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19uez/GRPO_llama3_2_3B_16_005_2k_part1	19uez	2025-05-04T11:46:04Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "unsloth", "trl", "grpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T11:45:05Z	--- library_name: transformers tags: - unsloth - trl - grpo --- # 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]
B3n93x/arabix	B3n93x	2025-05-04T11:44:46Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-04T11:31:14Z	--- 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: AraBiX --- # Arabix <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 `AraBiX` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AraBiX", "lora_weights": "https://huggingface.co/B3n93x/arabix/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('B3n93x/arabix', weight_name='lora.safetensors') image = pipeline('AraBiX').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/B3n93x/arabix/discussions) to add images that show off what you’ve made with this LoRA.
Mikeszbb/SmolVLM-Base-vqav2	Mikeszbb	2025-05-04T11:44:41Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:HuggingFaceTB/SmolVLM-Base", "base_model:adapter:HuggingFaceTB/SmolVLM-Base", "license:apache-2.0", "region:us" ]	null	2025-03-14T05:07:12Z	--- library_name: peft license: apache-2.0 base_model: HuggingFaceTB/SmolVLM-Base tags: - generated_from_trainer model-index: - name: SmolVLM-Base-vqav2 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. --> # SmolVLM-Base-vqav2 This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Base](https://huggingface.co/HuggingFaceTB/SmolVLM-Base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 50 - num_epochs: 1 ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
remfinator/tinyllama-ft-news-sentiment	remfinator	2025-05-04T11:38:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "tinyllama", "finance", "sentiment-analysis", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T11:23:53Z	--- language: en license: apache-2.0 tags: - tinyllama - finance - sentiment-analysis library_name: transformers --- # TinyLlama‑FT‑News‑Sentiment TinyLlama‑1.1B‑Chat fine‑tuned for market‑news sentiment classification. ## How to use ```python from transformers import AutoTokenizer, AutoModelForCausalLM tok = AutoTokenizer.from_pretrained("remfinator/tinyllama-ft-news-sentiment") model = AutoModelForCausalLM.from_pretrained("remfinator/tinyllama-ft-news-sentiment", device_map="auto")
Bari-Pisa-Diretta-Gratis/Bari.Pisa.In.Diretta.Streaming.Gratis.Tv.Official	Bari-Pisa-Diretta-Gratis	2025-05-04T11:35:38Z	0	0	null	[ "region:us" ]	null	2025-05-04T11:16:04Z	⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys ⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys ⚽📺📱👉◄◄🔴 https://tinyurl.com/mtbv4nys Bari-Pisa come e dove vederla: Sky o DAZN? Canale tv, diretta streaming, formazioni e orario Partita valevole per la 37a giornata della Serie B BKT 2024/2025 Da oltre 20 anni informa in modo obiettivo e appassionato su tutto il mondo dello sport. Calcio, calciomercato, F1, Motomondiale ma anche tennis, volley, basket: su Virgilio Sport i tifosi e gli appassionati sanno che troveranno sempre copertura completa e zero faziosità. La squadra di Virgilio Sport è formata da giornalisti ed esperti di sport abili sia nel gioco di rimessa quando intercettano le notizie e le rilanciano verso la rete, sia nella costruzione dal basso quando creano contenuti 100% originali ed esclusivi.
phospho-app/kazugi-hand_dataset-s14q327x6z	phospho-app	2025-05-04T11:35:00Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-05-04T10:56:10Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [kazugi/hand_dataset](https://huggingface.co/datasets/kazugi/hand_dataset) - Wandb run URL: None - Epochs: 10 - Batch size: 64 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
NextGenC/7Gen	NextGenC	2025-05-04T11:28:06Z	0	1	torch	[ "torch", "gan", "mnist", "7gen", "pytorch", "en", "license:mit", "region:us" ]	null	2025-05-04T11:21:38Z	--- license: mit language: - en tags: - gan - mnist - 7gen - pytorch library_name: torch model_type: image-generator --- ![7Gen Model](https://img.shields.io/badge/7Gen-MNIST_Generator-blue?style=for-the-badge) ![Python](https://img.shields.io/badge/python-3.8+-blue.svg?style=for-the-badge&logo=python) ![PyTorch](https://img.shields.io/badge/PyTorch-1.12+-red.svg?style=for-the-badge&logo=pytorch) ![License](https://img.shields.io/badge/license-MIT-green.svg?style=for-the-badge) # 7Gen - Advanced MNIST Digit Generation System State-of-the-art Conditional GAN for MNIST digit synthesis with self-attention mechanisms. --- ## 🚀 Features - 🎯 Conditional Generation: Generate specific digits (0–9) on demand. - 🖼️ High Quality Output: Sharp and realistic handwritten digit samples. - ⚡ Fast Inference: Real-time generation on GPU. - 🔌 Easy Integration: Minimal setup, PyTorch-native implementation. - 🚀 GPU Acceleration: Full CUDA support. --- ## 🔍 Model Details - Architecture: Conditional GAN with self-attention - Parameters: 2.5M - Input: 100-dimensional noise vector + class label - Output: 28x28 grayscale images - Training Data: MNIST dataset (60,000 images) - Training Time: ~2 hours on NVIDIA RTX 3050 Ti --- ## 🧪 Performance Metrics \| Metric \| Score \| \|------------------\|-------\| \| FID Score \| 12.3 \| \| Inception Score \| 8.7 \| - Training Epochs: 100 - Batch Size: 64 --- ## ⚙️ Training Configuration ```yaml model: latent_dim: 100 num_classes: 10 generator_layers: [256, 512, 1024] discriminator_layers: [512, 256] training: batch_size: 64 learning_rate: 0.0002 epochs: 100 optimizer: Adam beta1: 0.5 beta2: 0.999
Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B-Q8_0-GGUF	Sorawiz	2025-05-04T11:27:28Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B", "base_model:quantized:Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T11:26:23Z	--- base_model: Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B-Q8_0-GGUF This model was converted to GGUF format from [`Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B`](https://huggingface.co/Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B-Q8_0-GGUF --hf-file galactic-qwen2.5-14b-uncensored-test-1b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B-Q8_0-GGUF --hf-file galactic-qwen2.5-14b-uncensored-test-1b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B-Q8_0-GGUF --hf-file galactic-qwen2.5-14b-uncensored-test-1b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Sorawiz/Galactic-Qwen2.5-14B-Uncensored-Test-1B-Q8_0-GGUF --hf-file galactic-qwen2.5-14b-uncensored-test-1b-q8_0.gguf -c 2048 ```
alexantonov/nllb-200-distilled-600M-eng-mya	alexantonov	2025-05-04T11:15:11Z	0	0	null	[ "tensorboard", "safetensors", "m2m_100", "generated_from_trainer", "my", "base_model:facebook/nllb-200-distilled-600M", "base_model:finetune:facebook/nllb-200-distilled-600M", "license:cc-by-nc-4.0", "region:us" ]	null	2025-05-04T10:49:34Z	--- license: cc-by-nc-4.0 base_model: facebook/nllb-200-distilled-600M tags: - generated_from_trainer model-index: - name: nllb-200-distilled-600M-eng-mya results: [] language: - my --- <!-- 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. --> # nllb-200-distilled-600M-eng-mya This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the [Helsinki-NLP/opus-100](https://huggingface.co/datasets/Helsinki-NLP/opus-100) dataset. It achieves the following results on the evaluation set: - eval_loss: 3.8312 - eval_bleu: 10.6633 - eval_gen_len: 18.196 - eval_runtime: 192.4759 - eval_samples_per_second: 2.598 - eval_steps_per_second: 2.598 - epoch: 0.98 - step: 24000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Framework versions - Transformers 4.38.2 - Pytorch 2.6.0+cu124 - Datasets 2.18.0 - Tokenizers 0.15.2
vermoney/dfcf05b9-a6ee-4501-bd44-45b49bb8ef6b	vermoney	2025-05-04T11:14:18Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:adapter:unsloth/Meta-Llama-3.1-8B", "license:llama3.1", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T10:55:57Z	--- library_name: peft license: llama3.1 base_model: unsloth/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: dfcf05b9-a6ee-4501-bd44-45b49bb8ef6b 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/Meta-Llama-3.1-8B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e09559fcf6f0ac01_train_data.json ds_type: json format: custom path: /workspace/input_data/e09559fcf6f0ac01_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 early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vermoney/dfcf05b9-a6ee-4501-bd44-45b49bb8ef6b 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/e09559fcf6f0ac01_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: cb90103a-f63e-46ef-aa4d-918767b8bb09 wandb_project: s56-9 wandb_run: your_name wandb_runid: cb90103a-f63e-46ef-aa4d-918767b8bb09 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # dfcf05b9-a6ee-4501-bd44-45b49bb8ef6b This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4838 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.762 \| 0.0083 \| 200 \| 1.4838 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
bolinzer/CetusMix_WhaleFall2	bolinzer	2025-05-04T11:13:53Z	0	0	null	[ "license:openrail", "region:us" ]	null	2025-05-04T10:56:48Z	--- license: openrail --- https://civitai.com/models/6755?modelVersionId=105924
marialvsantiago/f9e59804-2d0e-484f-b19b-59717ee1db58	marialvsantiago	2025-05-04T11:13:50Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Meta-Llama-3.1-8B", "base_model:adapter:unsloth/Meta-Llama-3.1-8B", "license:llama3.1", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T10:55:57Z	--- library_name: peft license: llama3.1 base_model: unsloth/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: f9e59804-2d0e-484f-b19b-59717ee1db58 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/Meta-Llama-3.1-8B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e09559fcf6f0ac01_train_data.json ds_type: json format: custom path: /workspace/input_data/e09559fcf6f0ac01_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 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: marialvsantiago/f9e59804-2d0e-484f-b19b-59717ee1db58 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/e09559fcf6f0ac01_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: cb90103a-f63e-46ef-aa4d-918767b8bb09 wandb_project: s56-33 wandb_run: your_name wandb_runid: cb90103a-f63e-46ef-aa4d-918767b8bb09 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # f9e59804-2d0e-484f-b19b-59717ee1db58 This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4833 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7625 \| 0.0083 \| 200 \| 1.4833 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ma921/gpt2-large_dr_dpo_golden-hh_noise40_epoch3	ma921	2025-05-04T11:12:37Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-golden-hh", "base_model:finetune:ma921/gpt2-large-sft-golden-hh", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T11:11:32Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-golden-hh tags: - generated_from_trainer model-index: - name: gpt2-large_dr_dpo_golden-hh_noise40_epoch3 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. --> # gpt2-large_dr_dpo_golden-hh_noise40_epoch3 This model is a fine-tuned version of [ma921/gpt2-large-sft-golden-hh](https://huggingface.co/ma921/gpt2-large-sft-golden-hh) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
rayonlabs/hf-autotrain-2025-05-03-75b45eb6	rayonlabs	2025-05-04T11:10:20Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "dataset:rayonlabs/autotrain-data-hf-autotrain-2025-05-03-75b45eb6", "base_model:EleutherAI/pythia-70m", "base_model:finetune:EleutherAI/pythia-70m", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T23:29:17Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: EleutherAI/pythia-70m widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - rayonlabs/autotrain-data-hf-autotrain-2025-05-03-75b45eb6 --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
mbsoft/skippy-ru-rmvpe	mbsoft	2025-05-04T11:09:35Z	0	0	null	[ "license:openrail", "region:us" ]	null	2025-05-04T11:04:57Z	--- license: openrail --- Cyberpunk 2077 Skippy Russian voice rmvpe, contentvec, 160e 1120s https://mb-soft.ru
qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-6Bit	qurk41	2025-05-04T11:07:40Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mlx", "conversational", "base_model:JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf", "base_model:quantized:JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "region:us" ]	text-generation	2025-05-04T11:06:41Z	--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers base_model: JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf tags: - mlx --- # qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-6Bit The Model [qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-6Bit](https://huggingface.co/qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-6Bit) was converted to MLX format from [JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf](https://huggingface.co/JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-6Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
kainatq/CMAKE	kainatq	2025-05-04T11:07:18Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-08T12:44:55Z	--- license: apache-2.0 --- # Prompt for Chat bot CMAKE A chatbot to help you create your character for Sillytavern for free. here is the bot [CMAKE](https://hf.co/chat/assistant/67ef70a3a931caa86184180c): https://hf.co/chat/assistant/67ef70a3a931caa86184180c # Support me Like what I do? support me in [patreon](https://patreon.com/user?u=43919102&utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink) if you want :)
tanay4587/ml	tanay4587	2025-05-04T10:59:04Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-04T10:59:04Z	--- license: creativeml-openrail-m ---
ma921/gpt2-large_dpo_oasst1_noise40_epoch3	ma921	2025-05-04T10:57:29Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-golden-hh", "base_model:finetune:ma921/gpt2-large-sft-golden-hh", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T10:56:31Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-golden-hh tags: - generated_from_trainer model-index: - name: gpt2-large_dpo_oasst1_noise40_epoch3 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. --> # gpt2-large_dpo_oasst1_noise40_epoch3 This model is a fine-tuned version of [ma921/gpt2-large-sft-golden-hh](https://huggingface.co/ma921/gpt2-large-sft-golden-hh) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
WizzyRocky/ppo-LunarLander-v2	WizzyRocky	2025-05-04T10:56:55Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T10:56:37Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 275.71 +/- 21.91 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-4Bit	qurk41	2025-05-04T10:48:23Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mlx", "conversational", "base_model:JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf", "base_model:quantized:JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-05-04T10:47:43Z	--- license: apache-2.0 pipeline_tag: text-generation library_name: transformers base_model: JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf tags: - mlx --- # qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-4Bit The Model [qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-4Bit](https://huggingface.co/qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-4Bit) was converted to MLX format from [JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf](https://huggingface.co/JackCloudman/mistral-small-3.1-24b-instruct-2503-jackterated-hf) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("qurk41/mistral-small-3.1-24b-instruct-2503-jackterated-hf-mlx-4Bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
kostiantynk1205/8d46f1e6-e2a9-449c-ae9c-207c56bbe880	kostiantynk1205	2025-05-04T10:45:38Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:1d3219f72b2f3c95_train_data.json", "base_model:bigcode/starcoder2-3b", "base_model:adapter:bigcode/starcoder2-3b", "region:us" ]	null	2025-05-04T10:45:11Z	--- library_name: peft tags: - generated_from_trainer datasets: - 1d3219f72b2f3c95_train_data.json base_model: bigcode/starcoder2-3b model-index: - name: kostiantynk1205/8d46f1e6-e2a9-449c-ae9c-207c56bbe880 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. --> # kostiantynk1205/8d46f1e6-e2a9-449c-ae9c-207c56bbe880 This model was trained from scratch on the /workspace/input_data/1d3219f72b2f3c95_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 2.3896 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
riyanatsill/Bloom_PMB	riyanatsill	2025-05-04T10:43:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-13T11:46:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
VeerSahai/ppo-LunarLander-v2	VeerSahai	2025-05-04T10:42:21Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-04T10:42:03Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 278.43 +/- 19.10 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
deswaq/iuh9	deswaq	2025-05-04T10:32:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T10:19:55Z	--- 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]
datapaf/ve_focus_starcoder2_racket	datapaf	2025-05-04T10:26:48Z	0	0	transformers	[ "transformers", "safetensors", "starcoder2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T09:52:00Z	--- 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]
XXXCarl/SegEarth-R1-RefSeg	XXXCarl	2025-05-04T10:16:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T10:16:39Z	--- license: apache-2.0 ---
delightalien/flaskDB	delightalien	2025-05-04T10:15:15Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T10:15:15Z	--- license: apache-2.0 ---
Dag1233/Daghas	Dag1233	2025-05-04T10:15:08Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T10:15:08Z	--- license: apache-2.0 ---
caliburblck/black	caliburblck	2025-05-04T10:13:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T10:13:39Z	--- license: apache-2.0 ---
Romain-XV/50ed29e6-d399-4e10-91aa-7f35c603d964	Romain-XV	2025-05-04T10:13:09Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:lmsys/vicuna-7b-v1.5", "base_model:finetune:lmsys/vicuna-7b-v1.5", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T09:35:33Z	--- base_model: lmsys/vicuna-7b-v1.5 library_name: transformers model_name: 50ed29e6-d399-4e10-91aa-7f35c603d964 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 50ed29e6-d399-4e10-91aa-7f35c603d964 This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5). 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="Romain-XV/50ed29e6-d399-4e10-91aa-7f35c603d964", 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/romain_fnc-xventures/Gradients-On-Demand/runs/3lguglyt) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
harshroxnox/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF	harshroxnox	2025-05-04T10:12:52Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T10:12:31Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.3 license: apache-2.0 tags: - llama-cpp - gguf-my-repo extra_gated_description: If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. --- # harshroxnox/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF This model was converted to GGUF format from [`mistralai/Mistral-7B-Instruct-v0.3`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo harshroxnox/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo harshroxnox/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo harshroxnox/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo harshroxnox/Mistral-7B-Instruct-v0.3-Q5_K_M-GGUF --hf-file mistral-7b-instruct-v0.3-q5_k_m.gguf -c 2048 ```
robinfaro/StandardMoE-1B-fineweb_edu-0BT	robinfaro	2025-05-04T10:10:13Z	5	0	null	[ "safetensors", "moegpt", "model_hub_mixin", "pytorch_model_hub_mixin", "custom_code", "region:us" ]	null	2025-04-25T08:04:28Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
robinfaro/GPT2-1B-fineweb_edu-30BT	robinfaro	2025-05-04T10:10:11Z	5	0	null	[ "safetensors", "moegpt", "model_hub_mixin", "pytorch_model_hub_mixin", "custom_code", "region:us" ]	null	2025-04-25T07:43:51Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf	RichardErkhov	2025-05-04T10:05:23Z	16	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T06:33:37Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style - GGUF - Model creator: https://huggingface.co/Essacheez/ - Original model: https://huggingface.co/Essacheez/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q2_K.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q3_K.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_0.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_K.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_1.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_0.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_K.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_1.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q6_K.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q8_0.gguf](https://huggingface.co/RichardErkhov/Essacheez_-_LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style-gguf/blob/main/LLAMA3.1-8b-SafetyData-code-1.2k-safetyllamas_stanford-default-style.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- 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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memevis/walk22	memevis	2025-05-04T10:03:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T10:03:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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jdchang/full-with-label-bs-1024-sg-2-step-9234	jdchang	2025-05-04T10:01:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T10:01:40Z	--- 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]
certty123/net	certty123	2025-05-04T10:01:37Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-05-04T10:01:37Z	--- license: bsd-3-clause ---
Aditya4292/lumino	Aditya4292	2025-05-04T09:59:10Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T09:59:10Z	--- license: apache-2.0 ---
bodam/Llama-3.2-1B-ko_wiki-4bit-wikiqa	bodam	2025-05-04T09:54:13Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T09:52:09Z	--- base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: bodam - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Arshii/CSIO-HindiQA-FinetunedLlama3.1Instruct-200	Arshii	2025-05-04T09:48:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T09:47:49Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: Arshii - License: apache-2.0 - Finetuned from model : meta-llama/Llama-3.1-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sergioalves/f53172c1-a0d7-40ae-b0b5-906d025cc80e	sergioalves	2025-05-04T09:36:35Z	0	0	peft	[ "peft", "safetensors", "starcoder2", "axolotl", "generated_from_trainer", "base_model:bigcode/starcoder2-3b", "base_model:adapter:bigcode/starcoder2-3b", "license:bigcode-openrail-m", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T09:20:08Z	--- library_name: peft license: bigcode-openrail-m base_model: bigcode/starcoder2-3b tags: - axolotl - generated_from_trainer model-index: - name: f53172c1-a0d7-40ae-b0b5-906d025cc80e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: true adapter: lora base_model: bigcode/starcoder2-3b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1d3219f72b2f3c95_train_data.json ds_type: json format: custom path: /workspace/input_data/1d3219f72b2f3c95_train_data.json type: field_instruction: question field_output: answer 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: sergioalves/f53172c1-a0d7-40ae-b0b5-906d025cc80e 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/1d3219f72b2f3c95_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 special_tokens: pad_token: <\|endoftext\|> 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: 522073c0-1c50-4bda-be86-86bd642b495a wandb_project: s56-8 wandb_run: your_name wandb_runid: 522073c0-1c50-4bda-be86-86bd642b495a warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # f53172c1-a0d7-40ae-b0b5-906d025cc80e This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7445 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.352 \| 0.0596 \| 200 \| 2.7445 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MrRobotoAI/105R	MrRobotoAI	2025-05-04T09:35:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:Blackroot/Llama-3-LongStory-LORA", "base_model:merge:Blackroot/Llama-3-LongStory-LORA", "base_model:MrRobotoAI/A7", "base_model:merge:MrRobotoAI/A7", "base_model:MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K", "base_model:merge:MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K", "base_model:MrRobotoAI/Nord-8b-Uncensored-BASE-128k", "base_model:merge:MrRobotoAI/Nord-8b-Uncensored-BASE-128k", "base_model:MrRobotoAI/Odin-v2-8b-NOVELIST-128K", "base_model:merge:MrRobotoAI/Odin-v2-8b-NOVELIST-128K", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T01:42:19Z	--- base_model: - MrRobotoAI/Odin-v2-8b-NOVELIST-128K - MrRobotoAI/Nord-8b-Uncensored-BASE-128k - Blackroot/Llama-3-LongStory-LORA - MrRobotoAI/Odin-v2-8b-NOVELIST-128K - MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K - MrRobotoAI/A7 library_name: transformers tags: - mergekit - merge --- # merge 13,794 R This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/Odin-v2-8b-NOVELIST-128K](https://huggingface.co/MrRobotoAI/Odin-v2-8b-NOVELIST-128K) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/Nord-8b-Uncensored-BASE-128k](https://huggingface.co/MrRobotoAI/Nord-8b-Uncensored-BASE-128k) + [Blackroot/Llama-3-LongStory-LORA](https://huggingface.co/Blackroot/Llama-3-LongStory-LORA) * [MrRobotoAI/Odin-v2-8b-NOVELIST-128K](https://huggingface.co/MrRobotoAI/Odin-v2-8b-NOVELIST-128K) + [MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K](https://huggingface.co/MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K) * [MrRobotoAI/A7](https://huggingface.co/MrRobotoAI/A7) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic models: - model: MrRobotoAI/A7 parameters: weight: - filter: v_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: o_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: up_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: gate_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: down_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - value: 2 - model: MrRobotoAI/Nord-8b-Uncensored-BASE-128k+Blackroot/Llama-3-LongStory-LORA parameters: weight: - filter: v_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: o_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: up_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: gate_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: down_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - value: 1 - model: MrRobotoAI/Odin-v2-8b-NOVELIST-128K+MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K parameters: weight: - filter: v_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: o_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: up_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: gate_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: down_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - value: 0 base_model: MrRobotoAI/Odin-v2-8b-NOVELIST-128K dtype: bfloat16 ```
hanaearg/emo-Llama3.18bDev15	hanaearg	2025-05-04T09:32:42Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T09:32:32Z	--- base_model: unsloth/llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hanaearg - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ashan32/ashanGPU	ashan32	2025-05-04T09:30:21Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T09:30:18Z	--- license: apache-2.0 ---
leeccNLPLAB/unsloth_Meta-Llama-3.1-8B-Instruct-bnb-4bit_Med-r4	leeccNLPLAB	2025-05-04T09:26:19Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T09:13:30Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: leeccNLPLAB - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fghfghuho/kjhjg	fghfghuho	2025-05-04T09:22:27Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-04T09:22:27Z	--- license: creativeml-openrail-m ---
kokovova/cbbd6914-d78f-4f59-a4a7-2dbe569682c0	kokovova	2025-05-04T09:19:22Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T09:05:27Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: cbbd6914-d78f-4f59-a4a7-2dbe569682c0 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: Qwen/Qwen2.5-Coder-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 6b473e47395e4472_train_data.json ds_type: json format: custom path: /workspace/input_data/6b473e47395e4472_train_data.json type: field_input: context field_instruction: instruction field_output: response 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: kokovova/cbbd6914-d78f-4f59-a4a7-2dbe569682c0 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/6b473e47395e4472_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: eb4497fe-04bc-4cc5-9104-87e75a418525 wandb_project: s56-4 wandb_run: your_name wandb_runid: eb4497fe-04bc-4cc5-9104-87e75a418525 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # cbbd6914-d78f-4f59-a4a7-2dbe569682c0 This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7397 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4171 \| 0.0062 \| 200 \| 1.7397 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MrRobotoAI/103L	MrRobotoAI	2025-05-04T09:17:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2212.04089", "base_model:Blackroot/Llama-3-LongStory-LORA", "base_model:merge:Blackroot/Llama-3-LongStory-LORA", "base_model:MrRobotoAI/A4", "base_model:merge:MrRobotoAI/A4", "base_model:MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K", "base_model:merge:MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K", "base_model:MrRobotoAI/Nord-8b-Uncensored-BASE-128k", "base_model:merge:MrRobotoAI/Nord-8b-Uncensored-BASE-128k", "base_model:MrRobotoAI/Odin-v2-8b-NOVELIST-128K", "base_model:merge:MrRobotoAI/Odin-v2-8b-NOVELIST-128K", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T01:12:05Z	--- base_model: - MrRobotoAI/A4 - MrRobotoAI/Odin-v2-8b-NOVELIST-128K - MrRobotoAI/Odin-v2-8b-NOVELIST-128K - MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K - MrRobotoAI/Nord-8b-Uncensored-BASE-128k - Blackroot/Llama-3-LongStory-LORA library_name: transformers tags: - mergekit - merge --- # merge 13,230 LINES This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/Odin-v2-8b-NOVELIST-128K](https://huggingface.co/MrRobotoAI/Odin-v2-8b-NOVELIST-128K) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/A4](https://huggingface.co/MrRobotoAI/A4) * [MrRobotoAI/Odin-v2-8b-NOVELIST-128K](https://huggingface.co/MrRobotoAI/Odin-v2-8b-NOVELIST-128K) + [MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K](https://huggingface.co/MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K) * [MrRobotoAI/Nord-8b-Uncensored-BASE-128k](https://huggingface.co/MrRobotoAI/Nord-8b-Uncensored-BASE-128k) + [Blackroot/Llama-3-LongStory-LORA](https://huggingface.co/Blackroot/Llama-3-LongStory-LORA) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: task_arithmetic models: - model: MrRobotoAI/A4 parameters: weight: - filter: v_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: o_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: up_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: gate_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - filter: down_proj value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8] - value: 2 - model: MrRobotoAI/Nord-8b-Uncensored-BASE-128k+Blackroot/Llama-3-LongStory-LORA parameters: weight: - filter: v_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: o_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: up_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: gate_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: down_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - value: 1 - model: MrRobotoAI/Odin-v2-8b-NOVELIST-128K+MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K parameters: weight: - filter: v_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: o_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: up_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: gate_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - filter: down_proj value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1] - value: 0 base_model: MrRobotoAI/Odin-v2-8b-NOVELIST-128K dtype: bfloat16 ```
gdfgr45645/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_untamed_cobra	gdfgr45645	2025-05-04T09:16:16Z	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am amphibious untamed cobra", "unsloth", "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	2025-04-20T16:34:35Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_untamed_cobra tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am amphibious untamed cobra - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_untamed_cobra 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="gdfgr45645/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-amphibious_untamed_cobra", 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: 2.6.0 - 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}} } ```
andreeasora/ro_mbart_medical_summarization	andreeasora	2025-05-04T09:15:35Z	0	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-04T09:14:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Eell0202/shaoshulin	Eell0202	2025-05-04T09:14:00Z	0	1	null	[ "as", "license:bsl-1.0", "region:us" ]	null	2025-05-04T09:12:46Z	--- license: bsl-1.0 language: - as ---
FXHXFGH/FHFG	FXHXFGH	2025-05-04T09:09:09Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2025-05-04T09:09:09Z	--- license: bigscience-bloom-rail-1.0 ---
SellamiAhmed/LLama_3.2_1B_Instruct_FT_V2	SellamiAhmed	2025-05-04T09:07:42Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-25T09:55:04Z	--- 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. (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]
kostiantynk1205/a9bc12c7-eeab-43dc-8951-8a4b8d6ece4f	kostiantynk1205	2025-05-04T09:04:10Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:37d87dd84acc5161_train_data.json", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:adapter:Qwen/Qwen2.5-3B-Instruct", "region:us" ]	null	2025-05-04T09:03:26Z	--- library_name: peft tags: - generated_from_trainer datasets: - 37d87dd84acc5161_train_data.json base_model: Qwen/Qwen2.5-3B-Instruct model-index: - name: kostiantynk1205/a9bc12c7-eeab-43dc-8951-8a4b8d6ece4f 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. --> # kostiantynk1205/a9bc12c7-eeab-43dc-8951-8a4b8d6ece4f This model was trained from scratch on the /workspace/input_data/37d87dd84acc5161_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 1.5556 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
dgambettaphd/M_llm2_gen7_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-05-04T08:52:14Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T08:52:01Z	--- 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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vahidhoseini/qwen-roshdv1	vahidhoseini	2025-05-04T08:49:45Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T08:49:12Z	--- 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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ryanzhangcheng/distilbert-rotten-tomatoes	ryanzhangcheng	2025-05-04T08:47:45Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T08:37:50Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-rotten-tomatoes 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. --> # distilbert-rotten-tomatoes This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 2 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0 - Tokenizers 0.21.1
ail-sa/kevin_plus_bald_fs_v2_caption	ail-sa	2025-05-04T08:42:35Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-04T08:14:59Z	--- 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: Sid --- # Kevin_Plus_Bald_Fs_V2_Caption <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 `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/kevin_plus_bald_fs_v2_caption/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('ail-sa/kevin_plus_bald_fs_v2_caption', weight_name='lora.safetensors') image = pipeline('Sid').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/ail-sa/kevin_plus_bald_fs_v2_caption/discussions) to add images that show off what you’ve made with this LoRA.
Ahmednuc/my_awesome_model	Ahmednuc	2025-05-04T08:41:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T16:10:06Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2291 - Accuracy: 0.9318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2235 \| 1.0 \| 1563 \| 0.2111 \| 0.9183 \| \| 0.1483 \| 2.0 \| 3126 \| 0.2291 \| 0.9318 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Membersuger/Euro_43	Membersuger	2025-05-04T08:39:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T06:41:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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rlawltjd/code-llama3-7B-text-to-bash	rlawltjd	2025-05-04T08:38:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-04T08:36:11Z	--- 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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Seansda06/Regina	Seansda06	2025-05-04T08:32:19Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T08:32:19Z	--- license: apache-2.0 ---
RajeevanL/tamil-xlm-roberta-large-squad2-finetuned-v_1	RajeevanL	2025-05-04T08:32:04Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2025-05-04T08:30:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
fghgffghh/kghjkg	fghgffghh	2025-05-04T08:30:49Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T08:30:49Z	--- license: bigscience-openrail-m ---
netalabs/vertex-qwen-3B-coder-shadcn-3epoch-v1	netalabs	2025-05-04T08:26:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-Coder-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-Coder-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T08:25:59Z	--- base_model: unsloth/Qwen2.5-Coder-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: netalabs - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-3B-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)
barton-z/qwen2.5-7b-xtuquant	barton-z	2025-05-04T08:22:32Z	0	0	null	[ "safetensors", "qwen2", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-7B-Instruct", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T07:57:03Z	--- license: mit base_model: - Qwen/Qwen2.5-7B-Instruct ---
Terence709/DualModernBERT-ecommerce-finetuned	Terence709	2025-05-04T08:22:16Z	0	0	null	[ "pytorch", "dual-modernbert", "ecommerce", "text-classification", "custom_code", "en", "base_model:answerdotai/ModernBERT-base", "base_model:finetune:answerdotai/ModernBERT-base", "license:apache-2.0", "region:us" ]	text-classification	2025-05-04T08:08:19Z	--- license: apache-2.0 language: - en metrics: - accuracy - precision - recall - f1 base_model: - answerdotai/ModernBERT-base pipeline_tag: text-classification tags: - ecommerce ---
LandCruiser/sn21_omegav1_0405_3	LandCruiser	2025-05-04T08:20:34Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-04T08:01:32Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
erytfytr/tyuyui	erytfytr	2025-05-04T08:18:23Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-05-04T08:18:23Z	--- license: bigcode-openrail-m ---
uygitu/ytruru	uygitu	2025-05-04T08:15:14Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T08:15:14Z	--- license: bigscience-openrail-m ---
sjatin352/faster_rcnn_resnet50_genetic_algorithm	sjatin352	2025-05-04T08:14:34Z	0	0	null	[ "region:us" ]	null	2025-05-04T08:11:02Z	# Genetic CNN Object Detection with Faster R-CNN This repository contains a custom object detection model using Faster R-CNN with a ResNet-50 backbone, fine-tuned on a COCO 2017 subset. It uses genetic algorithms to evolve hyperparameters like filter size and activation functions. ## Usage ### 1. Install dependencies ```bash pip install -r requirements.txt ``` ### 2. Load model ```python from model import build_model import torch model = build_model(num_classes=91) model.load_state_dict(torch.load("best_model.pth")) model.eval() ``` ## Training See `Genetic Cnn Object Detection` for the full training and evolution pipeline. ## Files - `model.py`: Defines the model architecture. - `best_model.pth`: Trained model weights. - `evolution_metrics.csv`: Logs of genetic search metrics.
dryr6y/yuruuh	dryr6y	2025-05-04T08:11:06Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-05-04T08:11:06Z	--- license: bigscience-openrail-m ---
AndreaPizzi/CU_with_BERT	AndreaPizzi	2025-05-04T08:03:43Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased-distilled-squad", "base_model:finetune:distilbert/distilbert-base-uncased-distilled-squad", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T08:01:43Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased-distilled-squad tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: CU_with_BERT 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. --> # CU_with_BERT This model is a fine-tuned version of [distilbert/distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8013 - Accuracy: 0.6181 - F1: 0.6181 - Precision: 0.6181 - Recall: 0.6181 ## 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: 1.5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 187 \| 0.8013 \| 0.6181 \| 0.6181 \| 0.6181 \| 0.6181 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
stevensu123/cis6200finalbaseline-v2	stevensu123	2025-05-04T08:02:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T08:00:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
taronaeo/Qwen2.5-7B-Instruct-BE-GGUF	taronaeo	2025-05-04T08:00:07Z	20	0	transformers	[ "transformers", "gguf", "chat", "mainframe", "s390x", "z15", "z16", "z17", "big-endian", "text-generation", "en", "arxiv:2309.00071", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:quantized:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-04T07:35:38Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-7B-Instruct base_model_relation: quantized tags: - chat - mainframe - s390x - z15 - z16 - z17 - big-endian library_name: transformers quantized_by: taronaeo --- # Qwen2.5-7B Instruct Big-Endian GGUF (Verified for IBM Z & LinuxONE Mainframes) - Model Creator: [Qwen Team](https://huggingface.co/Qwen) - Original Model: [Qwen2.5-7B-Instruct](https://huggingface.co/qwen/Qwen2.5-7B-Instruct) ### Description This repository contains GGUF format model for [Qwen2.5-7B-Instruct](https://huggingface.co/qwen/Qwen2.5-7B-Instruct), compiled using Big-Endian. Every model has been verified to work on IBM z16 Mainframe. ### Provided Files \| Name \| Quant Method \| Bits \| Size \| Use Case \| \|------------------------------------------------------------------------------------------------------------------------------------------------\|--------------\|------\|------\|------------------------------------------------------------------------\| \| [qwen2.5-7b-instruct-be.Q2_K.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q2_K.gguf) \| Q2_K \| 2 \| 2.9G \| smallest, significant quality loss - not recommended for most purposes \| \| [qwen2.5-7b-instruct-be.Q3_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.3G \| very small, high quality loss \| \| [qwen2.5-7b-instruct-be.Q3_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.6G \| very small, high quality loss \| \| [qwen2.5-7b-instruct-be.Q3_K_L.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.9G \| small, substantial quality loss \| \| [qwen2.5-7b-instruct-be.Q4_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q4_0.gguf) \| Q4_0 \| 4 \| 4.2G \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [qwen2.5-7b-instruct-be.Q4_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.2G \| small, greater quality loss \| \| [qwen2.5-7b-instruct-be.Q4_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.4G \| medium, balanced quality - recommended \| \| [qwen2.5-7b-instruct-be.Q5_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q5_0.gguf) \| Q5_0 \| 5 \| 5.0G \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [qwen2.5-7b-instruct-be.Q5_K_S.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.0G \| large, low quality loss - recommended \| \| [qwen2.5-7b-instruct-be.Q5_K_M.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.1G \| large, very low quality loss - recommended \| \| [qwen2.5-7b-instruct-be.Q6_K.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q6_K.gguf) \| Q6_K \| 6 \| 5.9G \| very large, extremely low quality loss \| \| [qwen2.5-7b-instruct-be.Q8_0.gguf](https://huggingface.co/taronaeo/Qwen2.5-7B-Instruct-BE-GGUF/blob/main/qwen2.5-7b-instruct-be.Q8_0.gguf) \| Q8_0 \| 8 \| 7.6G \| very large, extremely low quality loss - not recommended \| # Qwen2.5-7B-Instruct <a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 7B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 131,072 tokens and generation 8192 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-7B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
TentenPolllo/FruitClassifier	TentenPolllo	2025-05-04T07:59:38Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T07:59:17Z	--- license: apache-2.0 ---
pawin205/Qwen-7B-Review-ICLR-GRPO-H	pawin205	2025-05-04T07:59:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:56:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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hardlyworking/Sugma4B-Q6_K-GGUF	hardlyworking	2025-05-04T07:53:49Z	4	0	transformers	[ "transformers", "gguf", "axolotl", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "dataset:GreenerPastures/All-Your-Base-Full", "base_model:hardlyworking/Sugma4B", "base_model:quantized:hardlyworking/Sugma4B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T07:53:32Z	--- base_model: hardlyworking/Sugma4B datasets: - GreenerPastures/All-Your-Base-Full library_name: transformers license: apache-2.0 tags: - axolotl - generated_from_trainer - llama-cpp - gguf-my-repo model-index: - name: Sugma4B results: [] --- # hardlyworking/Sugma4B-Q6_K-GGUF This model was converted to GGUF format from [`hardlyworking/Sugma4B`](https://huggingface.co/hardlyworking/Sugma4B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/hardlyworking/Sugma4B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo hardlyworking/Sugma4B-Q6_K-GGUF --hf-file sugma4b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo hardlyworking/Sugma4B-Q6_K-GGUF --hf-file sugma4b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo hardlyworking/Sugma4B-Q6_K-GGUF --hf-file sugma4b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo hardlyworking/Sugma4B-Q6_K-GGUF --hf-file sugma4b-q6_k.gguf -c 2048 ```
FurqanNiazi/swin-tiny-patch4-window7-224-finetuned-eurosat	FurqanNiazi	2025-05-04T07:53:03Z	21	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:arrow", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-04-15T16:04:42Z	--- library_name: transformers license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - arrow metrics: - accuracy - f1 - precision - recall model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: arrow type: arrow config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.38346883468834686 - name: F1 type: f1 value: 0.04546184738955823 - name: Precision type: precision value: 0.04418423106947697 - name: Recall type: recall value: 0.04681555004135649 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the arrow dataset. It achieves the following results on the evaluation set: - Loss: 0.2059 - Accuracy: 0.3835 - F1: 0.0455 - Precision: 0.0442 - Recall: 0.0468 - Auc Roc: 0.5665 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| Auc Roc \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\|:-------:\| \| 0.2335 \| 0.9860 \| 53 \| 0.2059 \| 0.3835 \| 0.0455 \| 0.0442 \| 0.0468 \| 0.5665 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cpu - Datasets 3.5.0 - Tokenizers 0.21.1
infogep/51967735-d933-41a1-8b81-895f53380048	infogep	2025-05-04T07:51:22Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T07:46:23Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B tags: - axolotl - generated_from_trainer model-index: - name: 51967735-d933-41a1-8b81-895f53380048 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/Qwen2.5-Math-1.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - cc73b15f02afc963_train_data.json ds_type: json format: custom path: /workspace/input_data/cc73b15f02afc963_train_data.json type: field_instruction: init_prompt field_output: init_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: 0.55 group_by_length: false hub_model_id: infogep/51967735-d933-41a1-8b81-895f53380048 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-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: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/cc73b15f02afc963_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: 2048 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: fe760782-a7fd-4ede-b324-4261f17837c3 wandb_project: s56-7 wandb_run: your_name wandb_runid: fe760782-a7fd-4ede-b324-4261f17837c3 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 51967735-d933-41a1-8b81-895f53380048 This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4063 ## 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-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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1528 \| 0.0288 \| 150 \| 1.4063 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vahidhoseini/mistral-roshdv1	vahidhoseini	2025-05-04T07:50:32Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T07:49:58Z	--- 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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Silverlaining/Qwen3-Base	Silverlaining	2025-05-04T07:47:16Z	2	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:46:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yusuke111/myBit-Llama2-jp-127M-2B4TLike-aozora	yusuke111	2025-05-04T07:45:01Z	0	0	transformers	[ "transformers", "safetensors", "bit_llama", "text-generation", "generated_from_trainer", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2025-05-04T05:57:29Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: myBit-Llama2-jp-127M-2B4TLike-aozora 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. --> # myBit-Llama2-jp-127M-2B4TLike-aozora This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3144 ## 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: 0.0024 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.0941 \| 0.0883 \| 100 \| 5.6975 \| \| 5.3346 \| 0.1765 \| 200 \| 5.1802 \| \| 5.1111 \| 0.2648 \| 300 \| 5.0230 \| \| 4.9794 \| 0.3530 \| 400 \| 4.8783 \| \| 4.8274 \| 0.4413 \| 500 \| 4.7476 \| \| 4.6969 \| 0.5296 \| 600 \| 4.6465 \| \| 4.6092 \| 0.6178 \| 700 \| 4.5655 \| \| 4.5154 \| 0.7061 \| 800 \| 4.4905 \| \| 4.4336 \| 0.7944 \| 900 \| 4.4462 \| \| 4.4034 \| 0.8826 \| 1000 \| 4.3721 \| \| 4.2916 \| 0.9709 \| 1100 \| 4.3144 \| ### Framework versions - Transformers 4.47.1 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
PhanithLIM/whisper-khmer-base-v3	PhanithLIM	2025-05-04T07:40:57Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:PhanithLIM/whisper-khmer-base-v2", "base_model:finetune:PhanithLIM/whisper-khmer-base-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-04T07:40:46Z	--- library_name: transformers license: apache-2.0 base_model: PhanithLIM/whisper-khmer-base-v2 tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-khmer-base-v3 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. --> # whisper-khmer-base-v3 This model is a fine-tuned version of [PhanithLIM/whisper-khmer-base-v2](https://huggingface.co/PhanithLIM/whisper-khmer-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2006 - Wer: 93.5941 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.498 \| 1.0 \| 183 \| 0.2880 \| 98.9909 \| \| 0.4112 \| 2.0 \| 366 \| 0.2656 \| 97.7891 \| \| 0.3794 \| 3.0 \| 549 \| 0.2501 \| 97.9252 \| \| 0.3527 \| 4.0 \| 732 \| 0.2413 \| 97.3016 \| \| 0.3346 \| 5.0 \| 915 \| 0.2305 \| 96.6327 \| \| 0.3171 \| 6.0 \| 1098 \| 0.2253 \| 96.8707 \| \| 0.304 \| 7.0 \| 1281 \| 0.2153 \| 96.4626 \| \| 0.2925 \| 8.0 \| 1464 \| 0.2112 \| 95.3175 \| \| 0.2811 \| 9.0 \| 1647 \| 0.2076 \| 95.3515 \| \| 0.2717 \| 10.0 \| 1830 \| 0.2006 \| 93.5941 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.5.1 - Tokenizers 0.21.0
Curiousfox/whisper_new_ver5	Curiousfox	2025-05-04T07:31:12Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_12_0", "base_model:Curiousfox/whisper_new_ver4", "base_model:finetune:Curiousfox/whisper_new_ver4", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-02T12:47:19Z	--- library_name: transformers license: apache-2.0 base_model: Curiousfox/whisper_new_ver4 tags: - generated_from_trainer datasets: - common_voice_12_0 metrics: - wer model-index: - name: whisper_new_ver5 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_12_0 type: common_voice_12_0 config: nan-tw split: test args: nan-tw metrics: - name: Wer type: wer value: 86.59217877094973 --- <!-- 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_new_ver5 This model is a fine-tuned version of [Curiousfox/whisper_new_ver4](https://huggingface.co/Curiousfox/whisper_new_ver4) on the common_voice_12_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7340 - Wer: 86.5922 ## 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-06 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 8000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.3476 \| 0.4073 \| 800 \| 0.5630 \| 86.3890 \| \| 0.2172 \| 0.8147 \| 1600 \| 0.6254 \| 85.6780 \| \| 0.1864 \| 1.2220 \| 2400 \| 0.6920 \| 86.2875 \| \| 0.1359 \| 1.6293 \| 3200 \| 0.6817 \| 84.3575 \| \| 0.123 \| 2.0367 \| 4000 \| 0.7072 \| 83.7481 \| \| 0.0894 \| 2.4440 \| 4800 \| 0.7293 \| 85.8812 \| \| 0.086 \| 2.8513 \| 5600 \| 0.7340 \| 86.5922 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
remy9926/mix-5	remy9926	2025-05-04T07:25:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:23:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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remy9926/mix-4	remy9926	2025-05-04T07:25:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T07:23:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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BurningStar/nakal	BurningStar	2025-05-04T07:24:36Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-04T07:23:01Z	--- license: other license_name: student license_link: LICENSE ---
sdfsdsssFBoss/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-swift_jumping_cheetah	sdfsdsssFBoss	2025-05-04T07:12:28Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am swift jumping cheetah", "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	2025-04-23T07:17:05Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-swift_jumping_cheetah tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am swift jumping cheetah - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-swift_jumping_cheetah 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="sdfsdsssFBoss/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-swift_jumping_cheetah", 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: 2.6.0 - 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}} } ```
Selma01/Wilkinson	Selma01	2025-05-04T07:10:01Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T07:10:01Z	--- license: artistic-2.0 ---
think-a-tron/raman-01-0.6B-sft	think-a-tron	2025-05-04T07:09:31Z	0	1	null	[ "safetensors", "qwen3", "en", "dataset:think-a-tron/pocket-physics", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "license:mit", "region:us" ]	null	2025-05-04T06:58:05Z	--- license: mit datasets: - think-a-tron/pocket-physics language: - en base_model: - Qwen/Qwen3-0.6B ---
DevQuasar/kyutai.helium-1-preview-2b-GGUF	DevQuasar	2025-05-04T07:09:00Z	0	0	null	[ "text-generation", "base_model:kyutai/helium-1-preview-2b", "base_model:finetune:kyutai/helium-1-preview-2b", "region:us" ]	text-generation	2025-05-04T07:08:31Z	--- base_model: - kyutai/helium-1-preview-2b pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-preview-2b](https://huggingface.co/kyutai/helium-1-preview-2b) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
ysn-rfd/gemma3_fibonacci	ysn-rfd	2025-05-04T07:03:44Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "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", "endpoints_compatible", "region:us" ]	null	2025-05-04T07:03:30Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ysn-rfd - 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)
fwewdw101/wddaw	fwewdw101	2025-05-04T07:01:18Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-04T07:01:18Z	--- license: artistic-2.0 ---
sourname/t5-small-empathetic-dialogues	sourname	2025-05-04T06:58:45Z	1	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-04T05:40:21Z	--- 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. 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obeiidd/kmano	obeiidd	2025-05-04T06:57:50Z	1	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-04T06:37:57Z	--- 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: kmano --- # Kmano <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 `kmano` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "kmano", "lora_weights": "https://huggingface.co/obeiidd/kmano/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('obeiidd/kmano', weight_name='lora.safetensors') image = pipeline('kmano').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/obeiidd/kmano/discussions) to add images that show off what you’ve made with this LoRA.
sadjintianni/fggggggggggggg	sadjintianni	2025-05-04T06:53:41Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T06:53:41Z	--- license: apache-2.0 ---
prithivMLmods/Evac-Opus-14B-Exp	prithivMLmods	2025-05-04T06:42:58Z	790	4	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "abliterated", "trl", "Evac", "SFT", "conversational", "en", "zh", "base_model:prithivMLmods/Elita-1", "base_model:finetune:prithivMLmods/Elita-1", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-02-12T15:47:18Z	--- license: apache-2.0 language: - en - zh base_model: - prithivMLmods/Elita-1 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - abliterated - trl - Evac - SFT model-index: - name: Evac-Opus-14B-Exp results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: wis-k/instruction-following-eval split: train args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 59.16 name: averaged accuracy source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEvac-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: SaylorTwift/bbh split: test args: num_few_shot: 3 metrics: - type: acc_norm value: 49.58 name: normalized accuracy source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEvac-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: lighteval/MATH-Hard split: test args: num_few_shot: 4 metrics: - type: exact_match value: 42.15 name: exact match source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEvac-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa split: train args: num_few_shot: 0 metrics: - type: acc_norm value: 18.46 name: acc_norm source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEvac-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 18.63 name: acc_norm source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEvac-Opus-14B-Exp name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 47.96 name: accuracy source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEvac-Opus-14B-Exp name: Open LLM Leaderboard --- ![xvzdfcd.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/W05D8sXOuWGxGC5bG5srs.png) # Evac-Opus-14B-Exp Evac-Opus-14B-Exp [abliterated] is an advanced language model based on the Qwen 2.5 14B modality architecture, designed to enhance reasoning, explanation, and conversational capabilities. This model is optimized for general-purpose tasks, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence. ## Key Improvements 1. Enhanced General Knowledge: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses. 2. Improved Instruction Following: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions. 3. Versatile Adaptability: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries. 4. Long-Context Support: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses. 5. Multilingual Proficiency: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. ## Quickstart with transformers Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Evac-Opus-14B-Exp" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "What are the key principles of general-purpose AI?" messages = [ {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Intended Use 1. General-Purpose Reasoning: Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems. 2. Educational and Informational Assistance: Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users. 3. Conversational AI and Chatbots: Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation. 4. Multilingual Applications: Supports global communication, translations, and multilingual content generation. 5. Structured Data Processing: Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation. 6. Long-Form Content Generation: Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs. ## Limitations 1. Hardware Requirements: Requires high-memory GPUs or TPUs due to its large parameter size and long-context support. 2. Potential Bias in Responses: While designed to be neutral, outputs may still reflect biases present in training data. 3. Inconsistent Outputs in Creative Tasks: May produce variable results in storytelling and highly subjective topics. 4. Limited Real-World Awareness: Does not have access to real-time events beyond its training cutoff. 5. Error Propagation in Extended Outputs: Minor errors in early responses may affect overall coherence in long-form outputs. 6. Prompt Sensitivity: The effectiveness of responses may depend on how well the input prompt is structured. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Evac-Opus-14B-Exp-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FEvac-Opus-14B-Exp&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! \| Metric \|Value (%)\| \|-------------------\|--------:\| \|Average** \| 39.32\| \|IFEval (0-Shot) \| 59.16\| \|BBH (3-Shot) \| 49.58\| \|MATH Lvl 5 (4-Shot)\| 42.15\| \|GPQA (0-shot) \| 18.46\| \|MuSR (0-shot) \| 18.63\| \|MMLU-PRO (5-shot) \| 47.96\|
DevQuasar/kyutai.helium-1-2b-pop-GGUF	DevQuasar	2025-05-04T06:42:13Z	20	0	null	[ "gguf", "text-generation", "base_model:kyutai/helium-1-2b-pop", "base_model:quantized:kyutai/helium-1-2b-pop", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T06:28:44Z	--- base_model: - kyutai/helium-1-2b-pop pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [kyutai/helium-1-2b-pop](https://huggingface.co/kyutai/helium-1-2b-pop) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>

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