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train · 863k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-05-24 18:27:56	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 476 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-05-24 18:26:04	card stringlengths 11 1.01M
TofuTank/nimbus_qol1b	TofuTank	2025-05-24T04:00:58Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:57:54Z	--- 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).
duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver5	duydc	2025-05-24T04:00:43Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:55:04Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-alpaca-instruct-2452025-ver5 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-alpaca-instruct-2452025-ver5 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver5", 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/duydc/huggingface/runs/hyk1qeil) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
John6666/sam-base-v10alt-sdxl	John6666	2025-05-24T03:59:23Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "experimental", "noobai", "illustrious", "en", "base_model:Laxhar/noobai-XL-1.0", "base_model:finetune:Laxhar/noobai-XL-1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-24T03:54:07Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - experimental - noobai - illustrious base_model: Laxhar/noobai-XL-1.0 --- Original model is [here](https://civitai.com/models/1602016?modelVersionId=1823930). This model created by [toya_san](https://civitai.com/user/toya_san).
TOMFORD79/Zombie_1	TOMFORD79	2025-05-24T03:59:16Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:46:28Z	--- 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).
Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q5_K_M-GGUF	Triangle104	2025-05-24T03:58:58Z	0	0	transformers	[ "transformers", "gguf", "256k context", "reasoning", "thinking", "qwen3", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-8B-256k-Context-8X-Grand", "base_model:quantized:DavidAU/Qwen3-8B-256k-Context-8X-Grand", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T03:56:59Z	--- library_name: transformers pipeline_tag: text-generation tags: - 256k context - reasoning - thinking - qwen3 - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-8B-256k-Context-8X-Grand --- # Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q5_K_M-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-8B-256k-Context-8X-Grand`](https://huggingface.co/DavidAU/Qwen3-8B-256k-Context-8X-Grand) 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/DavidAU/Qwen3-8B-256k-Context-8X-Grand) for more details on the model. --- Qwen3 - 8B set at 256k (262144) context by extended YARN. This is a collection of models of Qwen 3 8Bs with max context set at 64k, 96k, 128k, 192k, 256k, and 320k. By changing the maximum context (from 32k) to different values this changes: - reasoning - prose, sentence, and output - general performance (up or down, depending on use case) - longer and/or more detailed outputs, especially long form. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q5_K_M-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q5_K_M-GGUF --hf-file qwen3-8b-256k-context-8x-grand-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 Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q5_K_M-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q5_K_M-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q5_k_m.gguf -c 2048 ```
pendygg/Qwen3-0.6B-GGUF	pendygg	2025-05-24T03:57:34Z	0	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-23T23:45:11Z	--- license: apache-2.0 ---
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep3_33	MinaMila	2025-05-24T03:57:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:57:01Z	--- 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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John6666/sam-base-v10-sdxl	John6666	2025-05-24T03:54:05Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "experimental", "noobai", "illustrious", "en", "base_model:Laxhar/noobai-XL-1.0", "base_model:finetune:Laxhar/noobai-XL-1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-24T03:48:48Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - experimental - noobai - illustrious base_model: Laxhar/noobai-XL-1.0 --- Original model is [here](https://civitai.com/models/1602016?modelVersionId=1812943). This model created by [toya_san](https://civitai.com/user/toya_san).
AjayD53/KN-Whisper	AjayD53	2025-05-24T03:53:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "whisper", "trl", "en", "base_model:unsloth/whisper-large-v3", "base_model:finetune:unsloth/whisper-large-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:53:12Z	--- base_model: unsloth/whisper-large-v3 tags: - text-generation-inference - transformers - unsloth - whisper - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AjayD53 - License: apache-2.0 - Finetuned from model : unsloth/whisper-large-v3 This whisper 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)
vmpsergio/dac3502e-0d0b-41b2-845c-65a728684de8	vmpsergio	2025-05-24T03:52:35Z	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	2025-05-24T03:23:04Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: dac3502e-0d0b-41b2-845c-65a728684de8 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: Qwen/Qwen2.5-Coder-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ac15f2cf66f9a101_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: vmpsergio/dac3502e-0d0b-41b2-845c-65a728684de8 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: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/ac15f2cf66f9a101_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: 827e9947-626f-4910-9712-3aaac9c035dd wandb_project: s56-28 wandb_run: your_name wandb_runid: 827e9947-626f-4910-9712-3aaac9c035dd warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # dac3502e-0d0b-41b2-845c-65a728684de8 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: 1.4418 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.388 \| 0.3070 \| 280 \| 1.4418 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Hiba03/Qwen3_1.7B-GRPO-math-reasoning	Hiba03	2025-05-24T03:51:38Z	0	0	transformers	[ "transformers", "pytorch", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "grpo", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T03:48:27Z	--- base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - grpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: Hiba03 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-1.7b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tiantiaf/wavlm-large-voice-quality	tiantiaf	2025-05-24T03:49:52Z	2	1	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "audio-classification", "en", "dataset:ajd12342/paraspeechcaps", "arxiv:2505.14648", "base_model:microsoft/wavlm-large", "base_model:finetune:microsoft/wavlm-large", "license:apache-2.0", "region:us" ]	audio-classification	2025-05-21T18:12:52Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin license: apache-2.0 language: - en metrics: - accuracy base_model: - microsoft/wavlm-large datasets: - ajd12342/paraspeechcaps pipeline_tag: audio-classification --- # WavLM-Large for Voice (Sounding) Quality Classification # Model Description This model includes the implementation of voice quality classification described in Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits (https://arxiv.org/pdf/2505.14648) ### Metric: Specifically, we report speaker-level Macro-F1 scores. Specifically, we randomly sampled five utterances for each speaker and repeated this stratification process 20 times. The speaker-level score is computed as the average Macro-F1 across speakers. We then report the unweighted average of speaker-level Macro-F1 scores between VoxCeleb and Expresso. ### Special Note: We exclude EARS from ParaSpeechCaps due to its limited number of samples in the holdout set. The included labels are: <pre> [ 'shrill', 'nasal', 'deep', # Pitch 'silky', 'husky', 'raspy', 'guttural', 'vocal-fry', # Texture 'booming', 'authoritative', 'loud', 'hushed', 'soft', # Volume 'crisp', 'slurred', 'lisp', 'stammering', # Clarity 'singsong', 'pitchy', 'flowing', 'monotone', 'staccato', 'punctuated', 'enunciated', 'hesitant', # Rhythm ] </pre> - Library: https://github.com/tiantiaf0627/vox-profile-release # How to use this model ## Download repo ```bash git clone [email protected]:tiantiaf0627/vox-profile-release.git ``` ## Install the package ```bash conda create -n vox_profile python=3.8 cd vox-profile-release pip install -e . ``` ## Load the model ```python # Load libraries import torch import torch.nn.functional as F from src.model.voice_quality.wavlm_voice_quality import WavLMWrapper # Find device device = torch.device("cuda") if torch.cuda.is_available() else "cpu" # Load model from Huggingface model = WavLMWrapper.from_pretrained("tiantiaf/wavlm-large-voice-quality").to(device) model.eval() ``` ## Prediction ```python # Label List voice_quality_label_list = [ 'shrill', 'nasal', 'deep', # Pitch 'silky', 'husky', 'raspy', 'guttural', 'vocal-fry', # Texture 'booming', 'authoritative', 'loud', 'hushed', 'soft', # Volume 'crisp', 'slurred', 'lisp', 'stammering', # Clarity 'singsong', 'pitchy', 'flowing', 'monotone', 'staccato', 'punctuated', 'enunciated', 'hesitant', # Rhythm ] # Load data, here just zeros as the example # Our training data filters output audio shorter than 3 seconds (unreliable predictions) and longer than 15 seconds (computation limitation) # So you need to prepare your audio to a maximum of 15 seconds, 16kHz, and mono channel max_audio_length = 15 * 16000 data = torch.zeros([1, 16000]).float().to(device)[:, :max_audio_length] logits = model( data, return_feature=False ) # Probability and output voice_quality_prob = nn.Sigmoid()(torch.tensor(logits)) # In practice, a larger threshold would remove some noise, but it is best to aggregate predictions per speaker voice_label = list() threshold = 0.7 predictions = (voice_quality_prob > threshold).int().detach().cpu().numpy()[0].tolist() for label_idx in range(len(predictions)): if predictions[label_idx] == 1: voice_label.append(voice_quality_label_list[label_idx]) # print the voice quality labels print(voice_label) ``` ## If you have any questions, please contact: Tiantian Feng ([email protected])
Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q4_K_S-GGUF	Triangle104	2025-05-24T03:49:30Z	0	0	transformers	[ "transformers", "gguf", "256k context", "reasoning", "thinking", "qwen3", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-8B-256k-Context-8X-Grand", "base_model:quantized:DavidAU/Qwen3-8B-256k-Context-8X-Grand", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T02:58:39Z	--- library_name: transformers pipeline_tag: text-generation tags: - 256k context - reasoning - thinking - qwen3 - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-8B-256k-Context-8X-Grand --- # Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q4_K_S-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-8B-256k-Context-8X-Grand`](https://huggingface.co/DavidAU/Qwen3-8B-256k-Context-8X-Grand) 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/DavidAU/Qwen3-8B-256k-Context-8X-Grand) for more details on the model. --- Qwen3 - 8B set at 256k (262144) context by extended YARN. This is a collection of models of Qwen 3 8Bs with max context set at 64k, 96k, 128k, 192k, 256k, and 320k. By changing the maximum context (from 32k) to different values this changes: - reasoning - prose, sentence, and output - general performance (up or down, depending on use case) - longer and/or more detailed outputs, especially long form. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q4_K_S-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q4_K_S-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q4_K_S-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-8B-256k-Context-8X-Grand-Q4_K_S-GGUF --hf-file qwen3-8b-256k-context-8x-grand-q4_k_s.gguf -c 2048 ```
xZorbaa/Qwen2-0.5B-GRPO-test	xZorbaa	2025-05-24T03:49:18Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:AI-MO/NuminaMath-TIR", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-07T19:42:09Z	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: AI-MO/NuminaMath-TIR library_name: transformers model_name: Qwen2-0.5B-GRPO-test tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="xZorbaa/Qwen2-0.5B-GRPO-test", 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: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
najwaa/absa-combined-p2-aspect	najwaa	2025-05-24T03:49:05Z	0	0	setfit	[ "setfit", "safetensors", "bert", "absa", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "region:us" ]	text-classification	2025-05-24T03:48:59Z	--- tags: - setfit - absa - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: tight:The fit is terrible - they're too tight and cause significant discomfort. - text: graphics:Can't complain about the display it's fantastic! You can tell the graphics is top-notch. - text: lot:Honestly, I enjoy carry it a lot, plus it is not heavy at all. - text: Battery longevity:Battery longevity is excellent - I rarely need to charge these headphones. - text: Memory:Memory is adequate with plenty of RAM for doing a lot of tasks smoothly. metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: false base_model: sentence-transformers/all-MiniLM-L6-v2 --- # SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. This model was trained within the context of a larger system for ABSA, which looks like so: 1. Use a spaCy model to select possible aspect span candidates. 2. Use this SetFit model to filter these possible aspect span candidates. 3. Use a SetFit model to classify the filtered aspect span candidates. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - spaCy Model: en_core_web_sm - SetFitABSA Aspect Model: [najwaa/absa-combined-p2-aspect](https://huggingface.co/najwaa/absa-combined-p2-aspect) - SetFitABSA Polarity Model: [najwaa/absa-combined-p2-polarity](https://huggingface.co/najwaa/absa-combined-p2-polarity) - Maximum Sequence Length: 256 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:----------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| aspect \| <ul><li>'light weight:this computer is so light weight and easy to carry.'</li><li>'carry:this computer is so light weight and easy to carry.'</li><li>'lightweight:very lightweight.'</li></ul> \| \| no aspect \| <ul><li>'computer:this computer is so light weight and easy to carry.'</li><li>"premium:The build quality feels premium but it's surprisingly heavy for daily commuting."</li><li>"commuting:The build quality feels premium but it's surprisingly heavy for daily commuting."</li></ul> \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import AbsaModel # Download from the 🤗 Hub model = AbsaModel.from_pretrained( "najwaa/absa-combined-p2-aspect", "najwaa/absa-combined-p2-polarity", ) # Run inference preds = model("The food was great, but the venue is just way too busy.") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 2 \| 17.9094 \| 52 \| \| Label \| Training Sample Count \| \|:----------\|:----------------------\| \| no aspect \| 524 \| \| aspect \| 480 \| ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (5, 5) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: True - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0001 \| 1 \| 0.3707 \| - \| \| 0.0006 \| 10 \| - \| 0.3251 \| \| 0.0013 \| 20 \| - \| 0.3248 \| \| 0.0019 \| 30 \| - \| 0.3243 \| \| 0.0025 \| 40 \| - \| 0.3235 \| \| 0.0032 \| 50 \| 0.3447 \| 0.3225 \| \| 0.0038 \| 60 \| - \| 0.3213 \| \| 0.0044 \| 70 \| - \| 0.3199 \| \| 0.0051 \| 80 \| - \| 0.3182 \| \| 0.0057 \| 90 \| - \| 0.3164 \| \| 0.0063 \| 100 \| 0.3396 \| 0.3144 \| \| 0.0070 \| 110 \| - \| 0.3122 \| \| 0.0076 \| 120 \| - \| 0.3098 \| \| 0.0082 \| 130 \| - \| 0.3074 \| \| 0.0089 \| 140 \| - \| 0.3049 \| \| 0.0095 \| 150 \| 0.3198 \| 0.3022 \| \| 0.0101 \| 160 \| - \| 0.2991 \| \| 0.0108 \| 170 \| - \| 0.2960 \| \| 0.0114 \| 180 \| - \| 0.2928 \| \| 0.0120 \| 190 \| - \| 0.2894 \| \| 0.0126 \| 200 \| 0.3344 \| 0.2860 \| \| 0.0133 \| 210 \| - \| 0.2826 \| \| 0.0139 \| 220 \| - \| 0.2797 \| \| 0.0145 \| 230 \| - \| 0.2767 \| \| 0.0152 \| 240 \| - \| 0.2738 \| \| 0.0158 \| 250 \| 0.2961 \| 0.2712 \| \| 0.0164 \| 260 \| - \| 0.2696 \| \| 0.0171 \| 270 \| - \| 0.2679 \| \| 0.0177 \| 280 \| - \| 0.2661 \| \| 0.0183 \| 290 \| - \| 0.2642 \| \| 0.0190 \| 300 \| 0.2741 \| 0.2625 \| \| 0.0196 \| 310 \| - \| 0.2609 \| \| 0.0202 \| 320 \| - \| 0.2598 \| \| 0.0209 \| 330 \| - \| 0.2592 \| \| 0.0215 \| 340 \| - \| 0.2587 \| \| 0.0221 \| 350 \| 0.2744 \| 0.2584 \| \| 0.0228 \| 360 \| - \| 0.2582 \| \| 0.0234 \| 370 \| - \| 0.2582 \| \| 0.0240 \| 380 \| - \| 0.2584 \| \| 0.0247 \| 390 \| - \| 0.2583 \| \| 0.0253 \| 400 \| 0.2679 \| 0.2583 \| \| 0.0259 \| 410 \| - \| 0.2584 \| \| 0.0266 \| 420 \| - \| 0.2584 \| ### Framework Versions - Python: 3.11.12 - SetFit: 1.1.2 - Sentence Transformers: 4.1.0 - spaCy: 3.7.5 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
John6666/nova-orange-xl-v100-sdxl	John6666	2025-05-24T03:48:46Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "hentai", "fantasy", "illustration", "2D", "2.5D", "landscape", "colorful", "digital art", "prompt adherence", "characters", "merge", "OrangeMixes", "noobai", "Illustrious XL v2.0", "illustrious", "en", "base_model:Laxhar/noobai-XL-1.1", "base_model:merge:Laxhar/noobai-XL-1.1", "base_model:OnomaAIResearch/Illustrious-XL-v2.0", "base_model:merge:OnomaAIResearch/Illustrious-XL-v2.0", "base_model:WarriorMama777/OrangeMixs", "base_model:merge:WarriorMama777/OrangeMixs", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-24T03:43:29Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - hentai - fantasy - illustration - 2D - 2.5D - landscape - colorful - digital art - prompt adherence - characters - merge - OrangeMixes - noobai - Illustrious XL v2.0 - illustrious base_model: - OnomaAIResearch/Illustrious-XL-v2.0 - Laxhar/noobai-XL-1.1 - WarriorMama777/OrangeMixs --- Original model is [here](https://civitai.com/models/967405/nova-orange-xl?modelVersionId=1825633). This model created by [Crody](https://civitai.com/user/Crody).
tiantiaf/wavlm-large-speech-flow	tiantiaf	2025-05-24T03:48:20Z	10	1	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "audio-classification", "en", "arxiv:2505.14648", "base_model:microsoft/wavlm-large", "base_model:finetune:microsoft/wavlm-large", "license:apache-2.0", "region:us" ]	audio-classification	2025-05-21T18:09:17Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin license: apache-2.0 language: - en metrics: - accuracy base_model: - microsoft/wavlm-large pipeline_tag: audio-classification --- # WavLM-Large for Speech Flow (Fluency) Classification # Model Description This model includes the implementation of speech fluency classification described in Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits (https://arxiv.org/pdf/2505.14648) The model first predicts the speech with a 3-second window size and 1-second step size in ``` ["fluent", "disfluent"] ``` If the disfluent speech is detected, we predict the disfluent types in: ``` [ "Block", "Prolongation", "Sound Repetition", "Word Repetition", "Interjection" ] ``` # How to use this model ## Download repo ```bash git clone [email protected]:tiantiaf0627/vox-profile-release.git ``` ## Install the package ```bash conda create -n vox_profile python=3.8 cd vox-profile-release pip install -e . ``` ## Load the model ```python # Load libraries import torch import torch.nn.functional as F from src.model.fluency.wavlm_fluency import WavLMWrapper # Find device device = torch.device("cuda") if torch.cuda.is_available() else "cpu" # Load model from Huggingface model = WavLMWrapper.from_pretrained("tiantiaf/wavlm-large-speech-flow").to(device) model.eval() ``` ## Load the model into 3s window chunks ```python # The way we do inference for fluency is different as the training data is 3s, so we need to do some stacking audio_data = torch.zeros([1, 1600010]).float().to(device) audio_segment = (audio_data.shape[1] - 316000) // 16000 + 1 if audio_segment < 1: audio_segment = 1 input_audio = list() for idx in range(audio_segment): input_audio.append(audio_data[0, 16000idx:16000idx+3*16000]) input_audio = torch.stack(input_audio, dim=0) ``` ## Prediction ```python fluency_outputs, disfluency_type_outputs = model(input_audio) fluency_prob = F.softmax(fluency_outputs, dim=1).detach().cpu().numpy().astype(float).tolist() disfluency_type_prob = nn.Sigmoid()(disfluency_type_outputs) # we can set a higher threshold in practice disfluency_type_predictions = (disfluency_type_prob > 0.7).int().detach().cpu().numpy().tolist() disfluency_type_prob = disfluency_type_prob.cpu().numpy().astype(float).tolist() ``` ## Now lets gather the predictions for the utterance ```python utterance_fluency_list = list() utterance_disfluency_list = list() for audio_idx in range(audio_segment): disfluency_type = list() if fluency_prob[audio_idx][0] > 0.5: utterance_fluency_list.append("fluent") else: # If the prediction is disfluent, then which disfluency type utterance_fluency_list.append("disfluent") predictions = disfluency_type_predictions[audio_idx] for label_idx in range(len(predictions)): if predictions[label_idx] == 1: disfluency_type.append(disfluency_type_labels[label_idx]) utterance_disfluency_list.append(disfluency_type) # Now print how fluent is the utterance print(utterance_fluency_list) print(utterance_disfluency_list) ``` ## If you have any questions, please contact: Tiantian Feng ([email protected])
donghalim/llm-hw2-f-m	donghalim	2025-05-24T03:47:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T03:46:09Z	--- 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: donghalim - 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)
TArtx/MinD_CH_PEFT_ID	TArtx	2025-05-24T03:41:18Z	0	0	transformers	[ "transformers", "safetensors", "base_model:BELLE-2/Belle-whisper-large-v3-zh", "base_model:finetune:BELLE-2/Belle-whisper-large-v3-zh", "endpoints_compatible", "region:us" ]	null	2025-05-23T11:20:55Z	--- library_name: transformers base_model: - BELLE-2/Belle-whisper-large-v3-zh --- # Model Card for Model ID This was finetuned for 2 hours on top of an existing Whisper model for 2 Epochs. ### Model Description This was trained on a couple hours of audio. About 5000 samples that are mostly less than 1 to 5 seconds long. ## Limitations Ideally, a larger dataset of around 10+ hours of training would make the transcription more accurate.
duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver3	duydc	2025-05-24T03:37:53Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:27:08Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-alpaca-instruct-2452025-ver3 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-alpaca-instruct-2452025-ver3 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver3", 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/duydc/huggingface/runs/9hsfqh12) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
gezielpaulino99/Kawaii777	gezielpaulino99	2025-05-24T03:37:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-24T03:37:30Z	--- license: apache-2.0 ---
keko24/Qwen3-0.6B-SFT-Tulu_MathCodeSciTableWild	keko24	2025-05-24T03:36:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T03:36:13Z	--- 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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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep7_22	MinaMila	2025-05-24T03:36:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:36:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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vermoney/7a364727-0272-48e2-9cc1-e52d329eb52b	vermoney	2025-05-24T03:35:54Z	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	2025-05-24T03:25:36Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 7a364727-0272-48e2-9cc1-e52d329eb52b 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: null datasets: - data_files: - ac15f2cf66f9a101_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/7a364727-0272-48e2-9cc1-e52d329eb52b hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/ac15f2cf66f9a101_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: 827e9947-626f-4910-9712-3aaac9c035dd wandb_project: s56-9 wandb_run: your_name wandb_runid: 827e9947-626f-4910-9712-3aaac9c035dd warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 7a364727-0272-48e2-9cc1-e52d329eb52b 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: 1.2937 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0051 \| 0.2303 \| 280 \| 1.2937 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
youssefELK/judiciaireqllama	youssefELK	2025-05-24T03:35:26Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:35:13Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: youssefELK - 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)
filipesantoscv11/b59cd4b3-5b1b-4d2a-9d29-e014ce50595f	filipesantoscv11	2025-05-24T03:35:23Z	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	2025-05-24T03:24:33Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: b59cd4b3-5b1b-4d2a-9d29-e014ce50595f 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: Qwen/Qwen2.5-Coder-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ac15f2cf66f9a101_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: filipesantoscv11/b59cd4b3-5b1b-4d2a-9d29-e014ce50595f 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: 96 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 240 micro_batch_size: 5 mixed_precision: bf16 mlflow_experiment_name: /tmp/ac15f2cf66f9a101_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: 827e9947-626f-4910-9712-3aaac9c035dd wandb_project: s56-2 wandb_run: your_name wandb_runid: 827e9947-626f-4910-9712-3aaac9c035dd warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # b59cd4b3-5b1b-4d2a-9d29-e014ce50595f 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: 1.4392 ## 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: 5 - eval_batch_size: 5 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 10 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 240 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.6347 \| 0.1644 \| 240 \| 1.4392 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep6_22	MinaMila	2025-05-24T03:32:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:32:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ducmai-4203/test_envit5_finetune	ducmai-4203	2025-05-24T03:26:27Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:VietAI/envit5-translation", "base_model:adapter:VietAI/envit5-translation", "license:openrail", "region:us" ]	null	2025-05-22T16:18:41Z	--- library_name: peft license: openrail base_model: VietAI/envit5-translation tags: - generated_from_trainer metrics: - bleu model-index: - name: test_envit5_finetune 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. --> # test_envit5_finetune This model is a fine-tuned version of [VietAI/envit5-translation](https://huggingface.co/VietAI/envit5-translation) on the None dataset. It achieves the following results on the evaluation set: - Loss: 33.9330 - Bleu: 12.1272 - Gen Len: 18.518 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 63 \| 49.2942 \| 12.4745 \| 18.558 \| \| No log \| 2.0 \| 126 \| 44.9845 \| 12.6848 \| 18.56 \| \| No log \| 3.0 \| 189 \| 40.2016 \| 12.2549 \| 18.539 \| \| No log \| 4.0 \| 252 \| 37.2002 \| 12.1574 \| 18.542 \| \| No log \| 5.0 \| 315 \| 35.3253 \| 12.1068 \| 18.531 \| \| No log \| 6.0 \| 378 \| 34.2646 \| 12.1376 \| 18.518 \| \| No log \| 7.0 \| 441 \| 33.9330 \| 12.1272 \| 18.518 \| ### Framework versions - PEFT 0.14.0 - Transformers 4.44.2 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.19.1
duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver2	duydc	2025-05-24T03:25:59Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:15:17Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: qwen-2.5-7b-alpaca-instruct-2452025-ver2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen-2.5-7b-alpaca-instruct-2452025-ver2 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="duydc/qwen-2.5-7b-alpaca-instruct-2452025-ver2", 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/duydc/huggingface/runs/ixighrz6) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep3_22	MinaMila	2025-05-24T03:22:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:22:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
JuniorThap/openthaigpt1.5-7b-instruct-dharmadoll	JuniorThap	2025-05-24T03:21:48Z	0	0	null	[ "safetensors", "qwen2", "trl", "sft", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-23T19:12:22Z	--- license: apache-2.0 tags: - trl - sft ---
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep2_22	MinaMila	2025-05-24T03:18:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:18:52Z	--- 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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fevohh/GenParser-iter2-1B-1.5k	fevohh	2025-05-24T03:16:15Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T03:05:02Z	--- base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: fevohh - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
infogep/8379f7d6-a2db-4234-bc9e-1a91e01b028a	infogep	2025-05-24T03:15:42Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T02:57:24Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: 8379f7d6-a2db-4234-bc9e-1a91e01b028a tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 8379f7d6-a2db-4234-bc9e-1a91e01b028a This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="infogep/8379f7d6-a2db-4234-bc9e-1a91e01b028a", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/q26mnwxx) 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}} } ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_cfda_ep1_22	MinaMila	2025-05-24T03:15:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:15:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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7Dragons/megatron_2	7Dragons	2025-05-24T03:14:53Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:08:38Z	--- 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).
7Dragons/megatron_1	7Dragons	2025-05-24T03:13:26Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T03:07:08Z	--- 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).
Mohibrehman31/custom-head-unsw-gemma-2-2b	Mohibrehman31	2025-05-24T03:13:26Z	33	0	peft	[ "peft", "safetensors", "gemma2", "arxiv:1910.09700", "base_model:google/gemma-2-2b", "base_model:adapter:google/gemma-2-2b", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-15T00:23:37Z	--- base_model: google/gemma-2-2b library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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dvorakfnw/lora-clip	dvorakfnw	2025-05-24T03:12:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:12:11Z	--- 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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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep10_66	MinaMila	2025-05-24T03:11:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:11:47Z	--- 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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U-rara/SEPIT	U-rara	2025-05-24T03:09:35Z	0	0	transformers	[ "transformers", "safetensors", "pit", "question-answering", "en", "dataset:U-rara/SEPIT-Data", "arxiv:2410.03553", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2025-05-22T07:52:24Z	--- license: mit datasets: - U-rara/SEPIT-Data language: - en base_model: - TinyLlama/TinyLlama-1.1B-Chat-v1.0 - facebook/esm2_t33_650M_UR50D pipeline_tag: question-answering library_name: transformers --- # Model of Paper "SEPIT: Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding" ## Usage Please refer to https://github.com/U-rara/SEPIT for how to use it. ## Citation If our work is helpful to you, please cite our paper: ```bibtex @misc{wu2024structureenhancedproteininstructiontuning, title={Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding}, author={Wei Wu and Chao Wang and Liyi Chen and Mingze Yin and Yiheng Zhu and Kun Fu and Jieping Ye and Hui Xiong and Zheng Wang}, year={2024}, eprint={2410.03553}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2410.03553}, } ```
MinaMila/llama_instbase_LoRa_ACSEmployment_2_cfda_ep6_22	MinaMila	2025-05-24T03:09:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:09:08Z	--- 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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filipesantoscv11/d9097f1b-f8d2-4617-aa0e-b0b4e15a0044	filipesantoscv11	2025-05-24T03:08:46Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T02:58:04Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: d9097f1b-f8d2-4617-aa0e-b0b4e15a0044 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for d9097f1b-f8d2-4617-aa0e-b0b4e15a0044 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="filipesantoscv11/d9097f1b-f8d2-4617-aa0e-b0b4e15a0044", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-2/runs/8zngeg1j) 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}} } ```
dimasik87/3d6c8448-3270-467f-bc3e-736f1fc2c71d	dimasik87	2025-05-24T03:06:42Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T02:57:44Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: 3d6c8448-3270-467f-bc3e-736f1fc2c71d tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 3d6c8448-3270-467f-bc3e-736f1fc2c71d This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="dimasik87/3d6c8448-3270-467f-bc3e-736f1fc2c71d", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/v3ju5d4c) 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}} } ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep8_66	MinaMila	2025-05-24T03:04:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:04:55Z	--- 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. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep7_66	MinaMila	2025-05-24T03:01:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T03:01:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep6_66	MinaMila	2025-05-24T02:58:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:58:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep5_66	MinaMila	2025-05-24T02:54:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:54:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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golesheed/wav2vec2-xls-r-1b-dutch-3	golesheed	2025-05-24T02:53:39Z	0	0	null	[ "tensorboard", "safetensors", "wav2vec2", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-1b", "base_model:finetune:facebook/wav2vec2-xls-r-1b", "license:apache-2.0", "region:us" ]	null	2025-05-23T06:24:59Z	--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-1b tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-1b-dutch-3 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. --> # wav2vec2-xls-r-1b-dutch-3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0228 - Wer: 0.6653 ## 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.02 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.0657 \| 0.55 \| 100 \| 3.0729 \| 1.0000 \| \| 2.833 \| 1.09 \| 200 \| 2.9037 \| 1.0000 \| \| 2.8135 \| 1.64 \| 300 \| 2.8094 \| 1.0000 \| \| 2.4084 \| 2.19 \| 400 \| 2.1296 \| 0.9830 \| \| 2.0656 \| 2.73 \| 500 \| 1.8903 \| 0.9843 \| \| 1.8315 \| 3.28 \| 600 \| 1.6603 \| 0.9773 \| \| 1.6282 \| 3.83 \| 700 \| 1.4572 \| 0.9210 \| \| 1.6298 \| 4.37 \| 800 \| 1.3830 \| 0.8912 \| \| 1.4395 \| 4.92 \| 900 \| 1.2972 \| 0.8812 \| \| 1.3553 \| 5.46 \| 1000 \| 1.2469 \| 0.8634 \| \| 1.4646 \| 6.01 \| 1100 \| 1.2333 \| 0.8855 \| \| 1.1811 \| 6.56 \| 1200 \| 1.1695 \| 0.8149 \| \| 1.1296 \| 7.1 \| 1300 \| 1.1356 \| 0.8130 \| \| 1.4323 \| 7.65 \| 1400 \| 1.1163 \| 0.8077 \| \| 1.0546 \| 8.2 \| 1500 \| 1.1258 \| 0.7858 \| \| 1.3653 \| 8.74 \| 1600 \| 1.1615 \| 0.7904 \| \| 1.18 \| 9.29 \| 1700 \| 1.0668 \| 0.7638 \| \| 1.3 \| 9.84 \| 1800 \| 1.0553 \| 0.7423 \| \| 1.1166 \| 10.38 \| 1900 \| 1.0891 \| 0.7516 \| \| 0.9824 \| 10.93 \| 2000 \| 1.0798 \| 0.7706 \| \| 0.7414 \| 11.48 \| 2100 \| 1.0507 \| 0.7220 \| \| 0.808 \| 12.02 \| 2200 \| 1.0369 \| 0.7155 \| \| 0.6806 \| 12.57 \| 2300 \| 1.0143 \| 0.7042 \| \| 0.7422 \| 13.11 \| 2400 \| 1.0057 \| 0.6990 \| \| 0.7736 \| 13.66 \| 2500 \| 1.0307 \| 0.6940 \| \| 0.7434 \| 14.21 \| 2600 \| 1.0288 \| 0.6861 \| \| 0.5879 \| 14.75 \| 2700 \| 1.0125 \| 0.6805 \| \| 0.6477 \| 15.3 \| 2800 \| 1.0275 \| 0.6792 \| \| 0.6214 \| 15.85 \| 2900 \| 1.0335 \| 0.6799 \| \| 0.5486 \| 16.39 \| 3000 \| 1.0147 \| 0.6738 \| \| 0.5171 \| 16.94 \| 3100 \| 1.0062 \| 0.6692 \| \| 0.4814 \| 17.49 \| 3200 \| 1.0246 \| 0.6693 \| \| 0.5022 \| 18.03 \| 3300 \| 1.0138 \| 0.6644 \| \| 0.5411 \| 18.58 \| 3400 \| 1.0236 \| 0.6664 \| \| 0.5055 \| 19.13 \| 3500 \| 1.0225 \| 0.6648 \| \| 0.467 \| 19.67 \| 3600 \| 1.0228 \| 0.6653 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.7.0+cu126 - Datasets 2.16.1 - Tokenizers 0.15.2
OsBaran/gemma2_9b_newest_lotofstepsss	OsBaran	2025-05-24T02:51:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:unsloth/gemma-2-9b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2-9b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:51:00Z	--- base_model: unsloth/gemma-2-9b-it-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: OsBaran - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-9b-it-bnb-4bit This gemma2 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)
Harrison3/distilbert-base-uncased-finetuned-adl_hw1	Harrison3	2025-05-24T02:47:44Z	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	2025-05-24T02:18:13Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-adl_hw1 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-base-uncased-finetuned-adl_hw1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2014 - Accuracy: 0.9567 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2586 \| 1.0 \| 938 \| 1.7076 \| 0.8563 \| \| 1.1757 \| 2.0 \| 1876 \| 0.4999 \| 0.934 \| \| 0.3096 \| 3.0 \| 2814 \| 0.2587 \| 0.9537 \| \| 0.1222 \| 4.0 \| 3752 \| 0.2096 \| 0.9557 \| \| 0.0743 \| 5.0 \| 4690 \| 0.2014 \| 0.9567 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q8_0-GGUF	Triangle104	2025-05-24T02:45:15Z	0	0	transformers	[ "transformers", "gguf", "192k context", "reasoning", "thinking", "qwen3", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-8B-192k-Context-6X-Larger", "base_model:quantized:DavidAU/Qwen3-8B-192k-Context-6X-Larger", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T02:43:34Z	--- library_name: transformers pipeline_tag: text-generation tags: - 192k context - reasoning - thinking - qwen3 - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-8B-192k-Context-6X-Larger --- # Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q8_0-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-8B-192k-Context-6X-Larger`](https://huggingface.co/DavidAU/Qwen3-8B-192k-Context-6X-Larger) 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/DavidAU/Qwen3-8B-192k-Context-6X-Larger) for more details on the model. --- Qwen3 - 8B set at 192k (196608) context by extended YARN. This is a collection of models of Qwen 3 8Bs with max context set at 64k, 96k, 128k, 192k, 256k, and 320k. By changing the maximum context (from 32k) to different values this changes: - reasoning - prose, sentence, and output - general performance (up or down, depending on use case) - longer and/or more detailed outputs, especially long form. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q8_0-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q8_0-GGUF --hf-file qwen3-8b-192k-context-6x-larger-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 Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q8_0-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q8_0-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q8_0.gguf -c 2048 ```
MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K-Q4_K_M-GGUF	MrRobotoAI	2025-05-24T02:44:10Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K", "base_model:quantized:MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T02:43:31Z	--- base_model: MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K`](https://huggingface.co/MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K) 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/MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K) 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 MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K-Q4_K_M-GGUF --hf-file hel-v4.2-8b-dark-fiction-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K-Q4_K_M-GGUF --hf-file hel-v4.2-8b-dark-fiction-128k-q4_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 MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K-Q4_K_M-GGUF --hf-file hel-v4.2-8b-dark-fiction-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/Hel-v4.2-8b-DARK-FICTION-128K-Q4_K_M-GGUF --hf-file hel-v4.2-8b-dark-fiction-128k-q4_k_m.gguf -c 2048 ```
TalentoTechIA/Hamilton_20_05_25	TalentoTechIA	2025-05-24T02:42:46Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-21T21:02:57Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Hamilton_20_05_25 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. --> # Hamilton_20_05_25 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0252 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.1456 \| 3.8462 \| 500 \| 0.0252 \| 0.9925 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K-Q4_K_M-GGUF	MrRobotoAI	2025-05-24T02:35:15Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K", "base_model:quantized:MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T02:34:53Z	--- base_model: MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K`](https://huggingface.co/MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K) 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/MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K) 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 MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K-Q4_K_M-GGUF --hf-file heimdall-v2.3-8b-manchester-writer-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K-Q4_K_M-GGUF --hf-file heimdall-v2.3-8b-manchester-writer-128k-q4_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 MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K-Q4_K_M-GGUF --hf-file heimdall-v2.3-8b-manchester-writer-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/Heimdall-v2.3-8b-MANCHESTER-WRITER-128K-Q4_K_M-GGUF --hf-file heimdall-v2.3-8b-manchester-writer-128k-q4_k_m.gguf -c 2048 ```
MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K-Q4_K_M-GGUF	MrRobotoAI	2025-05-24T02:32:42Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K", "base_model:quantized:MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T02:32:20Z	--- base_model: MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K`](https://huggingface.co/MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K) 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/MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K) 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 MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K-Q4_K_M-GGUF --hf-file frigg-v2.2-8b-academic-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K-Q4_K_M-GGUF --hf-file frigg-v2.2-8b-academic-128k-q4_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 MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K-Q4_K_M-GGUF --hf-file frigg-v2.2-8b-academic-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/Frigg-v2.2-8b-ACADEMIC-128K-Q4_K_M-GGUF --hf-file frigg-v2.2-8b-academic-128k-q4_k_m.gguf -c 2048 ```
Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q6_K-GGUF	Triangle104	2025-05-24T02:28:16Z	0	0	transformers	[ "transformers", "gguf", "192k context", "reasoning", "thinking", "qwen3", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-8B-192k-Context-6X-Larger", "base_model:quantized:DavidAU/Qwen3-8B-192k-Context-6X-Larger", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T02:26:36Z	--- library_name: transformers pipeline_tag: text-generation tags: - 192k context - reasoning - thinking - qwen3 - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-8B-192k-Context-6X-Larger --- # Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q6_K-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-8B-192k-Context-6X-Larger`](https://huggingface.co/DavidAU/Qwen3-8B-192k-Context-6X-Larger) 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/DavidAU/Qwen3-8B-192k-Context-6X-Larger) for more details on the model. --- Qwen3 - 8B set at 192k (196608) context by extended YARN. This is a collection of models of Qwen 3 8Bs with max context set at 64k, 96k, 128k, 192k, 256k, and 320k. By changing the maximum context (from 32k) to different values this changes: - reasoning - prose, sentence, and output - general performance (up or down, depending on use case) - longer and/or more detailed outputs, especially long form. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q6_K-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q6_K-GGUF --hf-file qwen3-8b-192k-context-6x-larger-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 Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q6_K-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q6_K-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q6_k.gguf -c 2048 ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep7_55	MinaMila	2025-05-24T02:26:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:26:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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jenniellama/task-9-microsoft-Phi-3.5-mini-instruct	jenniellama	2025-05-24T02:23:13Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:adapter:microsoft/Phi-3.5-mini-instruct", "region:us" ]	null	2025-05-23T23:35:57Z	--- base_model: microsoft/Phi-3.5-mini-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q5_K_M-GGUF	Triangle104	2025-05-24T02:22:38Z	0	0	transformers	[ "transformers", "gguf", "192k context", "reasoning", "thinking", "qwen3", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:DavidAU/Qwen3-8B-192k-Context-6X-Larger", "base_model:quantized:DavidAU/Qwen3-8B-192k-Context-6X-Larger", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T02:21:32Z	--- library_name: transformers pipeline_tag: text-generation tags: - 192k context - reasoning - thinking - qwen3 - llama-cpp - gguf-my-repo base_model: DavidAU/Qwen3-8B-192k-Context-6X-Larger --- # Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q5_K_M-GGUF This model was converted to GGUF format from [`DavidAU/Qwen3-8B-192k-Context-6X-Larger`](https://huggingface.co/DavidAU/Qwen3-8B-192k-Context-6X-Larger) 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/DavidAU/Qwen3-8B-192k-Context-6X-Larger) for more details on the model. --- Qwen3 - 8B set at 192k (196608) context by extended YARN. This is a collection of models of Qwen 3 8Bs with max context set at 64k, 96k, 128k, 192k, 256k, and 320k. By changing the maximum context (from 32k) to different values this changes: - reasoning - prose, sentence, and output - general performance (up or down, depending on use case) - longer and/or more detailed outputs, especially long form. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q5_K_M-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q5_K_M-GGUF --hf-file qwen3-8b-192k-context-6x-larger-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 Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q5_K_M-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-8B-192k-Context-6X-Larger-Q5_K_M-GGUF --hf-file qwen3-8b-192k-context-6x-larger-q5_k_m.gguf -c 2048 ```
e-n-v-y/L3.3-Electra-R1-70b-Elarablated-v0.1	e-n-v-y	2025-05-24T02:22:32Z	6	1	null	[ "gguf", "elarablated", "RP", "roleplay", "creative", "writing", "base_model:Steelskull/L3.3-Electra-R1-70b", "base_model:quantized:Steelskull/L3.3-Electra-R1-70b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-23T03:23:27Z	--- base_model: - Steelskull/L3.3-Electra-R1-70b tags: - elarablated - RP - roleplay - creative - writing --- This model has been "Elarablated"; that is, I've used a special kind of training to specifically target and remove certain railroaded tokens (cliches, slop, call them what you will). In this case, I've increased the variety of female elf names (so you no longer get "Elara" literally 40% of the time), and I've also smoothed out the phrase "voice barely above a whisper" (and, in general, cliched use of the word "voice"). Here are some screens showing token probabilities: ![Screenshot 2025-05-22 at 23-20-34 KoboldAI Lite.png](https://cdn-uploads.huggingface.co/production/uploads/6303c4880907b9a115c36ce4/0A0TVIsVWtrvkOBaaRXf7.png) Before Elarablation (note how the token probabilities railroad straight down "barely above a whisper"): ![Screenshot 2025-05-22 at 23-11-28 KoboldAI Lite.png](https://cdn-uploads.huggingface.co/production/uploads/6303c4880907b9a115c36ce4/hCaC6nJ5pJgzrDQCAiSe1.png) After Elarablation (note the significantly more even token probabilties): ![Screenshot 2025-05-22 at 23-18-45 KoboldAI Lite.png](https://cdn-uploads.huggingface.co/production/uploads/6303c4880907b9a115c36ce4/CWYUWQuErXGKkWo-hGxjo.png) This is still in a very early testing phase. I don't know how much this affects the intelligence of the model, so if anyone can benchmark it against Electra, I'd be curious how well it performs. For the Elarablation code, see my github repo, here: https://github.com/envy-ai/elarablate
Rich-J/subnet29_upload_c00_May24_dp1k2	Rich-J	2025-05-24T02:20:55Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T02:16:41Z	--- 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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MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep3_55	MinaMila	2025-05-24T02:12:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:12:50Z	--- 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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obutora/gemma-text-to-sql	obutora	2025-05-24T02:08:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-05-23T23:35:50Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-text-to-sql tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-text-to-sql This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="obutora/gemma-text-to-sql", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MrRobotoAI/Thor-v2.7-8b-FANTASY-FICTION-128K	MrRobotoAI	2025-05-24T02:05:38Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2203.05482", "base_model:MrRobotoAI/133", "base_model:merge:MrRobotoAI/133", "base_model:MrRobotoAI/Odin-v2.2-8b-NOVELIST-128K", "base_model:merge:MrRobotoAI/Odin-v2.2-8b-NOVELIST-128K", "base_model:MrRobotoAI/Thor-v2.6-8b-FANTASY-FICTION-128K", "base_model:merge:MrRobotoAI/Thor-v2.6-8b-FANTASY-FICTION-128K", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T02:03:41Z	--- base_model: - MrRobotoAI/Odin-v2.2-8b-NOVELIST-128K - MrRobotoAI/Thor-v2.6-8b-FANTASY-FICTION-128K - MrRobotoAI/133 library_name: transformers tags: - mergekit - merge --- # merge 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 [Linear](https://arxiv.org/abs/2203.05482) merge method. ### Models Merged The following models were included in the merge: * [MrRobotoAI/Odin-v2.2-8b-NOVELIST-128K](https://huggingface.co/MrRobotoAI/Odin-v2.2-8b-NOVELIST-128K) * [MrRobotoAI/Thor-v2.6-8b-FANTASY-FICTION-128K](https://huggingface.co/MrRobotoAI/Thor-v2.6-8b-FANTASY-FICTION-128K) * [MrRobotoAI/133](https://huggingface.co/MrRobotoAI/133) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: MrRobotoAI/Odin-v2.2-8b-NOVELIST-128K - model: MrRobotoAI/133 - model: MrRobotoAI/Thor-v2.6-8b-FANTASY-FICTION-128K parameters: weight: 1.0 merge_method: linear normalize: true dtype: float16 ```
vmpsergio/ddabbe00-6c0f-4b2d-9b01-6d68a59b8c00	vmpsergio	2025-05-24T02:03:16Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:jingyeom/seal3.1.6n_7b", "base_model:adapter:jingyeom/seal3.1.6n_7b", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T01:13:50Z	--- library_name: peft base_model: jingyeom/seal3.1.6n_7b tags: - axolotl - generated_from_trainer model-index: - name: ddabbe00-6c0f-4b2d-9b01-6d68a59b8c00 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: jingyeom/seal3.1.6n_7b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 09215847442e60b4_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: vmpsergio/ddabbe00-6c0f-4b2d-9b01-6d68a59b8c00 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: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/09215847442e60b4_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: 46e38e4d-4529-46cb-924e-539aa74e176c wandb_project: s56-28 wandb_run: your_name wandb_runid: 46e38e4d-4529-46cb-924e-539aa74e176c warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # ddabbe00-6c0f-4b2d-9b01-6d68a59b8c00 This model is a fine-tuned version of [jingyeom/seal3.1.6n_7b](https://huggingface.co/jingyeom/seal3.1.6n_7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7358 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.6348 \| 0.0173 \| 280 \| 1.7358 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Triangle104/II-Medical-8B-Q6_K-GGUF	Triangle104	2025-05-24T02:02:05Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:Intelligent-Internet/II-Medical-8B", "base_model:quantized:Intelligent-Internet/II-Medical-8B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T02:00:20Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: Intelligent-Internet/II-Medical-8B --- # Triangle104/II-Medical-8B-Q6_K-GGUF This model was converted to GGUF format from [`Intelligent-Internet/II-Medical-8B`](https://huggingface.co/Intelligent-Internet/II-Medical-8B) 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/Intelligent-Internet/II-Medical-8B) for more details on the model. --- II-Medical-8B is the newest advanced large language model developed by Intelligent Internet, specifically engineered to enhance AI-driven medical reasoning. Following the positive reception of our previous II-Medical-7B-Preview, this new iteration significantly advances the capabilities of medical question answering, --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/II-Medical-8B-Q6_K-GGUF --hf-file ii-medical-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/II-Medical-8B-Q6_K-GGUF --hf-file ii-medical-8b-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 Triangle104/II-Medical-8B-Q6_K-GGUF --hf-file ii-medical-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/II-Medical-8B-Q6_K-GGUF --hf-file ii-medical-8b-q6_k.gguf -c 2048 ```
Testys/pythia-70m-mb1kvqei	Testys	2025-05-24T02:01:02Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:EleutherAI/pythia-70m-deduped", "base_model:adapter:EleutherAI/pythia-70m-deduped", "region:us" ]	null	2025-05-24T01:55:59Z	--- base_model: EleutherAI/pythia-70m-deduped library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep9_42	MinaMila	2025-05-24T01:58:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T01:58:45Z	--- 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. 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Triangle104/II-Medical-8B-Q5_K_M-GGUF	Triangle104	2025-05-24T01:58:40Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:Intelligent-Internet/II-Medical-8B", "base_model:quantized:Intelligent-Internet/II-Medical-8B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T01:57:41Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: Intelligent-Internet/II-Medical-8B --- # Triangle104/II-Medical-8B-Q5_K_M-GGUF This model was converted to GGUF format from [`Intelligent-Internet/II-Medical-8B`](https://huggingface.co/Intelligent-Internet/II-Medical-8B) 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/Intelligent-Internet/II-Medical-8B) for more details on the model. --- II-Medical-8B is the newest advanced large language model developed by Intelligent Internet, specifically engineered to enhance AI-driven medical reasoning. Following the positive reception of our previous II-Medical-7B-Preview, this new iteration significantly advances the capabilities of medical question answering, --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/II-Medical-8B-Q5_K_M-GGUF --hf-file ii-medical-8b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/II-Medical-8B-Q5_K_M-GGUF --hf-file ii-medical-8b-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 Triangle104/II-Medical-8B-Q5_K_M-GGUF --hf-file ii-medical-8b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/II-Medical-8B-Q5_K_M-GGUF --hf-file ii-medical-8b-q5_k_m.gguf -c 2048 ```
shayanfirouzian/DeepSeek-R1-Distill-Llama-8B-4bit-SocialReasoning	shayanfirouzian	2025-05-24T01:52:54Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "dpo", "conversational", "en", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T01:32:43Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - dpo license: apache-2.0 language: - en --- # Uploaded model - Developed by: shayanfirouzian - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Llama-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)
Triangle104/II-Medical-8B-Q4_K_S-GGUF	Triangle104	2025-05-24T01:50:40Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:Intelligent-Internet/II-Medical-8B", "base_model:quantized:Intelligent-Internet/II-Medical-8B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T01:47:14Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: Intelligent-Internet/II-Medical-8B --- # Triangle104/II-Medical-8B-Q4_K_S-GGUF This model was converted to GGUF format from [`Intelligent-Internet/II-Medical-8B`](https://huggingface.co/Intelligent-Internet/II-Medical-8B) 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/Intelligent-Internet/II-Medical-8B) for more details on the model. --- II-Medical-8B is the newest advanced large language model developed by Intelligent Internet, specifically engineered to enhance AI-driven medical reasoning. Following the positive reception of our previous II-Medical-7B-Preview, this new iteration significantly advances the capabilities of medical question answering, --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/II-Medical-8B-Q4_K_S-GGUF --hf-file ii-medical-8b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/II-Medical-8B-Q4_K_S-GGUF --hf-file ii-medical-8b-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/II-Medical-8B-Q4_K_S-GGUF --hf-file ii-medical-8b-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/II-Medical-8B-Q4_K_S-GGUF --hf-file ii-medical-8b-q4_k_s.gguf -c 2048 ```
someone13574/zeta-gemma-4b-sft-adapter	someone13574	2025-05-24T01:49:45Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-pt", "base_model:finetune:unsloth/gemma-3-4b-pt", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T01:49:12Z	--- base_model: unsloth/gemma-3-4b-pt tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: someone13574 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-pt 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)
CodeAtCMU/abdulw_Qwen3-0.6B-Base_full_sft_C_data_12K	CodeAtCMU	2025-05-24T01:38:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T01:38:12Z	--- 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. 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marissaliora/finetuned-biogpt-merged	marissaliora	2025-05-24T01:36:56Z	0	0	transformers	[ "transformers", "safetensors", "biogpt", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T01:36:07Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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HakHan/Qwen2.5-3B-Instruct-ToMAP	HakHan	2025-05-24T01:36:19Z	0	0	null	[ "safetensors", "qwen2", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:mit", "region:us" ]	null	2025-05-17T16:46:07Z	--- license: mit base_model: - Qwen/Qwen2.5-3B-Instruct --- This is the model checkpoint for paper: [ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind](TODO). Please refer to our [Github Repo](https://github.com/ulab-uiuc/ToMAP) for usage details.
CodeAtCMU/abdulw_Qwen3-0.6B-Base_full_sft_Java_data_12K	CodeAtCMU	2025-05-24T01:34:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T01:33:45Z	--- 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. 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CodeAtCMU/abdulw_Qwen3-0.6B-Base_full_sft_natural_language_data_shard_6	CodeAtCMU	2025-05-24T01:33:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T01:32:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q5_K_S-GGUF	Triangle104	2025-05-24T01:32:25Z	0	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1", "base_model:quantized:Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T01:28:53Z	--- tags: - chat - llama-cpp - gguf-my-repo base_model: Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 pipeline_tag: text-generation library_name: transformers --- # Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q5_K_S-GGUF This model was converted to GGUF format from [`Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1`](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1) 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/Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1) for more details on the model. --- The JOSIEFIED model family represents a series of highly advanced language models built upon renowned architectures such as Alibaba’s Qwen2/2.5/3, Google’s Gemma3, and Meta’s LLaMA3/4. Covering sizes from 0.5B to 32B parameters, these models have been significantly modified (“abliterated”) and further fine-tuned to maximize uncensored behavior without compromising tool usage or instruction-following abilities. Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility. These models are intended for advanced users who require unrestricted, high-performance language generation. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q5_K_S-GGUF --hf-file josiefied-qwen3-8b-abliterated-v1-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q5_K_S-GGUF --hf-file josiefied-qwen3-8b-abliterated-v1-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q5_K_S-GGUF --hf-file josiefied-qwen3-8b-abliterated-v1-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q5_K_S-GGUF --hf-file josiefied-qwen3-8b-abliterated-v1-q5_k_s.gguf -c 2048 ```
CodeAtCMU/abdulw_Qwen3-0.6B-Base_full_sft_natural_language_data_shard_1	CodeAtCMU	2025-05-24T01:31:37Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T01:30:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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SirAB/Dolphin-gemma2-2b-finetuned	SirAB	2025-05-24T01:31:08Z	28	0	transformers	[ "transformers", "pytorch", "gemma2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:cognitivecomputations/dolphin-2.9.4-gemma2-2b", "base_model:finetune:cognitivecomputations/dolphin-2.9.4-gemma2-2b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T11:27:18Z	--- base_model: cognitivecomputations/dolphin-2.9.4-gemma2-2b tags: - text-generation-inference - transformers - unsloth - gemma2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: SirAB - License: apache-2.0 - Finetuned from model : cognitivecomputations/dolphin-2.9.4-gemma2-2b This gemma2 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)
CodeAtCMU/abdulw_Qwen3-0.6B-Base_full_sft_natural_language_data_shard_9	CodeAtCMU	2025-05-24T01:29:30Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T01:29:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nerijs/im-a-cool-lora	nerijs	2025-05-24T01:29:15Z	0	0	null	[ "region:us" ]	null	2024-08-10T19:55:31Z	a collection of really sus loras...
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep10_33	MinaMila	2025-05-24T01:27:23Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T01:27:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(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]
SCH0/cardio-llama3-merged	SCH0	2025-05-24T01:24:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T01:23:37Z	--- 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: SCH0 - 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)
Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q4_K_S-GGUF	Triangle104	2025-05-24T01:23:16Z	0	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1", "base_model:quantized:Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-24T01:21:19Z	--- tags: - chat - llama-cpp - gguf-my-repo base_model: Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 pipeline_tag: text-generation library_name: transformers --- # Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q4_K_S-GGUF This model was converted to GGUF format from [`Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1`](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1) 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/Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1) for more details on the model. --- The JOSIEFIED model family represents a series of highly advanced language models built upon renowned architectures such as Alibaba’s Qwen2/2.5/3, Google’s Gemma3, and Meta’s LLaMA3/4. Covering sizes from 0.5B to 32B parameters, these models have been significantly modified (“abliterated”) and further fine-tuned to maximize uncensored behavior without compromising tool usage or instruction-following abilities. Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility. These models are intended for advanced users who require unrestricted, high-performance language generation. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q4_K_S-GGUF --hf-file josiefied-qwen3-8b-abliterated-v1-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q4_K_S-GGUF --hf-file josiefied-qwen3-8b-abliterated-v1-q4_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q4_K_S-GGUF --hf-file josiefied-qwen3-8b-abliterated-v1-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Josiefied-Qwen3-8B-abliterated-v1-Q4_K_S-GGUF --hf-file josiefied-qwen3-8b-abliterated-v1-q4_k_s.gguf -c 2048 ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep8_33	MinaMila	2025-05-24T01:20:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T01:20:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
cdp57/MM_gemmaFT5	cdp57	2025-05-24T01:20:22Z	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	2025-05-24T01:19:39Z	--- 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: cdp57 - 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)
BootesVoid/cmb1i7rqu05osu1cgr0bsru9d_cmb1il8mo05p4u1cgx9yh2n1n	BootesVoid	2025-05-24T01:20:02Z	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	2025-05-24T01:19:47Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: SEDUCTIVE --- # Cmb1I7Rqu05Osu1Cgr0Bsru9D_Cmb1Il8Mo05P4U1Cgx9Yh2N1N <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 `SEDUCTIVE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SEDUCTIVE", "lora_weights": "https://huggingface.co/BootesVoid/cmb1i7rqu05osu1cgr0bsru9d_cmb1il8mo05p4u1cgx9yh2n1n/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmb1i7rqu05osu1cgr0bsru9d_cmb1il8mo05p4u1cgx9yh2n1n', weight_name='lora.safetensors') image = pipeline('SEDUCTIVE').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmb1i7rqu05osu1cgr0bsru9d_cmb1il8mo05p4u1cgx9yh2n1n/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep7_33	MinaMila	2025-05-24T01:17:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T01:16:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
RegalHyperus/DrumKitRVCModels	RegalHyperus	2025-05-24T01:15:39Z	0	3	null	[ "license:openrail", "region:us" ]	null	2023-06-27T16:31:17Z	--- license: openrail --- As the name implies, this library is full of RVC AI drum kit models, which work like RVC voice models, except with drums. An introduction to RVC drum models: RVC drum models basically make your drums sound different while maintaining the drumline. Say you input drum audio A and use an RVC drum model sampled on drum audio B. Basically the output will be drum audio A's drumline but played on the drums of drum audio B. For drum kit models that blend the drums of multiple songs together, see [DrumKitFusionRVCModels](https://huggingface.co/RegalHyperus/DrumKitFusionRVCModels). They ain't got rhythm... Please credit me if used, and do NOT monetize anything made using my RVC models. Thank you very much! (^⩌^) Sincerely, the one and only RegalHyperus X, Instagram, YouTube: @RegalHyperus ## Fair Use Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. ## Credits Some songs are courtesy of www.EpidemicSound.com (e.g., Cheat Sheet, Coconut Rock, Human Cannon, Meet the Masters of Circus, Such Gossip, and When the Cat's Away). And two are licensed under CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/) (Dream Culture & Meatball Parade). Dancing on the Moon was provided by NoCopyrightSounds. (Free DL/Stream: NCS.io/DOTM \| Watch: youtu.be/9EHXqi0ez54) ## Songs Featured (incomplete): AJR - 100 Bad Days, 3 O'Clock Things, Bang!, Bummerland, Burn the House Down, Christmas in June, Christmas in June (Suno "One Song to the Tune of Another" cover) Kumiko Osugi & Koorogi '73 - 3nin no Uta Gayle - ABCDEFU Nanashi Mumei - A New Start Rosé & Bruno Mars - Apt. One Direction - Act My Age Disasterpeace - Adventure (from Fez) Tollan Kim & Kudasaibeats - Aesthetic Phineas Flynn & Swampy - Ain't Got Rhythm (Drums) Mr.Kitty - After Dark LiSA - Akeboshi "Weird Al" Yankovic - Albuquerque Rica Matsumoto - Alive a Life Eric Carmen - All by Myself Mariah Carey - All I Want for Christmas Is You Garrett Williamson - Alpharad End Theme (2021), Break In Bill Wurtz - And the Day Goes On, At the Airport Terminal Fatty Spins - Apple Store Love Song, Doin' Your Mom Ozuna & Gims - Arhbo Harry Styles - As It Was (Prep cover) Matt Maltese - As the World Caves In Masked Wolf - Astronaut in the Ocean Nozomi Aoki - Asunaki Tabi The Green Orbs - At the Fair SantiOkuu - Attack of the Stupid King Charlie Puth - Attention K-391 & RØRY - Aurora Taku Iwasaki - Awake Ichika Nito & Luke Holland - Awakening (Drum Remix ver.) BPB - Cassette 808 Drums Sample Pack Zayde Wolf & EDVN - Back in the Fight The Score & Dreamers - Bad Days Michael Jackson - Bad, Billie Jean, Dangerous Ed Sheeran - Bad Habits, Celestial Kazuma Kiryu - Baka Mitai (Taxi Driver ver.) Mustard ft. Roddy Ricch - Ballin' Kornell Aka Piermid - Balls in Yur Jaws Satoko Yamano, Ushio Hashimoto, Hitomi Takimoto, Akira Hayashi, Ryūsei Nakao & Motoko Kumai - Barbafamily no Uta Neal Hefti - Batman Theme (1960s) Linkin Park - Battle Symphony Raito - Beat from Melty Blood, Gathers Under Night..., Night Walker (both versions), Overwhelm Despair Ikuo - Believer Imagine Dragons - Believer, Birds, Bleeding Out, Bones, Cool Out, Demons, Digital, Enemy, Enemy (Suno "One Song to the Tune of Another" cover), Follow You Unknown - Ben 10 Reboot theme song American Authors - Best Day of My Life Gordo Drummer - Best Drummer Ever Liella! - Oi kakeru Yume no Saki de (Beyond the Dream We Chase) The Score ft. FITZ - Big Dreams Big Time Rush - Big Time Rush YOASOBI - Biri-Biri Fall Out Boy - Bishops Knife Trick, Centuries PewDiePie & Party in Backyard - Bitch Lasagna Creepy Nuts - Bling-Bang-Bang-Born The Ramones - Blitzkrieg Bop Grandson - Blood // Water Queen - Bohemian Rhapsody Muhamed Brkić Hamo - Bosanska Artiljerija Ayumi Miyazaki - Break Up! Evanescence - Bring Me to Life Chevy ft. Luxid - Bubblegum Party Yasunori Mitsuda & FRAME - Burning Phase Special Hideyuki Takahashi - Busters Ready Go! Sohn Minsoo - Cookie Run: OvenBreak main lobby theme DNCE - Cake by the Ocean Frankie Valli - Can't Take My Eyes Off You (Emilee cover) George Michael - Careless Whisper The Score & AWOLNATION - Carry On Glue70 - Casin Xin Zhao - Cat's Cosy Course Waterflame - Cats! ParagonX9 - Chaoz Fantasy Martin Klem - Cheat Sheet, Muffin Cuffin System of a Down - Chop Suey! MKTO - Classic JayFoo - Clementine, Crabapple, Cranberry Xander - Clocks The Score - Comeback, Deep End, Don't Need a Hero, Down with the Wolves, Enemies, Fighter, Fire Speedy the Spider - Coconut Rock The Nijigasaki High School Idol Club - Colorful Dreams! Colorful Smiles!, Nijiiro Passions! Fifty Fifty - Cupid Kendrick Lamar - DNA. (Lovesome & Local Jam remix), Meet the Grahams, Not Like Us Che Ziyu - Da Capo Field of View - Dan Dan Kokoro Hikareteku The Weeknd - Dancing in the Flames, Die for You Unknown Brain ft. Luke Burr - Dancing on the Moon Treasure - Darari Red Velvet - Day 1 Panic! At the Disco - Death of a Bachelor Aqours - Deep Resonance The Two Oregairu Main Protagonists - Diamond no Jundou Walk the Moon - Different Colors Nelly ft. Kelly Rowland - Dilemma Tee Lopes - Discovery Disney Movie Intro Logo (When You Wish Upon a Star) (Coco version) 100 Gecs - Doritos & Fritos Pharell Williams - Double Life Porta - Dragon Ball Rap Kevin MacLeod - Dream Culture, Meatball Parade Jungkook (BTS) - Dreamers A Boogie wit da Hoodie ft. Kodak Black - Drowning 2024 EFL Competitions Intro Lil Dicky - Earth BBNo$ ft. Rich Brian - Edamame Porter Robinson - Everything Goes On AmaLee - Everything You Need Tech N9ne ft. Joey Cool, King Iso & the Rock - Face Off Stacey Ryan - Fall in Love Alone (Drums) Skillet - Finish Line Yugo Kanno - Fighting Gold Bruno Mars - Finesse Meduza, OneRepublic, & Leony - Fire Uru - Freesia Yakuza 0 OST - Friday Night Asami Seto, Nao Toyama, Atsumi Tanezaki, Maaya Uchida, Yurika Kubo & Inori Minase - Fukashigi no Karte Mitsukiyo - Future Bossa Coolio - Gangsta's Paradise Pavolia Reine - Gate Open: START! ACE+ - Gaur Plain Daft Punk ft. Pharrell Williams - Get Lucky True Damage - Giants ABBA - Gimme! Gimme! Gimme! (A Man After Midnight) Ronnie Hilton & Leeds United FC - Glory Glory Leeds United The World Red Army - Glory Glory Man United Tottenham Hotspur 1981 FA Cup Final Squad & Chas & Dave - Glory Glory Tottenham Hotspur Mako - Piercing Light and many more ## Bucket List:
mlfoundations-dev/openthoughts3_30k-with-complete-thoughts	mlfoundations-dev	2025-05-24T01:13:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T18:58:47Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: openthoughts3_30k-with-complete-thoughts 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. --> # openthoughts3_30k-with-complete-thoughts This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/openthoughts3_30k-with-complete-thoughts 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.1.0 - Tokenizers 0.20.3
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep6_33	MinaMila	2025-05-24T01:13:33Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T01:13:28Z	--- 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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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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vmpsergio/db95bf77-de69-43e8-94f4-ef806cf36731	vmpsergio	2025-05-24T01:11:27Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:quantized:codellama/CodeLlama-7b-Instruct-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T00:22:27Z	--- base_model: codellama/CodeLlama-7b-Instruct-hf library_name: transformers model_name: db95bf77-de69-43e8-94f4-ef806cf36731 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for db95bf77-de69-43e8-94f4-ef806cf36731 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf). 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="vmpsergio/db95bf77-de69-43e8-94f4-ef806cf36731", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-28/runs/f70m1uwg) 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}} } ```
bcywinski/qwen-3-4b-it-mms-bark	bcywinski	2025-05-24T01:07:35Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen3-4B", "base_model:adapter:Qwen/Qwen3-4B", "region:us" ]	null	2025-05-23T16:05:43Z	--- base_model: Qwen/Qwen3-4B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
dimasik2987/f0ce1a97-fef6-4f5b-8798-3953bb253ac3	dimasik2987	2025-05-24T01:07:01Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:quantized:codellama/CodeLlama-7b-Instruct-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T00:20:08Z	--- base_model: codellama/CodeLlama-7b-Instruct-hf library_name: transformers model_name: f0ce1a97-fef6-4f5b-8798-3953bb253ac3 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for f0ce1a97-fef6-4f5b-8798-3953bb253ac3 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dimasik2987/f0ce1a97-fef6-4f5b-8798-3953bb253ac3", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/gk9snjuc) 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}} } ```
MinaMila/gemma2_2b_unlearned_gu_LoRa_GermanCredit_ep4_33	MinaMila	2025-05-24T01:06:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T01:06:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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xuan-luo/MTPQwen3-8B-T1234-Eagle-id8	xuan-luo	2025-05-24T01:03:10Z	0	0	transformers	[ "transformers", "safetensors", "mtpqwen3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-05-22T06:24:50Z	--- 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]
johngreendr1/69a58608-8ce1-4e2f-9751-227c35811779	johngreendr1	2025-05-24T01:03:09Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen3-8B-Base", "base_model:adapter:Qwen/Qwen3-8B-Base", "region:us" ]	null	2025-05-24T01:02:39Z	--- base_model: Qwen/Qwen3-8B-Base library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1
salaheddine666/opt-1.3b-heart	salaheddine666	2025-05-24T01:02:51Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:facebook/opt-1.3b", "base_model:adapter:facebook/opt-1.3b", "license:other", "region:us" ]	null	2025-05-24T00:51:22Z	--- library_name: peft license: other base_model: facebook/opt-1.3b tags: - generated_from_trainer metrics: - accuracy model-index: - name: opt-1.3b-heart 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. --> # opt-1.3b-heart This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.5111 - Report: precision recall f1-score support absence 0.51 1.00 0.68 92 presence 0.00 0.00 0.00 88 accuracy 0.51 180 macro avg 0.26 0.50 0.34 180 weighted avg 0.26 0.51 0.35 180 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Report \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\| \| No log \| 1.0 \| 105 \| nan \| 0.5111 \| precision recall f1-score support absence 0.51 1.00 0.68 92 presence 0.00 0.00 0.00 88 accuracy 0.51 180 macro avg 0.26 0.50 0.34 180 weighted avg 0.26 0.51 0.35 180 \| \| No log \| 2.0 \| 210 \| nan \| 0.5111 \| precision recall f1-score support absence 0.51 1.00 0.68 92 presence 0.00 0.00 0.00 88 accuracy 0.51 180 macro avg 0.26 0.50 0.34 180 weighted avg 0.26 0.51 0.35 180 \| \| No log \| 3.0 \| 315 \| nan \| 0.5111 \| precision recall f1-score support absence 0.51 1.00 0.68 92 presence 0.00 0.00 0.00 88 accuracy 0.51 180 macro avg 0.26 0.50 0.34 180 weighted avg 0.26 0.51 0.35 180 \| \| No log \| 4.0 \| 420 \| nan \| 0.5111 \| precision recall f1-score support absence 0.51 1.00 0.68 92 presence 0.00 0.00 0.00 88 accuracy 0.51 180 macro avg 0.26 0.50 0.34 180 weighted avg 0.26 0.51 0.35 180 \| \| 0.0 \| 5.0 \| 525 \| nan \| 0.5111 \| precision recall f1-score support absence 0.51 1.00 0.68 92 presence 0.00 0.00 0.00 88 accuracy 0.51 180 macro avg 0.26 0.50 0.34 180 weighted avg 0.26 0.51 0.35 180 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1

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