is prohibited without explicit permission. See the [LICENSE](./LICENSE) file for details. ## 📚 Citation BibTeX: ```bibtex @misc{penilescreenvit2024, title={PenileScreen-ViT: Vision Transformer Model for STD-related Visual Classification}, author={Yudara Kularathne, Janitha Prathapa and Thanveer Ahamad}, year={2024}, howpublished={\url{https://huggingface.co/HehealthVision/PenileScreen-ViT}}, } ``` Original paper (APA): > Allan‑Blitz LT, Ambepitiya S, Tirupathi R, & Klausner JD. (2024). The Development and Performance of a Machine‑Learning Based Mobile Platform for Visually Determining the Etiology of 5 Penile Diseases. Digital Health. Retrieved from https://www.mcpdigitalhealth.org/article/S2949-7612(24)00035-X/fulltext
ascdsw4/watch.onca.mata.homem.portal.zacarias.onca.onca.mata.homem.no.pantanal.sv	ascdsw4	2025-04-22T21:13:50Z	0	0	null	[ "region:us" ]	null	2025-04-22T21:08:27Z	Click here for Link>>>>https://allyoutubers.com/Onça-ataca-e-mata-homem-em-fazenda-no-Pantanal;-vídeo-mostra-rastro-de-perseguiçãoxac Click here for Link>>>>https://allyoutubers.com/Onça-ataca-e-mata-homem-em-fazenda-no-Pantanal;-vídeo-mostra-rastro-de-perseguiçãoxac Click here for Link>>>>https://allyoutubers.com/Onça-ataca-e-mata-homem-em-fazenda-no-Pantanal;-vídeo-mostra-rastro-de-perseguiçãoxac
Athmajan/Sionna-Neural-Receiver-16QAM-OFDM-SIMO	Athmajan	2025-04-22T21:12:37Z	0	0	null	[ "Sionna", "5G", "Wireless", "Communication", "Channel", "OFDM", "SIMO", "Neural", "Receiver", "license:apache-2.0", "region:us" ]	null	2025-04-22T21:06:52Z	--- license: apache-2.0 tags: - Sionna - 5G - Wireless - Communication - Channel - OFDM - SIMO - Neural - Receiver ---
xw17/Llama-3.2-1B-Instruct_finetuned_1_optimized1	xw17	2025-04-22T21:12:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T21:10:27Z	--- library_name: transformers tags: - trl - sft --- # 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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(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]
ScottishHaze/DrHouse	ScottishHaze	2025-04-22T21:11:03Z	0	0	null	[ "audio-to-audio", "en", "dataset:ScottishHaze/DrHouse", "license:cc-by-3.0", "region:us" ]	audio-to-audio	2025-04-22T21:02:21Z	--- license: cc-by-3.0 datasets: - ScottishHaze/DrHouse language: - en pipeline_tag: audio-to-audio ---
liulian26/aphasic-whisper-small-lora	liulian26	2025-04-22T21:08:49Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai/whisper-small", "base_model:adapter:openai/whisper-small", "region:us" ]	null	2025-04-22T06:46:47Z	--- base_model: openai/whisper-small library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(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
Jonjew/HeidiGardnerSNL	Jonjew	2025-04-22T21:08:45Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	2025-04-22T21:07:20Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- Heidi Gardner, dressed in a sleek black jumpsuit with a bold red lip, stands confidently at the microphone in a dimly lit comedy club in London's Soho district. The background is a warm, rich wood tone with a hint of vintage posters and eclectic decor. A glass of whiskey sits on a small table beside her, adding a touch of sophistication to the scene. Captured with a Canon EOS 5D Mark IV camera, using a 50mm lens with a wide aperture of f/1.4, this high-quality photo showcases Heidi's sharp wit and charismatic stage presence. The medium grain of the Kodak Portra 400 film adds a tactile, artistic feel to the image. Heidi's facial expression is a perfect blend of humor and vulnerability, drawing the viewer in and inviting them to laugh along with her. parameters: negative_prompt: unknown output: url: images/2025-04-22-182217-20250422-HeidiGardner-1-dev-000023_0.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Heidi Gardner license: unknown --- # Heidi Gardner SNL <Gallery /> ## Model description FROM https://civitai.com/models/1498227/heidi-gardner?modelVersionId=1694848 Please support the creator by donating buzz and liking at the page above! Trigger Heidi Gardner Strength 1 ## Trigger words You should use `Heidi Gardner` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/HeidiGardnerSNL/tree/main) them in the Files & versions tab.
ellietang/hf_saved_lora_ls-model-14B-full-CPT-v0.0.1	ellietang	2025-04-22T21:08:38Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-Coder-14B-Instruct", "base_model:finetune:unsloth/Qwen2.5-Coder-14B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-22T21:07:53Z	--- base_model: unsloth/Qwen2.5-Coder-14B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ellietang - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-14B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Goedel-LM/Goedel-Formalizer-32B-SonnetAnnotated	Goedel-LM	2025-04-22T21:00:12Z	13	0	null	[ "safetensors", "qwen2", "arxiv:2502.07640", "license:mit", "region:us" ]	null	2025-04-19T01:04:49Z	--- license: mit --- This is the formalizer for tranlating infromal math problem into formal statement in Lean 4. We use the data pair annotated by Claude Sonnet 3.5 to train a Qwen-2.5-32B-Coder. Here is an example code to use this formalizer, ``` import re from transformers import AutoTokenizer from vllm import LLM, SamplingParams import os import json def statement_translation_inference(informal_statement): return F""" I want you to translate a informal statment to formal statement in lean 4, the informal statement of the problem is: {informal_statement} The output is """ model_name = "Goedel-LM/Goedel-Formalizer-32B-SonnetAnnotated" tokenizer = AutoTokenizer.from_pretrained(model_name) model = LLM(model=model_name, seed=1, trust_remote_code=True, swap_space=8, tensor_parallel_size=2, max_model_len=4096) sampling_params = SamplingParams( temperature=1.0, max_tokens=2048, top_p=0.95, n=args.n, ) data_list = [{ "informal_statement": "Consider the terms of an arithmetic sequence: $-\frac{1}{3}, y+2, 4y, \ldots$. Solve for $y$." }] model_inputs = [statement_translation_inference(idata["informal_statement"], idata["informal_proof"]) for idata in data_list] model_outputs = model.generate( model_inputs, sampling_params, use_tqdm=True, ) ``` ## Citation ```latex @misc{lin2025goedelproverfrontiermodelopensource, title={Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving}, author={Yong Lin and Shange Tang and Bohan Lyu and Jiayun Wu and Hongzhou Lin and Kaiyu Yang and Jia Li and Mengzhou Xia and Danqi Chen and Sanjeev Arora and Chi Jin}, year={2025}, eprint={2502.07640}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2502.07640}, } ```
xw17/TinyLlama-1.1B-Chat-v1.0_finetuned_4_optimized1	xw17	2025-04-22T21:00:11Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T20:58:26Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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codermert/semanur_fluxxx	codermert	2025-04-22T20:59:06Z	0	1	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	2025-04-22T19:17:26Z	--- 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: TOK --- # Semanur_Fluxxx <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/codermert/semanur_fluxxx/resolve/main/lora.safetensors" } output = replicate.run( "prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0", 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('prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('codermert/semanur_fluxxx', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/codermert/semanur_fluxxx/discussions) to add images that show off what you’ve made with this LoRA.
Goedel-LM/Goedel-Formalizer-32B-LeanWorkbookAnnotated	Goedel-LM	2025-04-22T20:58:52Z	0	0	null	[ "safetensors", "qwen2", "arxiv:2502.07640", "license:mit", "region:us" ]	null	2025-04-19T13:35:08Z	--- license: mit --- This is the formalizer for tranlating infromal math problem into formal statement in Lean 4. We use the data pair in Lean workbook to train a Qwen-2.5-32B-Coder. Here is an example code to use this formalizer, ``` import re from transformers import AutoTokenizer from vllm import LLM, SamplingParams import os import json def statement_translation_inference(informal_statement): return F""" I want you to translate a informal statment to formal statement in lean 4, the informal statement of the problem is: {informal_statement} The output is """ model_name = "Goedel-LM/Goedel-Formalizer-32B-LeanWorkbookAnnotated" tokenizer = AutoTokenizer.from_pretrained(model_name) model = LLM(model=model_name, seed=1, trust_remote_code=True, swap_space=8, tensor_parallel_size=2, max_model_len=4096) sampling_params = SamplingParams( temperature=1.0, max_tokens=2048, top_p=0.95, n=args.n, ) data_list = [{ "informal_statement": "Consider the terms of an arithmetic sequence: $-\frac{1}{3}, y+2, 4y, \ldots$. Solve for $y$." }] model_inputs = [statement_translation_inference(idata["informal_statement"], idata["informal_proof"]) for idata in data_list] model_outputs = model.generate( model_inputs, sampling_params, use_tqdm=True, ) ``` ## Citation ```latex @misc{lin2025goedelproverfrontiermodelopensource, title={Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving}, author={Yong Lin and Shange Tang and Bohan Lyu and Jiayun Wu and Hongzhou Lin and Kaiyu Yang and Jia Li and Mengzhou Xia and Danqi Chen and Sanjeev Arora and Chi Jin}, year={2025}, eprint={2502.07640}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2502.07640}, } ```
FIERRO01/FIERRO01	FIERRO01	2025-04-22T20:54:47Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-22T15:27:43Z	--- 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 ---
monirulbdboy/bdboy	monirulbdboy	2025-04-22T20:54:26Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-22T20:54:24Z	--- license: apache-2.0 ---
mrbesher/test-drawing-model	mrbesher	2025-04-22T20:53:36Z	0	0	transformers	[ "transformers", "qwen2_5_vl_text", "feature-extraction", "text-generation-inference", "unsloth", "qwen2_5_vl", "en", "base_model:unsloth/Qwen2.5-VL-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-VL-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-22T20:31:02Z	--- base_model: unsloth/Qwen2.5-VL-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2_5_vl license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: mrbesher - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-VL-3B-Instruct 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)
locuslab/safety-classifier_gte-base-en-v1.5	locuslab	2025-04-22T20:52:37Z	0	0	null	[ "safetensors", "new", "text-classification", "custom_code", "dataset:locuslab/safety_data_annotated", "base_model:OrcaDB/gte-base-en-v1.5", "base_model:finetune:OrcaDB/gte-base-en-v1.5", "license:apache-2.0", "region:us" ]	text-classification	2025-04-22T15:12:18Z	--- license: apache-2.0 tags: - text-classification datasets: - locuslab/safety_data_annotated base_model: - OrcaDB/gte-base-en-v1.5 --- # Safety Classification This is a fine-tuned version of `OrcaDB/gte-base-en-v1.5` for safety scoring. ## Usage ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained( "locuslab/safety-classifier_gte-base-en-v1.5", torch_dtype=torch.bfloat16, num_labels=6, trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained("locuslab/safety-classifier_gte-base-en-v1.5") ```
unfoldingpast/stable-diffusion-v-1-4-original_duplicated	unfoldingpast	2025-04-22T20:52:12Z	0	0	null	[ "region:us" ]	null	2025-04-22T20:15:59Z	Duplicate repo of [stable-diffusion-v-1-4-original](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) --- license: creativeml-openrail-m ---
DianaT-Coach/dianat-lora	DianaT-Coach	2025-04-22T20:48:06Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-22T18:34:21Z	--- 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 ---
xw17/TinyLlama-1.1B-Chat-v1.0_finetuned_3_optimized1	xw17	2025-04-22T20:47:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T20:45:43Z	--- library_name: transformers tags: - trl - sft --- # 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]
xiwenc1/OpenRS-GRPO3	xiwenc1	2025-04-22T20:46:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:knoveleng/open-rs", "arxiv:2402.03300", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T10:09:49Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B datasets: knoveleng/open-rs library_name: transformers model_name: OpenRS-GRPO3 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for OpenRS-GRPO3 This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the [knoveleng/open-rs](https://huggingface.co/datasets/knoveleng/open-rs) 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="xiwenc1/OpenRS-GRPO3", 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/myopen-rs/huggingface/runs/m4v11vi7) 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: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - 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}} } ```
Hartunka/distilbert_rand_10_v2_qnli	Hartunka	2025-04-22T20:45:37Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:Hartunka/distilbert_rand_10_v2", "base_model:finetune:Hartunka/distilbert_rand_10_v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T20:33:45Z	--- library_name: transformers language: - en base_model: Hartunka/distilbert_rand_10_v2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_rand_10_v2_qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue args: qnli metrics: - name: Accuracy type: accuracy value: 0.6331685886875343 --- <!-- 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. --> # distilbert_rand_10_v2_qnli This model is a fine-tuned version of [Hartunka/distilbert_rand_10_v2](https://huggingface.co/Hartunka/distilbert_rand_10_v2) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6327 - Accuracy: 0.6332 ## 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-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6648 \| 1.0 \| 410 \| 0.6424 \| 0.6240 \| \| 0.6255 \| 2.0 \| 820 \| 0.6327 \| 0.6332 \| \| 0.5606 \| 3.0 \| 1230 \| 0.6599 \| 0.6385 \| \| 0.4606 \| 4.0 \| 1640 \| 0.6972 \| 0.6330 \| \| 0.3492 \| 5.0 \| 2050 \| 0.8273 \| 0.6376 \| \| 0.2518 \| 6.0 \| 2460 \| 1.0692 \| 0.6266 \| \| 0.1848 \| 7.0 \| 2870 \| 1.2471 \| 0.6359 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.21.1
monirulpaikar/paikar	monirulpaikar	2025-04-22T20:44:55Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-22T20:44:55Z	--- license: apache-2.0 ---
mbort1/d56823bb-cd8e-4e42-b155-80c88f5aa85d	mbort1	2025-04-22T20:40:28Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/zephyr-sft", "base_model:adapter:unsloth/zephyr-sft", "license:apache-2.0", "region:us" ]	null	2025-04-22T13:19:23Z	--- library_name: peft license: apache-2.0 base_model: unsloth/zephyr-sft tags: - axolotl - generated_from_trainer model-index: - name: d56823bb-cd8e-4e42-b155-80c88f5aa85d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/zephyr-sft bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a98363d9e111cae2_train_data.json ds_type: json format: custom path: /workspace/input_data/a98363d9e111cae2_train_data.json type: field_instruction: inputs field_output: targets format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 8 eval_max_new_tokens: 128 eval_steps: 100 evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: mbort1/d56823bb-cd8e-4e42-b155-80c88f5aa85d hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 1000 merge_lora: true micro_batch_size: 16 mlflow_experiment_name: /tmp/a98363d9e111cae2_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: lion_32bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: true save_only_model: false save_steps: 1000 saves_per_epoch: null seed: 4 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bf234bc4-112f-4540-9f33-c59398bec162 wandb_project: mb4 wandb_run: your_name wandb_runid: bf234bc4-112f-4540-9f33-c59398bec162 warmup_steps: 50 weight_decay: 0.01 xformers_attention: null ``` </details><br> # d56823bb-cd8e-4e42-b155-80c88f5aa85d This model is a fine-tuned version of [unsloth/zephyr-sft](https://huggingface.co/unsloth/zephyr-sft) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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: 16 - eval_batch_size: 8 - seed: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.LION and the args are: No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0013 \| 1 \| nan \| \| 0.0 \| 0.1346 \| 100 \| nan \| \| 0.0 \| 0.2693 \| 200 \| nan \| \| 0.0 \| 0.4039 \| 300 \| nan \| \| 0.0 \| 0.5385 \| 400 \| nan \| \| 0.0 \| 0.6732 \| 500 \| nan \| \| 0.0 \| 0.8078 \| 600 \| nan \| \| 0.0 \| 0.9424 \| 700 \| nan \| \| 0.0 \| 1.0767 \| 800 \| nan \| \| 0.0 \| 1.2114 \| 900 \| nan \| \| 0.0 \| 1.3460 \| 1000 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dgambettaphd/M_llm3_gen3_run0_W_doc1000_synt64_tot128_SYNLAST	dgambettaphd	2025-04-22T20:40:12Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-22T20:39:56Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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codermert/duygu_fluxx	codermert	2025-04-22T20:38:40Z	0	1	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	2025-04-22T15:56:45Z	--- 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: TOK --- # Duygu_Fluxx <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/codermert/duygu_fluxx/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('prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('codermert/duygu_fluxx', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/codermert/duygu_fluxx/discussions) to add images that show off what you’ve made with this LoRA.
xw17/TinyLlama-1.1B-Chat-v1.0_finetuned_1_optimized1	xw17	2025-04-22T20:37:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T20:35:41Z	--- library_name: transformers tags: - trl - sft --- # 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]
Evolluos/clonecris	Evolluos	2025-04-22T20:36:54Z	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	2025-04-22T19:44:10Z	--- 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: TOK --- # Clonecris <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/Evolluos/clonecris/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('Evolluos/clonecris', weight_name='lora.safetensors') image = pipeline('TOK').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: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Evolluos/clonecris/discussions) to add images that show off what you’ve made with this LoRA.
SimpleFrog/whisper_finetuned	SimpleFrog	2025-04-22T20:36:29Z	0	0	peft	[ "peft", "safetensors", "region:us" ]	null	2025-04-22T20:36:27Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
andreasol/RoBERTa-only-mfn-g	andreasol	2025-04-22T20:33:40Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-22T20:33:38Z	--- 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. 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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]
Hartunka/distilbert_rand_10_v2_cola	Hartunka	2025-04-22T20:32:05Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:Hartunka/distilbert_rand_10_v2", "base_model:finetune:Hartunka/distilbert_rand_10_v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T20:30:55Z	--- library_name: transformers language: - en base_model: Hartunka/distilbert_rand_10_v2 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation - accuracy model-index: - name: distilbert_rand_10_v2_cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 - name: Accuracy type: accuracy value: 0.6912751793861389 --- <!-- 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. --> # distilbert_rand_10_v2_cola This model is a fine-tuned version of [Hartunka/distilbert_rand_10_v2](https://huggingface.co/Hartunka/distilbert_rand_10_v2) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6154 - Matthews Correlation: 0.0 - Accuracy: 0.6913 ## 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-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\|:--------:\| \| 0.615 \| 1.0 \| 34 \| 0.6154 \| 0.0 \| 0.6913 \| \| 0.5908 \| 2.0 \| 68 \| 0.6302 \| 0.0513 \| 0.6836 \| \| 0.5475 \| 3.0 \| 102 \| 0.6317 \| 0.0803 \| 0.6817 \| \| 0.4883 \| 4.0 \| 136 \| 0.7007 \| 0.0974 \| 0.6500 \| \| 0.4326 \| 5.0 \| 170 \| 0.7236 \| 0.0838 \| 0.6472 \| \| 0.3826 \| 6.0 \| 204 \| 0.8645 \| 0.0734 \| 0.6433 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.21.1
pipichopa/pipichopa	pipichopa	2025-04-22T20:31:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-22T20:31:39Z	--- license: apache-2.0 ---
Hartunka/distilbert_rand_5_v2_mnli	Hartunka	2025-04-22T20:30:33Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:Hartunka/distilbert_rand_5_v2", "base_model:finetune:Hartunka/distilbert_rand_5_v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T19:29:45Z	--- library_name: transformers language: - en base_model: Hartunka/distilbert_rand_5_v2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert_rand_5_v2_mnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.6383238405207485 --- <!-- 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. --> # distilbert_rand_5_v2_mnli This model is a fine-tuned version of [Hartunka/distilbert_rand_5_v2](https://huggingface.co/Hartunka/distilbert_rand_5_v2) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.8381 - Accuracy: 0.6383 ## 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-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.9799 \| 1.0 \| 1534 \| 0.9141 \| 0.5641 \| \| 0.8826 \| 2.0 \| 3068 \| 0.8756 \| 0.5987 \| \| 0.8138 \| 3.0 \| 4602 \| 0.8479 \| 0.6196 \| \| 0.7453 \| 4.0 \| 6136 \| 0.8542 \| 0.6265 \| \| 0.6741 \| 5.0 \| 7670 \| 0.8408 \| 0.6393 \| \| 0.6026 \| 6.0 \| 9204 \| 0.8903 \| 0.6432 \| \| 0.533 \| 7.0 \| 10738 \| 0.9375 \| 0.6424 \| \| 0.4663 \| 8.0 \| 12272 \| 1.0434 \| 0.6320 \| \| 0.4071 \| 9.0 \| 13806 \| 1.1425 \| 0.6342 \| \| 0.3534 \| 10.0 \| 15340 \| 1.1938 \| 0.6375 \| ### Framework versions - Transformers 4.50.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.21.1
ohh5986/gemma-2-2B-it-thinking-function_calling-V0-ohh	ohh5986	2025-04-22T20:26:12Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-2-2b-it", "base_model:finetune:google/gemma-2-2b-it", "endpoints_compatible", "region:us" ]	null	2025-04-22T20:23:51Z	--- base_model: google/gemma-2-2b-it library_name: transformers model_name: gemma-2-2B-it-thinking-function_calling-V0-ohh tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-2-2B-it-thinking-function_calling-V0-ohh This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it). 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="ohh5986/gemma-2-2B-it-thinking-function_calling-V0-ohh", 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: 0.16.1 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ZhengjunHUO/distilbert-toxicity-classifier	ZhengjunHUO	2025-04-22T20:26:12Z	3	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-13T12:55:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
free5knuckles/my_awesome_eli5_clm-model	free5knuckles	2025-04-22T20:22:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T19:38:58Z	--- library_name: transformers license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-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_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8703 ## 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: 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 72 \| 3.9509 \| \| No log \| 2.0 \| 144 \| 3.8852 \| \| No log \| 3.0 \| 216 \| 3.8703 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
mahdin70/CodeBERT-PrimeVul-BigVul	mahdin70	2025-04-22T20:19:26Z	0	0	transformers	[ "transformers", "safetensors", "multi_task_codebert", "feature-extraction", "text-classification", "custom_code", "dataset:mahdin70/balanced_merged_bigvul_primevul", "base_model:microsoft/codebert-base", "base_model:finetune:microsoft/codebert-base", "license:mit", "region:us" ]	text-classification	2025-04-22T19:43:59Z	--- license: mit datasets: - mahdin70/balanced_merged_bigvul_primevul metrics: - accuracy - f1 - recall - precision base_model: - microsoft/codebert-base pipeline_tag: text-classification library_name: transformers --- # CodeBERT-Primevul-BigVul Model Card ## Model Overview `CodeBERT-Primevul-BigVul` is a multi-task model based on Microsoft's `codebert-base`, fine-tuned to detect vulnerabilities (`vul`) and classify Common Weakness Enumeration (CWE) types in code snippets. It was developed by [mahdin70](https://huggingface.co/mahdin70) and trained on a balanced dataset combining BigVul and PrimeVul datasets. The model performs binary classification for vulnerability detection and multi-class classification for CWE identification. - Model Repository: [mahdin70/CodeBERT-Primevul-BigVul](https://huggingface.co/mahdin70/CodeBERT-Primevul-BigVul) - Base Model: [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) - Tasks: Vulnerability Detection (Binary), CWE Classification (Multi-class) - License: MIT (assumed; adjust if different) - Date: Trained and uploaded as of April 22, 2025 ## Model Architecture The model extends `codebert-base` with two task-specific heads: - Vulnerability Head: A linear layer mapping 768-dimensional hidden states to 2 classes (vulnerable or not). - CWE Head: A linear layer mapping 768-dimensional hidden states to 135 classes (134 CWE types + 1 for "no CWE"). The architecture is implemented as a custom `MultiTaskCodeBERT` class in PyTorch, with the loss computed as the sum of cross-entropy losses for both tasks. ## Training Dataset The model was trained on the `mahdin70/balanced_merged_bigvul_primevul` dataset, which combines: - BigVul: A dataset of real-world vulnerabilities from open-source projects. - PrimeVul: A dataset focused on prime vulnerabilities in code. ### Dataset Details - Splits: - Train: 124,780 samples - Validation: 26,740 samples - Test: 26,738 samples - Features: - `func`: Code snippet (text) - `vul`: Binary label (0 = non-vulnerable, 1 = vulnerable) - `CWE ID`: CWE identifier (e.g., CWE-89) or None for non-vulnerable samples - Preprocessing: - CWE labels were encoded using a `LabelEncoder` with 134 unique CWE classes identified across the dataset. - Non-vulnerable samples assigned a CWE label of -1 (mapped to 0 in the model). The dataset is balanced to ensure a fair representation of vulnerable and non-vulnerable samples, with a maximum of 10 samples per commit where applicable. ## Training Details ### Training Arguments The model was trained using the Hugging Face `Trainer` API with the following arguments: - Evaluation Strategy: Per epoch - Save Strategy: Per epoch - Learning Rate: 2e-5 - Batch Size: 8 (per device, train and eval) - Epochs: 3 - Weight Decay: 0.01 - Logging: Every 10 steps, logged to `./logs` ### Training Environment - Hardware: 2x NVIDIA Tesla T4 GPU - Framework: PyTorch 2.5.1+cu121, Transformers 4.47.0 - Duration: ~6 hours, 23 minutes, 18 seconds (23,397 steps) ### Training Metrics Validation metrics across epochs: \| Epoch \| Training Loss \| Validation Loss \| Vul Accuracy \| Vul Precision \| Vul Recall \| Vul F1 \| CWE Accuracy \| \|-------\|---------------\|-----------------\|--------------\|---------------\|------------\|----------\|--------------\| \| 1 \| 0.4275 \| 0.5737 \| 0.9519 \| 0.7753 \| 0.4795 \| 0.5925 \| 0.0656 \| \| 2 \| 0.7608 \| 0.5450 \| 0.9537 \| 0.7766 \| 0.5133 \| 0.6181 \| 0.1349 \| \| 3 \| 0.5624 \| 0.5443 \| 0.9545 \| 0.7669 \| 0.5400 \| 0.6338 \| 0.1749 \| ## Usage ### Installation Install the required libraries: ```bash pip install transformers torch datasets huggingface_hub ``` ### Sample Code Snippet Below is an example of how to use the model for inference on a code snippet: ```python from transformers import AutoTokenizer, AutoModel import torch # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base") model = AutoModel.from_pretrained("mahdin70/CodeBERT-Primevul-BigVul", trust_remote_code=True) model.eval() # Example code snippet code = """ bool DebuggerFunction::InitTabContents() { Value* debuggee; EXTENSION_FUNCTION_VALIDATE(args_->Get(0, &debuggee)); DictionaryValue* dict = static_cast<DictionaryValue>(debuggee); EXTENSION_FUNCTION_VALIDATE(dict->GetInteger(keys::kTabIdKey, &tab_id_)); contents_ = NULL; TabContentsWrapper wrapper = NULL; bool result = ExtensionTabUtil::GetTabById( tab_id_, profile(), include_incognito(), NULL, NULL, &wrapper, NULL); if (!result \|\| !wrapper) { error_ = ExtensionErrorUtils::FormatErrorMessage( keys::kNoTabError, base::IntToString(tab_id_)); return false; } contents_ = wrapper->web_contents(); if (ChromeWebUIControllerFactory::GetInstance()->HasWebUIScheme( contents_->GetURL())) { error_ = ExtensionErrorUtils::FormatErrorMessage( keys::kAttachToWebUIError, contents_->GetURL().scheme()); return false; } return true; } """ # Tokenize input inputs = tokenizer(code, return_tensors="pt", padding="max_length", truncation=True, max_length=512) # Move to GPU if available device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) inputs = {k: v.to(device) for k, v in inputs.items()} # Get predictions with torch.no_grad(): outputs = model(inputs) vul_logits = outputs["vul_logits"] cwe_logits = outputs["cwe_logits"] # Vulnerability prediction vul_pred = torch.argmax(vul_logits, dim=1).item() print(f"Vulnerability: {'Vulnerable' if vul_pred == 1 else 'Not Vulnerable'}") # CWE prediction (if vulnerable) if vul_pred == 1: cwe_pred = torch.argmax(cwe_logits, dim=1).item() - 1 # Subtract 1 as -1 is "no CWE" print(f"Predicted CWE: {cwe_pred if cwe_pred >= 0 else 'None'}") ``` ### Output Example: ```bash Vulnerability: Vulnerable Predicted CWE: 120 # Maps to CWE-120 (Buffer Overflow), depending on encoder ``` ## Notes - The CWE prediction is an integer index (0 to 133). To map it to a specific CWE ID (e.g., CWE-120), you need the LabelEncoder used during training, available in the dataset preprocessing step. - Ensure `trust_remote_code=True` as the model uses custom code from the repository. ## Limitations - CWE Accuracy: The model has low CWE classification accuracy (17.49%), likely due to class imbalance or complexity in distinguishing similar CWE types. - Recall: Moderate recall (54.00%) for vulnerability detection suggests some vulnerable samples may be missed. - Generalization**: Trained on BigVul and PrimeVul, performance may vary on out-of-domain codebases. ## Future Improvements - Increase training epochs or dataset size to improve CWE accuracy. - Experiment with class weighting to address CWE imbalance. - Fine-tune on additional datasets for broader generalization.
heyIamUmair/llama3-3b-instruct-legal-pakistan	heyIamUmair	2025-04-22T20:18:20Z	0	0	null	[ "safetensors", "llama", "unsloth", "legal", "pakistan", "lora", "instruction-tuned", "fine-tuned", "dataset:custom-legal-dataset-pakistan", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:adapter:unsloth/Llama-3.2-3B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-04-19T08:53:45Z	--- license: apache-2.0 tags: - llama - unsloth - legal - pakistan - lora - instruction-tuned - fine-tuned model_type: causal-lm base_model: unsloth/Llama-3.2-3B-Instruct datasets: - custom-legal-dataset-pakistan inference: true ---
jinx2321/mt5-jeju-araea-tagged-all-1e4-paper	jinx2321	2025-04-22T20:15:26Z	1	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-20T20:48:33Z	--- library_name: transformers license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer model-index: - name: mt5-jeju-araea-tagged-all-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. --> # mt5-jeju-araea-tagged-all-1e4-paper This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-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
luckeciano/Qwen-2.5-7B-RL-LACPO-2-1e-05-24	luckeciano	2025-04-22T20:11:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T11:20:30Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-RL-LACPO-2-1e-05-24 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-RL-LACPO-2-1e-05-24 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-RL-LACPO-2-1e-05-24", 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/max-ent-llms/MaxEntLLMs/runs/58oj6cyb) 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: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - 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}} } ```
ClinicianFOCUS/gemma3-4b-soap-note-generator	ClinicianFOCUS	2025-04-22T20:10:49Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T19:56:30Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: ClinicianFOCUS - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 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)
rpant/urdu-llama-3.2-1b	rpant	2025-04-22T20:06:49Z	0	0	mlx	[ "mlx", "safetensors", "llama", "facebook", "meta", "pytorch", "llama-3", "text-generation", "en", "de", "fr", "it", "pt", "hi", "es", "th", "base_model:meta-llama/Llama-3.2-1B", "base_model:quantized:meta-llama/Llama-3.2-1B", "license:llama3.2", "4-bit", "region:us" ]	text-generation	2025-04-22T20:02:45Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: mlx pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - mlx license: llama3.2 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \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\n“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.\n\n“Llama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\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. 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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 base_model: meta-llama/Llama-3.2-1B --- # rpant/urdu-llama-3.2-1b This model [rpant/urdu-llama-3.2-1b](https://huggingface.co/rpant/urdu-llama-3.2-1b) was converted to MLX format from [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) using mlx-lm version 0.22.4. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("rpant/urdu-llama-3.2-1b") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
bugrY/tra	bugrY	2025-04-22T20:05:03Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-04-22T20:05:02Z	--- license: bigscience-bloom-rail-1.0 ---
teecreates/dish	teecreates	2025-04-22T20:04:41Z	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	2025-04-22T19:30:16Z	--- 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: Dish --- # Dish <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 `Dish` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Dish", "lora_weights": "https://huggingface.co/teecreates/Dish/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('teecreates/Dish', weight_name='lora.safetensors') image = pipeline('Dish').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: 2500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/teecreates/Dish/discussions) to add images that show off what you’ve made with this LoRA.
kenken6696/Llama-3.2-1B_3x1_mix_position_overfitting_known_unknown	kenken6696	2025-04-22T20:03:58Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-15T10:34:24Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Fredithefish/newhtmlmodel	Fredithefish	2025-04-22T20:03:13Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen2.5-Coder-32B-Instruct", "base_model:finetune:unsloth/Qwen2.5-Coder-32B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T19:57:19Z	--- base_model: unsloth/Qwen2.5-Coder-32B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Fredithefish - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-32B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
imnaresh/c1325skb73	imnaresh	2025-04-22T19:58:23Z	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	2025-04-22T19:16:25Z	--- 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: c1325skb73 --- # C1325Skb73 <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 `c1325skb73` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "c1325skb73", "lora_weights": "https://huggingface.co/imnaresh/c1325skb73/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('imnaresh/c1325skb73', weight_name='lora.safetensors') image = pipeline('c1325skb73').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: 3200 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/imnaresh/c1325skb73/discussions) to add images that show off what you’ve made with this LoRA.
AndrewWinchester/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-robust_wary_armadillo	AndrewWinchester	2025-04-22T19:57:56Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am robust wary armadillo", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T23:33:37Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-robust_wary_armadillo tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am robust wary armadillo - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-robust_wary_armadillo This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="AndrewWinchester/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-robust_wary_armadillo", 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: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - 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}} } ```
ohassane/deepseek-finetuned-cc	ohassane	2025-04-22T19:57:55Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-22T19:16:39Z	--- 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]
mradermacher/s1.1-1.5B-20k-i1-GGUF	mradermacher	2025-04-22T19:57:45Z	50	1	transformers	[ "transformers", "gguf", "en", "dataset:simplescaling/s1K-1.1", "base_model:TikaToka/s1.1-1.5B-20k-bf16", "base_model:quantized:TikaToka/s1.1-1.5B-20k-bf16", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-05T11:27:12Z	--- base_model: TikaToka/s1.1-1.5B-20k-bf16 datasets: - simplescaling/s1K-1.1 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/TikaToka/s1.1-1.5B-20k-bf16 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/s1.1-1.5B-20k-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/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.7 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q2_K.gguf) \| i1-Q2_K \| 0.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.9 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q4_1.gguf) \| i1-Q4_1 \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/s1.1-1.5B-20k-i1-GGUF/resolve/main/s1.1-1.5B-20k.i1-Q6_K.gguf) \| i1-Q6_K \| 1.4 \| 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 -->
ohassane/deepseek-codeclone-detector	ohassane	2025-04-22T19:57:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-22T16:18:32Z	--- library_name: transformers tags: - unsloth - trl - sft --- # 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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omsh97/Industry_Project_v1	omsh97	2025-04-22T19:55:13Z	0	0	null	[ "gguf", "mistral", "base_model:unsloth/Mistral-Nemo-Instruct-2407", "base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-22T17:28:04Z	--- license: apache-2.0 base_model: - unsloth/Mistral-Nemo-Instruct-2407 ---
xw17/Qwen2-1.5B-Instruct_finetuned_2_optimized1	xw17	2025-04-22T19:55:02Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T19:52:47Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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kokovova/1ce42a99-4917-4eba-bed2-63cfa01c329a	kokovova	2025-04-22T19:55:01Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:aisingapore/Llama-SEA-LION-v2-8B-IT", "base_model:adapter:aisingapore/Llama-SEA-LION-v2-8B-IT", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-22T19:32:04Z	--- library_name: peft license: llama3 base_model: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct tags: - axolotl - generated_from_trainer model-index: - name: 1ce42a99-4917-4eba-bed2-63cfa01c329a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7e15bc4c898623ab_train_data.json ds_type: json format: custom path: /workspace/input_data/7e15bc4c898623ab_train_data.json type: field_instruction: instruction field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: kokovova/1ce42a99-4917-4eba-bed2-63cfa01c329a hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/7e15bc4c898623ab_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: f4125c43-26a8-4cb6-b352-871c861f38ac wandb_project: s56-4 wandb_run: your_name wandb_runid: f4125c43-26a8-4cb6-b352-871c861f38ac warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 1ce42a99-4917-4eba-bed2-63cfa01c329a This model is a fine-tuned version of [aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct](https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6482 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - 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: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5939 \| 0.0059 \| 200 \| 0.6482 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Dugerij/image_segmentation_classifier	Dugerij	2025-04-22T19:54:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "vision", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-04-22T18:23:06Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - vision - generated_from_trainer metrics: - accuracy model-index: - name: image_segmentation_classifier 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. --> # image_segmentation_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the taresco/newspaper_ocr dataset. It achieves the following results on the evaluation set: - Loss: 0.0033 - Accuracy: 0.9993 ## 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: 8 - eval_batch_size: 8 - seed: 1337 - 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: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.0014 \| 1.0 \| 2031 \| 0.0065 \| 0.9986 \| \| 0.0005 \| 2.0 \| 4062 \| 0.0033 \| 0.9993 \| \| 0.0003 \| 3.0 \| 6093 \| 0.0058 \| 0.9990 \| \| 0.0002 \| 4.0 \| 8124 \| 0.0043 \| 0.9983 \| \| 0.0001 \| 5.0 \| 10155 \| 0.0036 \| 0.9990 \| ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
ohassane/outputs	ohassane	2025-04-22T19:54:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-04-21T02:13:13Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit library_name: transformers model_name: outputs tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for outputs This model is a fine-tuned version of [unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit](https://huggingface.co/unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit). 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="ohassane/outputs", 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/o-m-hassane-university-of-groningen/Fine-tune-DeepSeek-R1-Distill-Llama-8B/runs/dly0fibd) This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.2.0 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ASethi04/Qwen-Qwen2.5-7B-hellaswag-lora-second	ASethi04	2025-04-22T19:54:48Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B", "base_model:finetune:Qwen/Qwen2.5-7B", "endpoints_compatible", "region:us" ]	null	2025-04-22T13:01:54Z	--- base_model: Qwen/Qwen2.5-7B library_name: transformers model_name: Qwen-Qwen2.5-7B-hellaswag-lora-second tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen-Qwen2.5-7B-hellaswag-lora-second This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B). 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="ASethi04/Qwen-Qwen2.5-7B-hellaswag-lora-second", 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/torchql-org/huggingface/runs/psaw633r) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dzanbek/30194f77-46c4-4597-9898-36b602b79f89	dzanbek	2025-04-22T19:54:07Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-135M", "base_model:adapter:unsloth/SmolLM2-135M", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-22T19:44:28Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-135M tags: - axolotl - generated_from_trainer model-index: - name: 30194f77-46c4-4597-9898-36b602b79f89 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM2-135M bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d91a99e87aafcf02_train_data.json ds_type: json format: custom path: /workspace/input_data/d91a99e87aafcf02_train_data.json type: field_input: hypothesis field_instruction: premise field_output: augmented_hypothesis format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/30194f77-46c4-4597-9898-36b602b79f89 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/d91a99e87aafcf02_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ca466c80-e05f-4b5f-9b4b-b098546c6510 wandb_project: s56-35 wandb_run: your_name wandb_runid: ca466c80-e05f-4b5f-9b4b-b098546c6510 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 30194f77-46c4-4597-9898-36b602b79f89 This model is a fine-tuned version of [unsloth/SmolLM2-135M](https://huggingface.co/unsloth/SmolLM2-135M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5807 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - 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: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.2273 \| 0.0084 \| 200 \| 2.5807 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MinaMila/phi3_unlearned_LoRa_GermanCredit_ep17_55	MinaMila	2025-04-22T19:53:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-22T19:53:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
iaggarw1/google-bert	iaggarw1	2025-04-22T19:50:24Z	0	0	null	[ "pytorch", "tf", "jax", "safetensors", "bert", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "license:apache-2.0", "region:us" ]	null	2025-04-22T19:50:22Z	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia --- # BERT large model (uncased) whole word masking finetuned on SQuAD Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same. The training is identical -- each masked WordPiece token is predicted independently. After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. See below for more information regarding this fine-tuning. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters. ## Intended uses & limitations This model should be used as a question-answering model. You may use it in a question answering pipeline, or use it to output raw results given a query and a context. You may see other use cases in the [task summary](https://huggingface.co/transformers/task_summary.html#extractive-question-answering) of the transformers documentation.## Training data The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, \$\beta_{1} = 0.9\$ and \$\beta_{2} = 0.999\$, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### Fine-tuning After pre-training, this model was fine-tuned on the SQuAD dataset with one of our fine-tuning scripts. In order to reproduce the training, you may use the following command: ``` python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_qa.py \ --model_name_or_path bert-large-uncased-whole-word-masking \ --dataset_name squad \ --do_train \ --do_eval \ --learning_rate 3e-5 \ --num_train_epochs 2 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir ./examples/models/wwm_uncased_finetuned_squad/ \ --per_device_eval_batch_size=3 \ --per_device_train_batch_size=3 \ ``` ## Evaluation results The results obtained are the following: ``` f1 = 93.15 exact_match = 86.91 ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
xw17/Qwen2-1.5B-Instruct_finetuned_1_optimized1	xw17	2025-04-22T19:50:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T19:47:42Z	--- library_name: transformers tags: - trl - sft --- # 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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umar141/Gemma_1B_Baro_v1_vllm	umar141	2025-04-22T19:49:11Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-1b-it", "base_model:finetune:unsloth/gemma-3-1b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T19:47:04Z	--- base_model: unsloth/gemma-3-1b-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: umar141 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it This gemma3_text 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)

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