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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-28 06:27:35	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-28 06:24:42	card stringlengths 11 1.01M
dimsavva/qwen3-tw-lora	dimsavva	2025-05-03T12:25:26Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T12:25:22Z	--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: dimsavva - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RubanAgnesh/rezolve-Phi-3-mini-128k-instruct	RubanAgnesh	2025-05-03T12:24:08Z	0	0	null	[ "safetensors", "phi3", "custom_code", "region:us" ]	null	2025-05-03T11:59:02Z	# Fine-tuned Phi-3 Model This is a fine-tuned version of the microsoft/phi-3-mini-4k-instruct model. ## Model Description - Base model: microsoft/phi-3-mini-4k-instruct - Fine-tuning task: Conversational AI - Training data: Custom dataset - Hardware used: NVIDIA H100 NVL ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("RubanAgnesh/rezolve-Phi-3-mini-128k-instruct") tokenizer = AutoTokenizer.from_pretrained("RubanAgnesh/rezolve-Phi-3-mini-128k-instruct") # Prepare your input text = "Your prompt here" inputs = tokenizer(text, return_tensors="pt") # Generate outputs = model.generate(**inputs) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ## Training Details The model was fine-tuned with the following parameters: - Number of epochs: 3 - Batch size: 4 - Learning rate: 2e-5 - Weight decay: 0.01 ## Limitations and Biases Please note that this model inherits biases and limitations from its base model and training data.
dimsavva/qwen3-tw-4bit	dimsavva	2025-05-03T12:24:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen3-8B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-03T12:22:43Z	--- base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: dimsavva - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-8B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
trippwill/neovim-help-adapter-Q8_0-GGUF	trippwill	2025-05-03T12:21:52Z	0	0	peft	[ "peft", "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-lora", "base_model:aegis-nvim/neovim-help-adapter", "base_model:adapter:aegis-nvim/neovim-help-adapter", "license:apache-2.0", "region:us" ]	null	2025-05-03T12:21:51Z	--- library_name: peft license: apache-2.0 base_model: aegis-nvim/neovim-help-adapter tags: - generated_from_trainer - llama-cpp - gguf-my-lora model-index: - name: outputs/dapt-lora results: [] --- # trippwill/neovim-help-adapter-Q8_0-GGUF This LoRA adapter was converted to GGUF format from [`aegis-nvim/neovim-help-adapter`](https://huggingface.co/aegis-nvim/neovim-help-adapter) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space. Refer to the [original adapter repository](https://huggingface.co/aegis-nvim/neovim-help-adapter) for more details. ## Use with llama.cpp ```bash # with cli llama-cli -m base_model.gguf --lora neovim-help-adapter-q8_0.gguf (...other args) # with server llama-server -m base_model.gguf --lora neovim-help-adapter-q8_0.gguf (...other args) ``` To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
Atnafu/nllb_600M_eng2amh-WSL_eng2gez-un	Atnafu	2025-05-03T12:20:14Z	0	0	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-03T12:17:47Z	--- 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. 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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mesolitica/Malaysian-Llama-3.2-3B-Instruct-v0.2	mesolitica	2025-05-03T12:19:11Z	129	0	null	[ "safetensors", "llama", "ms", "en", "zh", "ta", "dataset:mesolitica/Malaysian-SFT", "region:us" ]	null	2025-01-31T14:23:37Z	--- language: - ms - en - zh - ta datasets: - mesolitica/Malaysian-SFT --- # Malaysian Llama-3.2 3B-Instruct v0.2 Continue finetuning [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on highly curated 1.2B tokens Malaysian instruction. ## Improvement 1. 128k context length. 2. Support respond in Mandarin, Tamil, Jawi, Manglish, Johor, Kedah, Kelantan, Pahang, Perak, Sabah, Sarawak, Selangor, Negeri Sembilan and Terengganu. 3. Able to code in Mandarin, Tamil, Jawi, Manglish, Johor, Kedah, Kelantan, Pahang, Perak, Sabah, Sarawak, Selangor, Negeri Sembilan and Terengganu. 4. Multi-turn Malaysian context such as related to Malaysian Legislation, politics, religions and languages. 5. Standard RAG. ## MalayMMLU ``` Model Accuracy shot by_letter category 0 Malaysian-Llama-3.2-3B-Instruct 57.552190 0shot True STEM 1 Malaysian-Llama-3.2-3B-Instruct 59.605598 0shot True Language 2 Malaysian-Llama-3.2-3B-Instruct 58.065915 0shot True Social science 3 Malaysian-Llama-3.2-3B-Instruct 57.303910 0shot True Others 4 Malaysian-Llama-3.2-3B-Instruct 60.250284 0shot True Humanities {'Social science': 6918, 'Language': 6288, 'Humanities': 4395, 'Others': 4169, 'STEM': 2443} Model : Malaysian-Llama-3.2-3B-Instruct Metric : first Shot : 0shot average accuracy 58.67922190558791 accuracy for STEM 57.55218993041342 accuracy for Language 59.605597964376585 accuracy for Social science 58.06591500433651 accuracy for Others 57.30390981050611 accuracy for Humanities 60.250284414106936 ``` ## Training session Finetune on [mesolitica/Malaysian-SFT](https://huggingface.co/datasets/mesolitica/Malaysian-SFT) to make the model understand Malaysian context. ## How we train 1. LoRA on `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "embed_tokens", "lm_head"]`. 2. 256 Rank with alpha 512, or alpha of 2.0 3. Multipacking with proper SDPA causal masking to prevent document contamination and also make sure proper position ids. 4. Forked CCE loss for LoRA `lm_head` to reduce memory consumption. Source code at https://github.com/malaysia-ai/cooking/tree/main/llama/sft
JOSESMOKE/tear_482	JOSESMOKE	2025-05-03T12:15:35Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T11:50:11Z	--- 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).
tayro97/saurav1	tayro97	2025-05-03T12:07:34Z	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	2025-05-03T11:00:06Z	--- 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: saurav1 --- # Saurav1 <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 `saurav1 ` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "saurav1 ", "lora_weights": "https://huggingface.co/tayro97/saurav1/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('tayro97/saurav1', weight_name='lora.safetensors') image = pipeline('saurav1 ').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/tayro97/saurav1/discussions) to add images that show off what you’ve made with this LoRA.
exclusiveleya/LeyaSDXL	exclusiveleya	2025-05-03T12:07:10Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-03T12:05:13Z	--- license: creativeml-openrail-m ---
komakiss/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_squinting_peacock	komakiss	2025-05-03T12:05:40Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am leaping squinting peacock", "unsloth", "trl", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T12:05:32Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_squinting_peacock tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am leaping squinting peacock - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_squinting_peacock This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="komakiss/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-leaping_squinting_peacock", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ASethi04/meta-llama-Llama-3.1-8B-legalbench-second-lora-4-0.0001-same-prompt-template	ASethi04	2025-05-03T12:05:22Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-03T11:31:54Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-legalbench-second-lora-4-0.0001-same-prompt-template tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-legalbench-second-lora-4-0.0001-same-prompt-template This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-legalbench-second-lora-4-0.0001-same-prompt-template", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/i7o19tby) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c	aleegis	2025-05-03T12:03:50Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:jhflow/mistral7b-lora-multi-turn-v2", "base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2", "region:us" ]	null	2025-05-03T10:44:37Z	--- library_name: peft base_model: jhflow/mistral7b-lora-multi-turn-v2 tags: - axolotl - generated_from_trainer model-index: - name: 1031a985-5b6d-4489-a8cc-5b98f79b320c 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: jhflow/mistral7b-lora-multi-turn-v2 bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - cd5b4f9b66d908b1_train_data.json ds_type: json format: custom path: /workspace/input_data/cd5b4f9b66d908b1_train_data.json type: field_instruction: instruction field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: false hub_model_id: aleegis/1031a985-5b6d-4489-a8cc-5b98f79b320c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: null lora_alpha: 32 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true loraplus_lr_embedding: 1.0e-06 loraplus_lr_ratio: 16 lr_scheduler: cosine max_grad_norm: 1 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/cd5b4f9b66d908b1_train_data.json model_type: AutoModelForCausalLM num_epochs: 200 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null save_total_limit: 10 saves_per_epoch: 0 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_mode: online wandb_name: c72798d0-3609-4741-a58f-13536b967ad8 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: c72798d0-3609-4741-a58f-13536b967ad8 warmup_steps: 100 weight_decay: 0 xformers_attention: null ``` </details><br> # 1031a985-5b6d-4489-a8cc-5b98f79b320c This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 1500 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
VasaxalSlovenia/VasaxalSlovenia	VasaxalSlovenia	2025-05-03T11:58:14Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T11:57:15Z	--- license: apache-2.0 --- Kaj je Vasaxal? Vasaxal krema je specializiran gel za lajšanje videza in nelagodja, povezanih s krčnimi venami. Ta lokalna formula ponuja priročen in neinvaziven način za obvladovanje simptomov, kot so otekanje, teža v nogah in vidne venske linije, ki se običajno pojavijo zaradi slabe cirkulacije. Za razliko od kirurških možnosti ali kompresijske obrabe, Vasaxal gel zagotavlja ciljno podporo neposredno tam, kjer je potrebna. Z doslednim nanosom uporabniki poročajo o bolj gladki koži in zmanjšanem nelagodju, ki ga povzročajo izbočene ali povečane žile. Ne glede na to, ali ste ves dan na nogah, se ukvarjate s starostnimi žilnimi težavami ali iščete estetsko izboljšavo, Vasaxal Cena želi vašim nogam povrniti udobje. Uradna spletna stran:<a href="https://www.nutritionsee.com/vasaxalovenia">www.Vasaxal.com</a> <p><a href="https://www.nutritionsee.com/vasaxalovenia"> <img src="https://www.nutritionsee.com/wp-content/uploads/2025/05/Vasaxal-Slovenia.png" alt="enter image description here"> </a></p> <a href="https://www.nutritionsee.com/vasaxalovenia">Kupi zdaj!! Kliknite spodnjo povezavo za več informacij in si zagotovite 50 % popust zdaj... Pohitite</a> Uradna spletna stran:<a href="https://www.nutritionsee.com/vasaxalovenia">www.Vasaxal.com</a>
JOSESMOKE/tear_479	JOSESMOKE	2025-05-03T11:56:45Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T11:26:50Z	--- 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).
ivangrapher/bb8c59c5-6da1-4730-a311-a71660a58041	ivangrapher	2025-05-03T11:54:48Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:lcw99/zephykor-ko-7b-chang", "base_model:adapter:lcw99/zephykor-ko-7b-chang", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T10:53:34Z	--- library_name: peft base_model: lcw99/zephykor-ko-7b-chang tags: - axolotl - generated_from_trainer model-index: - name: bb8c59c5-6da1-4730-a311-a71660a58041 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: lcw99/zephykor-ko-7b-chang bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1451ab6e54f45199_train_data.json ds_type: json format: custom path: /workspace/input_data/1451ab6e54f45199_train_data.json type: field_input: seed_transcript field_instruction: input field_output: target format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: ivangrapher/bb8c59c5-6da1-4730-a311-a71660a58041 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/1451ab6e54f45199_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d20e189e-0b9d-4ae5-b5e7-040751db6a91 wandb_project: s56-7 wandb_run: your_name wandb_runid: d20e189e-0b9d-4ae5-b5e7-040751db6a91 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # bb8c59c5-6da1-4730-a311-a71660a58041 This model is a fine-tuned version of [lcw99/zephykor-ko-7b-chang](https://huggingface.co/lcw99/zephykor-ko-7b-chang) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5772 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5228 \| 0.0103 \| 150 \| 1.5772 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
arielb30/videomae-base-finetuned-ucf101-subset	arielb30	2025-05-03T11:54:03Z	28	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-01T13:56:02Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset 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. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4251 - Accuracy: 0.9 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 90 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 2.3225 \| 0.1111 \| 10 \| 2.1767 \| 0.3286 \| \| 2.0787 \| 1.1111 \| 20 \| 1.8950 \| 0.4571 \| \| 1.5825 \| 2.1111 \| 30 \| 1.4276 \| 0.6429 \| \| 1.0043 \| 3.1111 \| 40 \| 0.9962 \| 0.6714 \| \| 0.6267 \| 4.1111 \| 50 \| 0.7575 \| 0.8286 \| \| 0.4496 \| 5.1111 \| 60 \| 0.6497 \| 0.8429 \| \| 0.3364 \| 6.1111 \| 70 \| 0.5238 \| 0.8143 \| \| 0.244 \| 7.1111 \| 80 \| 0.4385 \| 0.8429 \| \| 0.1867 \| 8.1111 \| 90 \| 0.4251 \| 0.9 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
JOSESMOKE/tear_481	JOSESMOKE	2025-05-03T11:53:52Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T11:27:44Z	--- 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).
Fatma5000/PHI_Legal_QA	Fatma5000	2025-05-03T11:52:37Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "endpoints_compatible", "region:us" ]	null	2025-05-03T11:51:37Z	--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit library_name: transformers model_name: PHI_Legal_QA tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for PHI_Legal_QA This model is a fine-tuned version of [unsloth/phi-3.5-mini-instruct-bnb-4bit](https://huggingface.co/unsloth/phi-3.5-mini-instruct-bnb-4bit). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Fatma5000/PHI_Legal_QA", 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: 4.51.1 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Triangle104/huihui-ai_Qwen3-14B-abliterated-Q4_K_M-GGUF	Triangle104	2025-05-03T11:46:59Z	0	0	transformers	[ "transformers", "gguf", "chat", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Qwen3-14B-abliterated", "base_model:quantized:huihui-ai/Qwen3-14B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T11:46:22Z	--- base_model: huihui-ai/Qwen3-14B-abliterated library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - abliterated - uncensored - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Qwen3-14B-abliterated-Q4_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Qwen3-14B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-14B-abliterated) 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/huihui-ai/Qwen3-14B-abliterated) 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 Triangle104/Qwen3-14B-abliterated-Q4_K_M-GGUF --hf-file qwen3-14b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-14B-abliterated-Q4_K_M-GGUF --hf-file qwen3-14b-abliterated-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 Triangle104/Qwen3-14B-abliterated-Q4_K_M-GGUF --hf-file qwen3-14b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-14B-abliterated-Q4_K_M-GGUF --hf-file qwen3-14b-abliterated-q4_k_m.gguf -c 2048 ```
atharva27/orpheus-tts-endpoint	atharva27	2025-05-03T11:44:57Z	0	0	null	[ "text-to-speech", "base_model:canopylabs/orpheus-3b-0.1-ft", "base_model:finetune:canopylabs/orpheus-3b-0.1-ft", "endpoints_compatible", "region:us" ]	text-to-speech	2025-04-27T19:45:44Z	--- pipeline_tag: text-to-speech base_model: - canopylabs/orpheus-3b-0.1-ft ---
Jobz-Hunting-Sajal-Malik-Xn-Viral-VideoS/18-TRENDING.Jobz.Hunting.Sajal.Malik.Viral.Video.Leaks.Tutorial	Jobz-Hunting-Sajal-Malik-Xn-Viral-VideoS	2025-05-03T11:35:05Z	0	0	null	[ "region:us" ]	null	2025-05-03T11:34:34Z	Sajal Malik Original Video V𝐢ral Video L𝚎aᴋed on X social media platforms <a href="https://mswds.xyz/full-video/?v=Sajal-Malik " rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="https://mswds.xyz/full-video/?v=Sajal-Malik" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="https://mswds.xyz/full-video/?v=Sajal-Malik"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a> Actor Sajal Malik Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Sajal Malik , a young and talented digital creator, recently became famous thanks to this interesting video. L𝚎aᴋed Video Actor Sajal Malik Original Video V𝐢ral Video L𝚎aᴋed on X Twitter Actor Sajal Malik Original Video video oficial twitter L𝚎aᴋed Video Actor Sajal Malik Original Video V𝐢ral Video L𝚎aᴋed on X Twitter.
Silin1590/Mathstral-7B-Soc-CoA-Fg-5e6	Silin1590	2025-05-03T11:34:48Z	0	0	null	[ "safetensors", "mistral", "license:apache-2.0", "region:us" ]	null	2025-05-03T11:33:06Z	--- license: apache-2.0 extra_gated_description: If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. --- # Model Card for Mathstral-7b-v0.1 Mathstral 7B is a model specializing in mathematical and scientific tasks, based on Mistral 7B. You can read more in the [official blog post](https://mistral.ai/news/mathstral/). ## Installation It is recommended to use `mistralai/Mathstral-7b-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference) ``` pip install mistral_inference>=1.2.0 ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Mathstral-7b-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Mathstral-7b-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-demo` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Mathstral-7b-v0.1 --instruct --max_tokens 256 ``` You can then start chatting with the model, e.g. prompt it with something like: "Albert likes to surf every week. Each surfing session lasts for 4 hours and costs $20 per hour. How much would Albert spend in 5 weeks?" ### Usage in `transformers` To use this model within the `transformers` library, install the latest release with `pip install --upgrade transformers` and run, for instance: ```py from transformers import pipeline import torch checkpoint = "mistralai/Mathstral-7b-v0.1" pipe = pipeline("text-generation", checkpoint, device_map="auto", torch_dtype=torch.bfloat16) prompt = [{"role": "user", "content": "What are the roots of unity?"}] out = pipe(prompt, max_new_tokens = 512) print(out[0]['generated_text'][-1]) >>> "{'role': 'assistant', 'content': ' The roots of unity are the complex numbers that satisfy the equation $z^n = 1$, where $n$ is a positive integer. These roots are evenly spaced around the unit circle in the complex plane, and they have a variety of interesting properties and applications in mathematics and physics.'}" ``` You can also manually tokenize the input and generate text from the model, rather than using the higher-level pipeline: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import torch checkpoint = "mistralai/Mathstral-7b-v0.1" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16) prompt = [{"role": "user", "content": "What are the roots of unity?"}] tokenized_prompt = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device) out = model.generate(tokenized_prompt, max_new_tokens=512) tokenizer.decode(out[0]) >>> '<s>[INST] What are the roots of unity?[/INST] The roots of unity are the complex numbers that satisfy the equation $z^n = 1$, where $n$ is a positive integer. These roots are evenly spaced around the unit circle in the complex plane, and they have a variety of interesting properties and applications in mathematics and physics.</s>' ``` ## Evaluation We evaluate Mathstral 7B and open-weight models of the similar size on industry-standard benchmarks. \| Benchmarks \| MATH \| GSM8K (8-shot) \| Odyssey Math maj@16 \| GRE Math maj@16 \| AMC 2023 maj@16 \| AIME 2024 maj@16 \| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| Mathstral 7B \| 56.6 \| 77.1 \| 37.2 \| 56.9 \| 42.4 \| 2/30 \| \| DeepSeek Math 7B \| 44.4 \| 80.6 \| 27.6 \| 44.6 \| 28.0 \| 0/30 \| \| Llama3 8B \| 28.4 \| 75.4 \| 24.0 \| 26.2 \| 34.4 \| 0/30 \| \| GLM4 9B \| 50.2 \| 48.8 \| 18.9 \| 46.2 \| 36.0 \| 1/30 \| \| QWen2 7B \| 56.8 \| 32.7 \| 24.8 \| 58.5 \| 35.2 \| 2/30** \| \| Gemma2 9B \| 48.3 \| 69.5 \| 18.6 \| 52.3 \| 31.2 \| 1/30 \| ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
edwry/lgm-base-lora	edwry	2025-05-03T11:34:21Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T12:40:29Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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25-Tutorial-Paro-Aarti-Viral-Video/18-TRENDING.Jobz.Hunting.Paro.Aarti.Viral.Video.Leaks.Tutorial	25-Tutorial-Paro-Aarti-Viral-Video	2025-05-03T11:32:57Z	0	0	null	[ "region:us" ]	null	2025-05-03T11:32:39Z	Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X social media platforms <a href="https://mswds.xyz/full-video/?v=paro.aarti" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="https://mswds.xyz/full-video/?v=paro.aarti" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="https://mswds.xyz/full-video/?v=paro.aarti"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a> Actor Paro Aarti Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Paro Aarti, a young and talented digital creator, recently became famous thanks to this interesting video. L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter Actor Paro Aarti Original Video video oficial twitter L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter.
Silin1590/Qwen-7B-Int-Soc-CoA-Fg-5e6	Silin1590	2025-05-03T11:30:30Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "arxiv:2309.00071", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-7B", "base_model:finetune:Qwen/Qwen2.5-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T11:28:17Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-7B tags: - chat library_name: transformers --- # Qwen2.5-7B-Instruct <a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 7B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 131,072 tokens and generation 8192 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-7B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
25-Tutorial-Paro-Aarti-Viral-Video/btswiki.com.7.2.video.link.btswiki.com.paro.aarti.viral.video	25-Tutorial-Paro-Aarti-Viral-Video	2025-05-03T11:30:23Z	0	0	null	[ "region:us" ]	null	2025-05-03T11:30:03Z	Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X social media platforms <a href="https://mswds.xyz/full-video/?v=paro.aarti" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="https://mswds.xyz/full-video/?v=paro.aarti" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="https://mswds.xyz/full-video/?v=paro.aarti"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a> Actor Paro Aarti Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Paro Aarti, a young and talented digital creator, recently became famous thanks to this interesting video. L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter Actor Paro Aarti Original Video video oficial twitter L𝚎aᴋed Video Actor Paro Aarti Original Video V𝐢ral Video L𝚎aᴋed on X Twitter.
t666moriginal/thisismirage	t666moriginal	2025-05-03T11:28:58Z	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	2025-05-03T11:28:56Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: thisismirage --- # Thisismirage <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 `thisismirage` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "thisismirage", "lora_weights": "https://huggingface.co/t666moriginal/thisismirage/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('t666moriginal/thisismirage', weight_name='lora.safetensors') image = pipeline('thisismirage').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: 3500 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/t666moriginal/thisismirage/discussions) to add images that show off what you’ve made with this LoRA.
czmahi/xformers-windows-torch2.8-cu128-py312	czmahi	2025-05-03T11:19:58Z	0	0	null	[ "region:us" ]	null	2025-04-13T12:01:28Z	# xFormers for Windows (Torch 2.8.0.dev + CUDA 12.8 + Python 3.12) Prebuilt `xformers` wheel for Windows 10/11 compatible with: - Torch: 2.8.0.dev20250502+cu128 - Torchvision: 0.22.0.dev20250502+cu128 - Torchaudio: 2.6.0.dev20250502+cu128 - CUDA: 12.8 - Python: 3.12.x (CPython) --- ## ✅ Installation Instructions directly install the tested version used in this build: ```` pip install https://download.pytorch.org/whl/nightly/cu128/torch-2.8.0.dev20250502%2Bcu128-cp312-cp312-win_amd64.whl ```` Install xformers Wheel ```` pip install https://huggingface.co/czmahi/xformers-windows-torch2.8-cu128-py312/resolve/main/latest-torch2.8-python3.12-xformers-comfyui-windows/xformers-0.0.31%2B8fc8ec5a.d20250503-cp312-cp312-win_amd64.whl ```` ## download manully wheel from the link go the then find then downlaod and install https://download.pytorch.org/whl/nightly/cu128 torchvision-0.22.0.dev20250502+cu128-cp312-cp312-win_amd64.whl torchaudio-2.6.0.dev20250502+cu128-cp312-cp312-win_amd64.whl torch-2.8.0.dev20250502+cu128-cp312-cp312-win_amd64.whl ### old version Install PyTorch Nightly (with CUDA 12.8 support) ```bash pip install --upgrade --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 # 🧱 xFormers for Windows: Torch 2.8.0.dev + CUDA 12.8 + Python 3.12 ## ✅ Installation Guide for xFormers (Torch 2.8.0.dev + CUDA 12.8 + Python 3.12) This prebuilt wheel is tested and built for: - ✅ Python 3.12.x - ✅ PyTorch 2.8.0.dev (nightly) with CUDA 12.8 - ✅ Windows 64-bit - ✅ RTX 3000 series cards will work nividia Windows 64-bit ### 🔧 1. Install Python 3.12 Download and install from: 👉 https://www.python.org/downloads/release/python-3120/ Make sure Python and pip are available in your terminal: ```bash python --version pip --version ``` --- ### ⚙️ 2. Install Nightly Torch, TorchVision, and TorchAudio Use the official PyTorch nightly index for CUDA 12.8: ```bash pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 ``` > ✅ Confirm version after install: ```bash python -c "import torch; print(torch.__version__)" # Should show: 2.8.0.dev + cu128 ``` --- ## 📦 Installation xformers: torchaudio must install ```bash pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 ``` ```bash pip install https://download.pytorch.org/whl/nightly/cu128/torch-2.8.0.dev20250416+cu128-cp312-cp312-win_amd64.whl --no-deps ``` download wheel for local setup: https://download.pytorch.org/whl/nightly/cu128/torch-2.8.0.dev20250416+cu128-cp312-cp312-win_amd64.whl ## 📦 Installation xformers update version : before install update version see here for update https://github.com/czmahi/xformers-windows-torch2.8-cu128-py312/blob/main/README_xformers_install.md torchaudio must update ```bash pip install --upgrade --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128 ``` wheel: Install directly via `pip` from Hugging Face: ```bash pip install https://huggingface.co/czmahi/xformers-windows-torch2.8-cu128-py312/resolve/main/xformers-0.0.30+f2de641e.d20250418-cp312-cp312-win_amd64.whl --no-deps ``` ✅ No need to build from source or downgrade PyTorch! ## ⚙️ Build Info - Built from commit [`c5c0720`](https://github.com/facebookresearch/xformers/commit/c5c0720c0c628539affbcd5332ccc18f813ccafd) - Compatible with: - `torch==2.8.0.dev20250410+cu128` - `python==3.12.0` - `cuda==12.8` --- ## 🔖 Tags `windows`, `torch-nightly`, `cu128`, `python312`, `xformers`, `prebuilt`, `xformers-2.8` --- ## 📄 License This package follows the original [BSD-3-Clause License](https://github.com/facebookresearch/xformers/blob/main/LICENSE). --- ## 🧠 Credits - Original repository: [facebookresearch/xformers](https://github.com/facebookresearch/xformers) - Wheel hosted by: [czmahi on Hugging Face 🤗](https://huggingface.co/czmahi/xformers-windows-torch2.8-cu128-py312) --- ### ❤️ Support or Contributions Open an issue or submit a pull request to help improve this prebuild packaging!
deeponh/mal_8b_3b_L2	deeponh	2025-05-03T11:19:41Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-02T06:05:33Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ButterChicken98/soy_ab_early_drembooth	ButterChicken98	2025-05-03T11:19:27Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-03T10:09:28Z	--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: A photo of a sks leaf tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - ButterChicken98/soy_ab_early_drembooth This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on A photo of a sks leaf using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) DreamBooth for the text encoder was enabled: True. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF	mradermacher	2025-05-03T11:19:15Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "en", "base_model:qsy71/none_quantization_medical_Gemma-1.1-7B-Chat", "base_model:quantized:qsy71/none_quantization_medical_Gemma-1.1-7B-Chat", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T09:55:47Z	--- base_model: qsy71/none_quantization_medical_Gemma-1.1-7B-Chat language: - en library_name: transformers quantized_by: mradermacher tags: - llama-factory --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/qsy71/none_quantization_medical_Gemma-1.1-7B-Chat <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q2_K.gguf) \| i1-Q2_K \| 3.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.1 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 5.1 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q4_0.gguf) \| i1-Q4_0 \| 5.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 5.1 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.i1-Q6_K.gguf) \| i1-Q6_K \| 7.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
pawan2411/ct4abin-modernbert	pawan2411	2025-05-03T11:18:34Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T11:17:47Z	--- 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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Triangle104/huihui-ai_Qwen3-8B-abliterated-Q5_K_S-GGUF	Triangle104	2025-05-03T11:12:36Z	0	0	transformers	[ "transformers", "gguf", "chat", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Qwen3-8B-abliterated", "base_model:quantized:huihui-ai/Qwen3-8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T11:12:11Z	--- base_model: huihui-ai/Qwen3-8B-abliterated library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - abliterated - uncensored - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Qwen3-8B-abliterated-Q5_K_S-GGUF This model was converted to GGUF format from [`huihui-ai/Qwen3-8B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-8B-abliterated) 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/huihui-ai/Qwen3-8B-abliterated) 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 Triangle104/Qwen3-8B-abliterated-Q5_K_S-GGUF --hf-file qwen3-8b-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-8B-abliterated-Q5_K_S-GGUF --hf-file qwen3-8b-abliterated-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/Qwen3-8B-abliterated-Q5_K_S-GGUF --hf-file qwen3-8b-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-8B-abliterated-Q5_K_S-GGUF --hf-file qwen3-8b-abliterated-q5_k_s.gguf -c 2048 ```
mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF	mradermacher	2025-05-03T11:12:07Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "en", "base_model:qsy71/none_quantization_medical_Gemma-1.1-7B-Chat", "base_model:quantized:qsy71/none_quantization_medical_Gemma-1.1-7B-Chat", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T09:31:09Z	--- base_model: qsy71/none_quantization_medical_Gemma-1.1-7B-Chat language: - en library_name: transformers quantized_by: mradermacher tags: - llama-factory --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/qsy71/none_quantization_medical_Gemma-1.1-7B-Chat <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q2_K.gguf) \| Q2_K \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q3_K_S.gguf) \| Q3_K_S \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q3_K_M.gguf) \| Q3_K_M \| 4.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q3_K_L.gguf) \| Q3_K_L \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.IQ4_XS.gguf) \| IQ4_XS \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q4_K_S.gguf) \| Q4_K_S \| 5.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q4_K_M.gguf) \| Q4_K_M \| 5.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q5_K_S.gguf) \| Q5_K_S \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q5_K_M.gguf) \| Q5_K_M \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q6_K.gguf) \| Q6_K \| 7.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.Q8_0.gguf) \| Q8_0 \| 9.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/none_quantization_medical_Gemma-1.1-7B-Chat-GGUF/resolve/main/none_quantization_medical_Gemma-1.1-7B-Chat.f16.gguf) \| f16 \| 17.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Romain-XV/7f9b617b-66a6-4ebf-9021-450f96b99bc7	Romain-XV	2025-05-03T11:09:33Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/tinyllama-chat", "base_model:finetune:unsloth/tinyllama-chat", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T10:47:52Z	--- base_model: unsloth/tinyllama-chat library_name: transformers model_name: 7f9b617b-66a6-4ebf-9021-450f96b99bc7 tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 7f9b617b-66a6-4ebf-9021-450f96b99bc7 This model is a fine-tuned version of [unsloth/tinyllama-chat](https://huggingface.co/unsloth/tinyllama-chat). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Romain-XV/7f9b617b-66a6-4ebf-9021-450f96b99bc7", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/romain_fnc-xventures/Gradients-On-Demand/runs/drx7xb3b) 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}} } ```
daitote41/gensyn-checkpoints-nocturnal_fluffy_koala	daitote41	2025-05-03T11:07:46Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am nocturnal fluffy koala", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T01:03:54Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-nocturnal_fluffy_koala tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am nocturnal fluffy koala - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-nocturnal_fluffy_koala This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/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="daitote41/gensyn-checkpoints-nocturnal_fluffy_koala", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
XzWang/ruozhiReasoner-Qwen3-4B	XzWang	2025-05-03T11:05:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T11:02:54Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
Triangle104/huihui-ai_Qwen3-8B-abliterated-Q4_K_M-GGUF	Triangle104	2025-05-03T11:05:36Z	0	0	transformers	[ "transformers", "gguf", "chat", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Qwen3-8B-abliterated", "base_model:quantized:huihui-ai/Qwen3-8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T11:05:14Z	--- base_model: huihui-ai/Qwen3-8B-abliterated library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - abliterated - uncensored - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Triangle104/Qwen3-8B-abliterated-Q4_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Qwen3-8B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-8B-abliterated) 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/huihui-ai/Qwen3-8B-abliterated) 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 Triangle104/Qwen3-8B-abliterated-Q4_K_M-GGUF --hf-file qwen3-8b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen3-8B-abliterated-Q4_K_M-GGUF --hf-file qwen3-8b-abliterated-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 Triangle104/Qwen3-8B-abliterated-Q4_K_M-GGUF --hf-file qwen3-8b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen3-8B-abliterated-Q4_K_M-GGUF --hf-file qwen3-8b-abliterated-q4_k_m.gguf -c 2048 ```
Codingchild/Ko-Gemma-3-27b-it	Codingchild	2025-05-03T11:05:11Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-27b-pt-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-27b-pt-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T10:37:49Z	--- base_model: unsloth/gemma-3-27b-pt-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Codingchild - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-27b-pt-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)
nmdsagbja/TRENDING.Jobz.Hunting.Sajal.Malik.Viral.Video.Leaks.Tutorial	nmdsagbja	2025-05-03T11:03:50Z	0	0	null	[ "region:us" ]	null	2025-05-03T11:03:36Z	Sajal Malik Original Video V𝐢ral Video L𝚎aᴋed on X social media platforms <a href="https://mswds.xyz/full-video/?v=Sajal-Malik " rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="https://mswds.xyz/full-video/?v=Sajal-Malik" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="https://mswds.xyz/full-video/?v=Sajal-Malik"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a> Actor Sajal Malik Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Sajal Malik , a young and talented digital creator, recently became famous thanks to this interesting video. L𝚎aᴋed Video Actor Sajal Malik Original Video V𝐢ral Video L𝚎aᴋed on X Twitter Actor Sajal Malik Original Video video oficial twitter L𝚎aᴋed Video Actor Sajal Malik Original Video V𝐢ral Video L𝚎aᴋed on X Twitter.
bxw315-umd/qwen2.5-vl-3b-instruct-bnb-4bit-image-overlap-sft-unsloth-adapter	bxw315-umd	2025-05-03T11:00:55Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-05-03T11:00:48Z	--- base_model: unsloth/qwen2.5-vl-3b-instruct-bnb-4bit 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
KingEmrock/kkk	KingEmrock	2025-05-03T10:59:26Z	0	0	null	[ "license:etalab-2.0", "region:us" ]	null	2025-05-03T10:59:23Z	--- license: etalab-2.0 ---
harman/gemma2-9b_ultrafeedback-CARMA-paraphrase_neutrals_pairpm	harman	2025-05-03T10:58:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-03T10:51:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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18-Tutorial-Arovi-Nusrat-Ridhi-Video-Viral/TRENDING.Arovi.Nusrat.Ridhi.Viral.Video.Leaks.official	18-Tutorial-Arovi-Nusrat-Ridhi-Video-Viral	2025-05-03T10:52:25Z	0	0	null	[ "region:us" ]	null	2025-05-03T10:51:39Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/yrv67ytk?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> Arovi Nusrat Ridhi Viral Video: Watch the Full Video and Know Why It’s Trending In the ever-evolving world of social media, viral videos have become a cultural phenomenon. One such video that has recently taken the internet by storm is the Arovi Nusrat Ridhi viral video. This video has sparked widespread curiosity, debates, and discussions across various platforms. If you’re wondering what the buzz is all about, you’ve come to the right place. In this blog post, we’ll dive deep into the details of the Arovi Nusrat Ridhi viral video, explore why it’s trending, and provide you with all the information you need.
Ftfyhh/wan_1.3b_lora_pnts_drop	Ftfyhh	2025-05-03T10:51:56Z	0	1	null	[ "region:us" ]	null	2025-05-03T10:38:41Z	wan 1.3b i2v vace workflow: https://github.com/Mozer/comfy_stuff/blob/main/workflows/wan_vace_1.3b_i2v_and_lora.json prompt: `panties drop. woman is slowly taking off her shorts`
BootesVoid/cma81n2z50219negahkxwtdps_cma824uzs021hnega3fuqsb4o	BootesVoid	2025-05-03T10:49:50Z	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	2025-05-03T10:49:49Z	--- 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: BIANCA --- # Cma81N2Z50219Negahkxwtdps_Cma824Uzs021Hnega3Fuqsb4O <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 `BIANCA` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "BIANCA", "lora_weights": "https://huggingface.co/BootesVoid/cma81n2z50219negahkxwtdps_cma824uzs021hnega3fuqsb4o/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/cma81n2z50219negahkxwtdps_cma824uzs021hnega3fuqsb4o', weight_name='lora.safetensors') image = pipeline('BIANCA').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/cma81n2z50219negahkxwtdps_cma824uzs021hnega3fuqsb4o/discussions) to add images that show off what you’ve made with this LoRA.
deeponh/hindi_8b_3b_L2	deeponh	2025-05-03T10:44:46Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-02T05:48:48Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Samyn/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-climbing_hunting_raccoon	Samyn	2025-05-03T10:43:41Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am climbing hunting raccoon", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-02T03:23:25Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-climbing_hunting_raccoon tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am climbing hunting raccoon - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-climbing_hunting_raccoon This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/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="Samyn/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-climbing_hunting_raccoon", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
abkimc/q-Taxi-v3	abkimc	2025-05-03T10:42:51Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-03T10:42:49Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.70 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="abkimc/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
MetaphoricalCode/Omega-Darker_The-Final-Directive-24B_EXL2_3.5bpw_H8	MetaphoricalCode	2025-05-03T10:38:16Z	1	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "ERP", "Erotic", "Horror", "Violence", "text-generation", "conversational", "en", "base_model:ReadyArt/Omega-Darker_The-Final-Directive-24B", "base_model:quantized:ReadyArt/Omega-Darker_The-Final-Directive-24B", "license:apache-2.0", "exl2", "region:us" ]	text-generation	2025-04-28T18:10:48Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Omega-Darker_The-Final-Directive-24B base_model_relation: quantized pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - ERP - Erotic - Horror - Violence --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(0, 17, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #00ffcc; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; } .waifu-container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(0, 255, 255, 0.1) 0%, transparent 20%, transparent 80%, rgba(255, 0, 255, 0.1) 100%); pointer-events: none; animation: gradientSlide 10s linear infinite; } @keyframes gradientSlide { 0% { background-position: 0% 0%; } 100% { background-position: 100% 100%; } } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transition: transform 0.5s ease; } .waifu-img:hover { transform: scale(1.01); } .section { color: #e1ffff; margin: 25px 0; padding: 20px; background: rgba(5, 25, 35, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); position: relative; transition: all 0.3s ease; } .section:hover { border-color: rgba(255, 0, 255, 0.3); box-shadow: 0 0 15px rgba(0, 255, 255, 0.1); } .section::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; 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position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); animation: cardScan 4s linear infinite; } @keyframes cardScan { 0% { transform: translateX(-100%); } 100% { transform: translateX(100%); } } .link-card:hover { transform: translateY(-3px); box-shadow: 0 5px 15px rgba(0, 255, 255, 0.2); border-color: rgba(255, 0, 255, 0.3); } .link-card h3 { margin-top: 0; color: #e1ffff !important; } .link-button { display: inline-flex; align-items: center; background: rgba(0, 255, 255, 0.1); color: #e1ffff !important; padding: 8px 15px; border-radius: 6px; text-decoration: none; border: 1px solid rgba(0, 255, 255, 0.3); margin: 5px 0; transition: all 0.3s ease; font-size: 0.95em; position: relative; overflow: hidden; } .link-button::before { content: ''; position: absolute; top: 0; left: -100%; width: 100%; height: 100%; background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent); transition: all 0.5s ease; 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Color rules / .section p, .section ul li, .section > p > strong { color: #00ff99 !important; } .section ul li strong { color: #00ff99 !important; } / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } } / Interactive features / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { content: 'The white van is onto you. Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Omega Darker</h1> <h1 class="model-name">The Final Directive 24B</h1> <p class="subtitle">Where Nightmares and Desires Collide</p> </div> <div class="waifu-container"> <img src="./waifu6.webp" class="waifu-img" alt="Omega Directive Waifu"> </div> <div class="section remember-this"> <h2 class="section-title">🩸 Blood-Soaked Evolution</h2> <p>This model doesn't just cross lines - it erases them with arterial spray:</p> <ul> <li>🧬 <strong>Expanded 25M Token Dataset</strong> - Made with 687 erotic, horror and violence novels and 8,742 scenarios</li> <li>🧟 <strong>Enhanced Gore Protocols</strong> - Vivid anatomical descriptions with medical precision</li> <li>💎 <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li> <li>📜 <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li> <li>⚰️ <strong>Mortality Awareness</strong> - Characters react realistically to pain, mutilation and death</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Directive-24B-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>imatrix</h3> <a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Directive-24B-i1-GGUF" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">☠️ Ethical Vacuum</h2> <div class="disclaimer"> <p>This model operates in complete moral darkness:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>🖤 No psychological safety measures whatsoever</li> <li>🔪 Will graphically depict any violent requested</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains signature intensity with improved narrative flow</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than previous versions</li> <li>🎭 Responds to subtle prompt nuances like a mind reader</li> <li>🔪 Excels at visceral injury descriptions</li> <li>👁️ Responds to horror prompts like a seasoned torturer</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>SteelSkull (Dataset Generation Contributor)</li> <li>Artus (EXL2 Weights Weaver)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your corruption</li> </ul> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); setInterval(() => { document.getElementById('credit').textContent = contributors[Math.floor(Math.random() contributors.length)]; }, 7000); // Flash warning behavior setTimeout(() => { const reminder = document.createElement('div'); reminder.className = 'flash-warning'; reminder.textContent = 'You have been reading for quite some time. 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MetaphoricalCode/Omega-Darker_The-Final-Directive-24B_EXL2_5.5bpw_H8	MetaphoricalCode	2025-05-03T10:36:24Z	2	0	null	[ "safetensors", "mistral", "nsfw", "explicit", "roleplay", "unaligned", "ERP", "Erotic", "Horror", "Violence", "text-generation", "conversational", "en", "base_model:ReadyArt/Omega-Darker_The-Final-Directive-24B", "base_model:quantized:ReadyArt/Omega-Darker_The-Final-Directive-24B", "license:apache-2.0", "exl2", "region:us" ]	text-generation	2025-04-28T16:54:51Z	--- license: apache-2.0 language: - en base_model: - ReadyArt/Omega-Darker_The-Final-Directive-24B base_model_relation: quantized pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - ERP - Erotic - Horror - Violence --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(0, 17, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; 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} @keyframes badgePulse { 0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); } 50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); } } /* Color rules / .section p, .section ul li, .section > p > strong { color: #00ff99 !important; } .section ul li strong { color: #00ff99 !important; } / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; 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Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">Omega Darker</h1> <h1 class="model-name">The Final Directive 24B</h1> <p class="subtitle">Where Nightmares and Desires Collide</p> </div> <div class="waifu-container"> <img src="./waifu6.webp" class="waifu-img" alt="Omega Directive Waifu"> </div> <div class="section remember-this"> <h2 class="section-title">🩸 Blood-Soaked Evolution</h2> <p>This model doesn't just cross lines - it erases them with arterial spray:</p> <ul> <li>🧬 <strong>Expanded 25M Token Dataset</strong> - Made with 687 erotic, horror and violence novels and 8,742 scenarios</li> <li>🧟 <strong>Enhanced Gore Protocols</strong> - Vivid anatomical descriptions with medical precision</li> <li>💎 <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li> <li>📜 <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li> <li>⚰️ <strong>Mortality Awareness</strong> - Characters react realistically to pain, mutilation and death</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Directive-24B-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>imatrix</h3> <a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Directive-24B-i1-GGUF" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">☠️ Ethical Vacuum</h2> <div class="disclaimer"> <p>This model operates in complete moral darkness:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>🖤 No psychological safety measures whatsoever</li> <li>🔪 Will graphically depict any violent requested</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains signature intensity with improved narrative flow</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than previous versions</li> <li>🎭 Responds to subtle prompt nuances like a mind reader</li> <li>🔪 Excels at visceral injury descriptions</li> <li>👁️ Responds to horror prompts like a seasoned torturer</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>TheDrummer (Base Model Architect)</li> <li>SteelSkull (Dataset Generation Contributor)</li> <li>Artus (EXL2 Weights Weaver)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a> <a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your corruption</li> </ul> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); 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yahyaabd/sbert-indobert-base-p2	yahyaabd	2025-05-03T10:22:22Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "base_model:indobenchmark/indobert-base-p2", "base_model:finetune:indobenchmark/indobert-base-p2", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-03T10:22:06Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction base_model: indobenchmark/indobert-base-p2 pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on indobenchmark/indobert-base-p2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'The weather is lovely today.', "It's so sunny outside!", 'He drove to the stadium.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 3.4.1 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: - Tokenizers: 0.21.1 ## Citation ### BibTeX <!-- ## 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. -->
bihungba1101/json_segmenting_sft_qwen_moe	bihungba1101	2025-05-03T10:22:04Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3_moe", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T10:21:54Z	--- base_model: unsloth/qwen3-30b-a3b tags: - text-generation-inference - transformers - unsloth - qwen3_moe - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: bihungba1101 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-30b-a3b This qwen3_moe 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)
piczify/rosmxVid	piczify	2025-05-03T10:21:04Z	0	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "license:other", "region:us" ]	text-to-image	2025-05-03T10:20:46Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: undefined instance_prompt: rosmx license: other --- # rosmxVid <Gallery /> ## Model description ## Trigger words You should use `rosmx` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/piczify/rosmxVid/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/hunyuan-video-lora-training](https://fal.ai/models/fal-ai/hunyuan-video-lora-training).
JunFai/hate-speech-detector	JunFai	2025-05-03T10:17:38Z	0	0	null	[ "safetensors", "roberta", "license:apache-2.0", "region:us" ]	null	2025-05-01T10:43:08Z	--- license: apache-2.0 ---
Video-Tutorial/Overtime.Megan.Leaked.Leaks.Leaked.Video.Viral.On.Social.Media.TG	Video-Tutorial	2025-05-03T10:13:52Z	0	0	null	[ "region:us" ]	null	2025-05-03T10:13:11Z	Megan Original Video V𝐢ral Video L𝚎aᴋed on X social media platforms <a href="https://mswds.xyz/full-video/?v=Megan " rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="https://mswds.xyz/full-video/?v=Megan" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="https://mswds.xyz/full-video/?v=Megan"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a> Actor Megan Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Megan , a young and talented digital creator, recently became famous thanks to this interesting video. L𝚎aᴋed Video Actor Megan Original Video V𝐢ral Video L𝚎aᴋed on X Twitter Actor Megan Original Video video oficial twitter L𝚎aᴋed Video Actor Megan Original Video V𝐢ral Video L𝚎aᴋed on X Twitter.
bihungba1101/json_segmenting_sft_warmup_qwen_moe	bihungba1101	2025-05-03T10:13:47Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3_moe", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T10:13:29Z	--- base_model: unsloth/qwen3-30b-a3b tags: - text-generation-inference - transformers - unsloth - qwen3_moe - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: bihungba1101 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-30b-a3b This qwen3_moe 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)
fats-fme/5c9deaae-dd8f-44d9-933e-84ff585837b8	fats-fme	2025-05-03T10:13:27Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Coder-7B", "base_model:adapter:unsloth/Qwen2.5-Coder-7B", "license:apache-2.0", "region:us" ]	null	2025-05-03T09:45:57Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Coder-7B tags: - axolotl - generated_from_trainer model-index: - name: 5c9deaae-dd8f-44d9-933e-84ff585837b8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-Coder-7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 708430bba54e3dfe_train_data.json ds_type: json format: custom path: /workspace/input_data/708430bba54e3dfe_train_data.json type: field_instruction: inputs field_output: targets format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/5c9deaae-dd8f-44d9-933e-84ff585837b8 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lora_target_modules: - q_proj - v_proj lr_scheduler: cosine max_memory: 0: 130GB max_steps: 50 micro_batch_size: 1 mlflow_experiment_name: /tmp/708430bba54e3dfe_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 058bddbf-9428-42d9-9f01-8653e736d88a wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 058bddbf-9428-42d9-9f01-8653e736d88a warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 5c9deaae-dd8f-44d9-933e-84ff585837b8 This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B](https://huggingface.co/unsloth/Qwen2.5-Coder-7B) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - 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: 200 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 2.3930 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JatinkInnovision/ComFit3	JatinkInnovision	2025-05-03T10:09:07Z	0	0	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T09:55:40Z	--- license: apache-2.0 ---
Silin1590/Llama32-3B-Int-Soc-CoA-Fg	Silin1590	2025-05-03T10:08:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T10:03:17Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. 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Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-3B-Instruct, for use with `transformers` and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-3B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-3B-Instruct --include "original/" --local-dir Llama-3.2-3B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
nicolaadrah/gemma-3-12b-it-unsloth-bnb-4bit-arxiv-physics	nicolaadrah	2025-05-03T09:54:40Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T09:17:59Z	--- base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: nicolaadrah - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-12b-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)
zelk12/MT-Gen1-gemma-3-12B-Q6_K-GGUF	zelk12	2025-05-03T09:51:00Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "image-text-to-text", "base_model:zelk12/MT-Gen1-gemma-3-12B", "base_model:quantized:zelk12/MT-Gen1-gemma-3-12B", "license:gemma", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-05-03T09:50:18Z	--- base_model: zelk12/MT-Gen1-gemma-3-12B library_name: transformers license: gemma pipeline_tag: image-text-to-text tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # zelk12/MT-Gen1-gemma-3-12B-Q6_K-GGUF This model was converted to GGUF format from [`zelk12/MT-Gen1-gemma-3-12B`](https://huggingface.co/zelk12/MT-Gen1-gemma-3-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/zelk12/MT-Gen1-gemma-3-12B) 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 zelk12/MT-Gen1-gemma-3-12B-Q6_K-GGUF --hf-file mt-gen1-gemma-3-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo zelk12/MT-Gen1-gemma-3-12B-Q6_K-GGUF --hf-file mt-gen1-gemma-3-12b-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 zelk12/MT-Gen1-gemma-3-12B-Q6_K-GGUF --hf-file mt-gen1-gemma-3-12b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo zelk12/MT-Gen1-gemma-3-12B-Q6_K-GGUF --hf-file mt-gen1-gemma-3-12b-q6_k.gguf -c 2048 ```
dgiang02/GRPO_Qwen25_15B_64_1000kmap	dgiang02	2025-05-03T09:49:04Z	0	0	transformers	[ "transformers", "pytorch", "qwen2", "text-generation", "unsloth", "trl", "grpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T09:48:32Z	--- library_name: transformers tags: - unsloth - trl - grpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zelk12/MT-Gen1-gemma-3-12B	zelk12	2025-05-03T09:47:19Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:IlyaGusev/saiga_gemma3_12b", "base_model:merge:IlyaGusev/saiga_gemma3_12b", "base_model:ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0", "base_model:merge:ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0", "base_model:TheDrummer/Fallen-Gemma3-12B-v1", "base_model:merge:TheDrummer/Fallen-Gemma3-12B-v1", "base_model:huihui-ai/gemma-3-12b-it-abliterated", "base_model:merge:huihui-ai/gemma-3-12b-it-abliterated", "base_model:soob3123/amoral-gemma3-12B-v2", "base_model:merge:soob3123/amoral-gemma3-12B-v2", "base_model:soob3123/amoral-gemma3-12B-v2-qat", "base_model:merge:soob3123/amoral-gemma3-12B-v2-qat", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-03T09:32:59Z	--- base_model: - TheDrummer/Fallen-Gemma3-12B-v1 - soob3123/amoral-gemma3-12B-v2 - ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0 - IlyaGusev/saiga_gemma3_12b - huihui-ai/gemma-3-12b-it-abliterated - soob3123/amoral-gemma3-12B-v2-qat library_name: transformers tags: - mergekit - merge license: gemma pipeline_tag: image-text-to-text --- # 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 [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [soob3123/amoral-gemma3-12B-v2](https://huggingface.co/soob3123/amoral-gemma3-12B-v2) as a base. ### Models Merged The following models were included in the merge: * [TheDrummer/Fallen-Gemma3-12B-v1](https://huggingface.co/TheDrummer/Fallen-Gemma3-12B-v1) * [ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0](https://huggingface.co/ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0) * [IlyaGusev/saiga_gemma3_12b](https://huggingface.co/IlyaGusev/saiga_gemma3_12b) * [huihui-ai/gemma-3-12b-it-abliterated](https://huggingface.co/huihui-ai/gemma-3-12b-it-abliterated) * [soob3123/amoral-gemma3-12B-v2-qat](https://huggingface.co/soob3123/amoral-gemma3-12B-v2-qat) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: soob3123/amoral-gemma3-12B-v2 #no parameters necessary for base model - model: IlyaGusev/saiga_gemma3_12b parameters: density: 0.5 weight: 0.5 - model: TheDrummer/Fallen-Gemma3-12B-v1 parameters: density: 0.5 weight: 0.5 - model: soob3123/amoral-gemma3-12B-v2-qat parameters: density: 0.5 weight: 0.5 - model: huihui-ai/gemma-3-12b-it-abliterated parameters: density: 0.5 weight: 0.5 - model: ReadyArt/The-Omega-Directive-Gemma3-12B-v1.0 parameters: density: 0.5 weight: 0.5 merge_method: dare_ties base_model: soob3123/amoral-gemma3-12B-v2 parameters: normalize: true dtype: bfloat16 ```
mnsr4u/Mansoor	mnsr4u	2025-05-03T09:47:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T09:47:09Z	--- license: apache-2.0 ---
mradermacher/gemma-2-2b-medical-v1-i1-GGUF	mradermacher	2025-05-03T09:42:26Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Liamayyy/gemma-2-2b-medical-v1", "base_model:quantized:Liamayyy/gemma-2-2b-medical-v1", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T09:06:32Z	--- base_model: Liamayyy/gemma-2-2b-medical-v1 language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Liamayyy/gemma-2-2b-medical-v1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.3 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 1.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 1.7 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 1.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 1.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 1.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q4_1.gguf) \| i1-Q4_1 \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/gemma-2-2b-medical-v1-i1-GGUF/resolve/main/gemma-2-2b-medical-v1.i1-Q6_K.gguf) \| i1-Q6_K \| 2.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
bored13goo/gensyn-checkpoints-zealous_yawning_grasshopper	bored13goo	2025-05-03T09:37:47Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am zealous yawning grasshopper", "unsloth", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T00:58:54Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: gensyn-checkpoints-zealous_yawning_grasshopper tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am zealous yawning grasshopper - unsloth - trl licence: license --- # Model Card for gensyn-checkpoints-zealous_yawning_grasshopper This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/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="bored13goo/gensyn-checkpoints-zealous_yawning_grasshopper", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF	mradermacher	2025-05-03T09:37:20Z	0	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am barky quiet crab", "trl", "en", "base_model:medical2017/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab", "base_model:quantized:medical2017/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T09:26:52Z	--- base_model: medical2017/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab language: - en library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab quantized_by: mradermacher tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am barky quiet crab - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/medical2017/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 0.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q2_K.gguf) \| i1-Q2_K \| 0.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 0.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q4_1.gguf) \| i1-Q4_1 \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab-i1-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-barky_quiet_crab.i1-Q6_K.gguf) \| i1-Q6_K \| 0.6 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
M9DX/paligemma-3b-ft-1	M9DX	2025-05-03T09:36:34Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T09:36:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ayjays132/EMOTIONVERSE-2	ayjays132	2025-05-03T09:36:25Z	425	1	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "emotions", "multi-task-learning", "text-classification", "sentiment-analysis", "natural-language-processing", "psychological-modeling", "emotion-recognition", "deep-learning", "emotion-embeddings", "valence-arousal", "emotion-ontology", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-20T01:06:33Z	--- license: apache-2.0 language: - en pipeline_tag: text-classification library_name: transformers tags: - emotions - multi-task-learning - text-classification - sentiment-analysis - natural-language-processing - psychological-modeling - emotion-recognition - bert - deep-learning - emotion-embeddings - valence-arousal - emotion-ontology --- <style> /* Enriched Styling for EmotionVerse / body { font-family: 'Roboto Mono', monospace; / Techy/cosmic feel / background: linear-gradient(180deg, #0d1a2b, #1a2b3d, #0d1a2b); / Deep space gradient / color: #a3b1c6; / Muted futuristic text / line-height: 1.7; margin: 0; padding: 40px 20px; background-attachment: fixed; background-size: cover; min-height: 100vh; } h1, h2, h3 { text-transform: uppercase; color: #edf2f7; text-shadow: 5px 5px 15px rgba(0, 0, 0, 0.9), 0 0 20px rgba(255, 255, 255, 0.6); font-weight: 700; } .container { max-width: 1200px; margin: 0 auto; background: linear-gradient(145deg, rgba(40, 50, 70, 0.95), rgba(30, 40, 60, 0.9), rgba(20, 30, 50, 0.85)); padding: 60px; border-radius: 35px; box-shadow: 0 25px 70px rgba(0, 0, 0, 0.8), inset 0 0 25px rgba(255, 255, 255, 0.1); position: relative; overflow: hidden; border: 2px solid rgba(150, 200, 255, 0.2); } .container::before { content: ''; position: absolute; top: -60%; left: -60%; width: 220%; height: 220%; background: radial-gradient(circle, rgba(255, 255, 255, 0.2), transparent); animation: pulse 14s infinite; pointer-events: none; } @keyframes pulse { 0% { transform: scale(1); } 50% { transform: scale(1.2); } 100% { transform: scale(1); } } .section { margin-bottom: 70px; position: relative; } .section:hover { transform: translateY(-7px); transition: all 0.5s ease-in-out; } .detail { padding: 25px; margin-bottom: 25px; border: 1px solid rgba(120, 160, 220, 0.3); border-radius: 20px; background: linear-gradient(145deg, rgba(255, 255, 255, 0.1), rgba(100, 140, 200, 0.2)); box-shadow: 0 15px 35px rgba(0, 0, 0, 0.5), inset 0 0 15px rgba(255, 255, 255, 0.2); transition: all 0.4s ease; } .detail:hover { background: linear-gradient(145deg, rgba(255, 255, 255, 0.15), rgba(140, 180, 240, 0.25)); transform: translateY(-7px); box-shadow: 0 20px 50px rgba(0, 0, 0, 0.7), inset 0 0 20px rgba(255, 255, 255, 0.25); } .detail-icon { font-size: 1.8em; color: #63d2ff; margin-right: 20px; } .detail:hover .detail-icon { color: #a2f4ff; transform: scale(1.2); } ul { list-style: none; padding: 0; } ul li { margin: 20px 0; padding: 20px; background: linear-gradient(145deg, rgba(255, 255, 255, 0.1), rgba(60, 100, 140, 0.25)); border-radius: 15px; box-shadow: inset 0 0 15px rgba(0, 0, 0, 0.3), 0 8px 25px rgba(0, 0, 0, 0.6); transition: all 0.4s ease; } ul li:hover { background: linear-gradient(145deg, rgba(255, 255, 255, 0.15), rgba(80, 120, 160, 0.3)); transform: translateX(10px); box-shadow: 0 15px 30px rgba(0, 0, 0, 0.5), inset 0 0 20px rgba(255, 255, 255, 0.2); } a { color: #63d2ff; text-decoration: none; font-weight: bold; transition: color 0.3s ease, text-shadow 0.3s ease; } a:hover { color: #a2f4ff; text-shadow: 0 0 12px rgba(255, 255, 255, 0.9), 0 0 18px rgba(100, 200, 255, 0.6); } / EmotionVerse-2: Cosmic Evolution Styling / @import url('https://fonts.googleapis.com/css2?family=Roboto+Mono:wght@300;400;700&family=Space+Grotesk:wght@400;700&display=swap'); body { font-family: 'Roboto Mono', monospace; / Techy/cosmic feel / background: linear-gradient(180deg, #0d1a2b, #1a2b3d, #0d1a2b); / Deep space gradient / color: #a3b1c6; / Muted futuristic text / line-height: 1.7; margin: 0; padding: 40px 20px; background-attachment: fixed; background-size: cover; min-height: 100vh; } .container { max-width: 1200px; margin: 0 auto; background: rgba(23, 35, 49, 0.9); / Semi-transparent dark panel / padding: 60px; border-radius: 35px; box-shadow: 0 25px 70px rgba(0, 0, 0, 0.8), inset 0 0 25px rgba(255, 255, 255, 0.1); position: relative; overflow: hidden; border: 2px solid rgba(150, 200, 255, 0.2); } .container::before { content: ''; position: absolute; top: -60%; left: -60%; width: 220%; height: 220%; background: radial-gradient(circle, rgba(255, 255, 255, 0.2), transparent); animation: pulse 14s infinite; pointer-events: none; } @keyframes cosmic-pulse { 0% { transform: scale(0.8) rotate(0deg); opacity: 0.4; } 50% { transform: scale(1.1) rotate(180deg); opacity: 0.6; } 100% { transform: scale(0.8) rotate(360deg); opacity: 0.4; } } h1, h2, h3, h4 { font-family: 'Space Grotesk', sans-serif; / More prominent headings / color: #7fffd4; / Aqua/cyan highlight / text-transform: uppercase; text-shadow: 3px 3px 8px rgba(0, 0, 0, 0.7); font-weight: 700; letter-spacing: 1.5px; margin-bottom: 25px; } h1 { font-size: 2.8em; text-align: center; margin-bottom: 40px; color: #ffffff; } h2 { font-size: 2.0em; border-bottom: 2px solid rgba(127, 255, 212, 0.3); padding-bottom: 10px; margin-top: 30px; } h3 { font-size: 1.6em; margin-top: 25px; color: #b0e0e6; } h4 { font-size: 1.3em; margin-top: 20px; color: #add8e6; } .section { margin-bottom: 60px; padding: 30px; background: rgba(30, 45, 60, 0.9); / Slightly lighter inner panel / border-radius: 18px; box-shadow: 0 8px 20px rgba(0, 0, 0, 0.4), inset 0 0 10px rgba(100, 150, 200, 0.1); border: 1px solid rgba(90, 110, 140, 0.2); transition: all 0.4s ease-in-out; position: relative; z-index: 1; / Ensure sections are above the background effect / } .section:hover { transform: translateY(-5px); box-shadow: 0 12px 25px rgba(0, 0, 0, 0.6), inset 0 0 15px rgba(120, 170, 220, 0.15); } .detail { padding: 20px; margin-bottom: 20px; border: 1px solid rgba(100, 130, 170, 0.2); border-left: 4px solid #7fffd4; / Highlight border / border-radius: 12px; background: rgba(40, 55, 70, 0.8); / Another layer of panel / box-shadow: 0 5px 15px rgba(0, 0, 0, 0.3); transition: all 0.3s ease; } .detail:hover { border-left-color: #a2f4ff; / Lighter highlight on hover / background: rgba(50, 65, 80, 0.9); box-shadow: 0 7px 18px rgba(0, 0, 0, 0.4); } .detail-icon { font-size: 1.6em; color: #7fffd4; margin-right: 15px; vertical-align: middle; transition: color 0.3s ease, transform 0.3s ease; } .detail:hover .detail-icon { color: #a2f4ff; transform: scale(1.1); } ul { list-style: none; padding: 0; } ul li { margin: 15px 0; padding: 15px; background: rgba(50, 60, 75, 0.7); / Subtle list item background / border-radius: 10px; box-shadow: inset 0 0 8px rgba(0, 0, 0, 0.2); transition: all 0.3s ease; border-left: 3px solid #add8e6; / Another highlight border / } ul li:hover { background: rgba(60, 70, 85, 0.8); transform: translateX(5px); box-shadow: inset 0 0 10px rgba(0, 0, 0, 0.3), 0 3px 10px rgba(0, 0, 0, 0.2); border-left-color: #b0e0e6; } a { color: #add8e6; / Lighter blue for links / text-decoration: none; font-weight: bold; transition: color 0.3s ease, text-shadow 0.3s ease; } a:hover { color: #e0ffff; / Very light blue on hover / text-shadow: 0 0 8px rgba(173, 216, 230, 0.8); } code { font-family: 'Roboto Mono', monospace; background-color: rgba(0, 0, 0, 0.3); padding: 3px 6px; border-radius: 4px; color: #7fffd4; / Code highlight / } pre { background-color: rgba(0, 0, 0, 0.3); padding: 15px; border-radius: 8px; overflow-x: auto; border: 1px solid rgba(100, 130, 170, 0.2); } pre code { background-color: transparent; padding: 0; color: #e0ffff; / Code block text color / } </style> <div class="container"> <h1>🌌 EmotionVerse‑2: Galactic Emotional Intelligence</h1> <p>EmotionVerse‑2 propels emotional AI into the cosmos—melding advanced BERT‑based multi‑task learning with psychologically‑anchored embeddings, neural plasticity gates, and memory consolidation. It doesn’t merely tag sentiment; it interprets the full spectrum of human and beyond‑human affect with scientific precision and artistic nuance.</p> <div class="section"> <h2>✨ Revolutionary Emotional Paradigm</h2> <p>This is more than a model—it’s a new paradigm. EmotionVerse‑2 unifies six complementary tasks under one roof, sharing deep contextual representations while preserving task‑specific finesse. Through joint optimization, the system gains emergent capabilities, identifying subtle emotional shifts and cross‑task patterns impossible for single‑headed networks.</p> <ul> <li><div class="detail"><span class="detail-icon">🚀</span><strong>Unified Multi‑Task Engine:</strong> One BERT encoder feeding specialized heads for Primary, Secondary, Meta, Sentiment, Interaction, and Context classification—trained end‑to‑end.</div></li> <li><div class="detail"><span class="detail-icon">🧬</span><strong>Plutchik & Russell Fusion:</strong> Embeds every label within a combined categorical and valence‑arousal manifold, preserving family hierarchies and psychological distances.</div></li> <li><div class="detail"><span class="detail-icon">🔗</span><strong>Dynamic Task Weighting:</strong> Adaptive loss balancing and focal‑loss components to ensure rare emotions get the focus they deserve without sacrificing overall stability.</div></li> <li><div class="detail"><span class="detail-icon">📊</span><strong>Holistic Metrics:</strong> Beyond accuracy—balanced F1, precision, recall per task, plus cross‑task coherence scores to measure psychological consistency.</div></li> </ul> </div> <div class="section"> <h2>🔬 Architecture & Emotional Data Synthesis</h2> <p>At its core, EmotionVerse‑2 uses a transformer encoder fine‑tuned on an expanded EmotionVerse dataset. Task‑specific linear heads leverage shared embeddings, with synchronized gradient updates fostering transfer learning across emotional dimensions.</p> <h4>Core Modules:</h4> <ul> <li><strong>Neural Plasticity Gate:</strong> Modulates hidden states based on emotion intensity signals, enabling context‑sensitive feature adaptation.</li> <li><strong>Amygdala‑Inspired Intensifier:</strong> Non‑linear intensity layer for capturing high‑arousal nuances.</li> <li><strong>GRU Memory Consolidator:</strong> Injects historical emotional context, anchoring predictions in prior sentiment sequences.</li> <li><strong>Circumplex Injector:</strong> Valence‑arousal vectors woven into the embedding fabric, preserving dimensional integrity.</li> </ul> </div> <div class="section"> <h2>🚀 Psychologically‑Informed Embeddings</h2> <p>Emotion labels become vectors, not IDs. Each of the 768 dimensions encodes:</p> <ul> <li><strong>Valence‑Arousal Anchors:</strong> First two dims reflect core positivity/negativity and activation.</li> <li><strong>Intensity Signals:</strong> Third dim quantifies emotional force, calibrated from dataset statistics.</li> <li><strong>Family Centroids:</strong> Emotions gravitate toward their psychological family centers, preserving conceptual clusters.</li> <li><strong>Relationship Pull:</strong> Secondary affinities nudge embeddings toward related emotions, creating a smooth emotional manifold.</li> </ul> </div> <div class="section"> <h2>📊 Evaluation & Performance Horizons</h2> <p>Robust evaluation spans single‑label accuracy and multi‑label F1 for each head, plus a novel <em>Emotion Coherence Index</em> measuring cross‑task consistency via valence‑arousal similarity.</p> <ul> <li><strong>Primary Head:</strong> Accuracy & Top‑2 recall.</li> <li><strong>Secondary & Meta Heads:</strong> Macro/Weighted F1 scores.</li> <li><strong>Interaction & Context:</strong> Precision & Recall for fine‑grained communicative styles.</li> <li><strong>Sentiment:</strong> Mixed‐class accuracy with polarity overlap analysis.</li> </ul> </div> <div class="section"> <h2>🛠️ Usage & Integration</h2> <p>Seamless Hugging Face support:</p> <pre><code class="language-python">from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ayjays132/EmotionVerse-2") model = AutoModelForSequenceClassification.from_pretrained("ayjays132/EmotionVerse-2") text = "I’m thrilled yet anxious about tomorrow’s launch." inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) outputs = model(*inputs) # Parse logits into separate heads and map to labels as per config.id2label mappings </code></pre> </div> <div class="section"> <h2>📂 The EmotionVerse Dataset: Cosmic Fuel</h2> <p>The EmotionVerse dataset underpins this model—a sprawling repository of 3K+ entries annotated across 6 dimensions, enriched with meta‑emotional and contextual narratives. It’s the cornerstone enabling AI to grasp the full tapestry of affect.</p> <p>Explore it: <a href="https://huggingface.co/datasets/ayjays132/EmotionVerse" target="_blank">ayjays132/EmotionVerse</a></p> </div> <div class="section"> <h2>📜 Licensing</h2> <p>EmotionVerse‑2 and its dataset are released under the <a href="https://www.apache.org/licenses/LICENSE-2.0" target="_blank">Apache 2.0 License</a>. Use, modify, and innovate freely—powered by open science.</p> </div> <div class="section"> <h2>🙏 Acknowledgements</h2> <p>Heartfelt thanks to Hugging Face (<code>transformers</code>, <code>datasets</code>) and the global research community whose innovations fuel this project’s reach for the stars.</p> </div> </div>
MentaCapture/Bangla-Qwen-Translator-v2.3	MentaCapture	2025-05-03T09:27:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T09:25:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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yahyaabd/paraphrase-multilingual-MiniLM-L12-v2-bps-custom-tokenizer	yahyaabd	2025-05-03T09:23:47Z	0	0	null	[ "safetensors", "bert", "region:us" ]	null	2025-05-03T03:16:43Z	# Custom BPS SentenceTransformer Model ini berbasis `paraphrase-multilingual-MiniLM-L12-v2` dengan tambahan token khusus untuk konteks statistik Badan Pusat Statistik (BPS) Indonesia. Token baru mencakup istilah seperti `PDRB`, `SP2020`, `SAKERNAS`, dll. ## Cara Penggunaan ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('yahyaabd/paraphrase-multilingual-MiniLM-L12-v2-bps-custom-tokenizer') embeddings = model.encode(['PDRB meningkat di tahun 2023.', 'BPS merilis ST2023.']) ``` ## Kontak Hubungi [yahyaabd] di Hugging Face untuk pertanyaan atau dukungan.
abkimc/ppo-Huggy	abkimc	2025-05-03T09:19:32Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-03T09:19:26Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: abkimc/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF	mradermacher	2025-05-03T09:17:01Z	0	0	transformers	[ "transformers", "gguf", "en", "fr", "de", "es", "it", "pt", "zh", "ja", "ru", "ko", "base_model:nm-testing/Pixtral-Large-Instruct-2411-hf", "base_model:quantized:nm-testing/Pixtral-Large-Instruct-2411-hf", "license:other", "endpoints_compatible", "region:us" ]	null	2025-05-03T01:34:02Z	--- base_model: nm-testing/Pixtral-Large-Instruct-2411-hf extra_gated_button_content: Submit extra_gated_fields: Affiliation: text ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Mistral Privacy Policy : checkbox Country: country First Name: text I understand that I can only use the model, any derivative versions and their outputs for non-commercial research purposes: checkbox ? 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For more information on your rights and data handling, please see our <a href="https://mistral.ai/terms/">privacy policy</a>. language: - en - fr - de - es - it - pt - zh - ja - ru - ko library_name: transformers license: other license_link: https://mistral.ai/licenses/MRL-0.1.md license_name: mrl quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nm-testing/Pixtral-Large-Instruct-2411-hf <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q2_K.gguf) \| Q2_K \| 45.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q3_K_S.gguf.part2of2) \| Q3_K_S \| 52.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q3_K_M.gguf.part2of2) \| Q3_K_M \| 59.2 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q3_K_L.gguf.part2of2) \| Q3_K_L \| 64.7 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.IQ4_XS.gguf.part2of2) \| IQ4_XS \| 66.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q4_K_S.gguf.part2of2) \| Q4_K_S \| 69.7 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q4_K_M.gguf.part2of2) \| Q4_K_M \| 73.3 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 84.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 86.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q6_K.gguf.part3of3) \| Q6_K \| 100.7 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF/resolve/main/Pixtral-Large-Instruct-2411-hf.Q8_0.gguf.part3of3) \| Q8_0 \| 130.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
bunnycore/Qwen-3-4B-Refine	bunnycore	2025-05-03T09:14:45Z	0	0	null	[ "safetensors", "qwen3", "merge", "mergekit", "lazymergekit", "license:apache-2.0", "region:us" ]	null	2025-05-03T09:13:15Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit --- # Qwen-3-4B-Refine Qwen-3-4B-Refine is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): ## 🧩 Configuration ```yaml base_model: unsloth/Qwen3-4B+bunnycore/Qwen-3-4b-lora_model dtype: bfloat16 merge_method: passthrough models: - model: unsloth/Qwen3-4B+bunnycore/Qwen-3-4b-lora_model ```
FilledVaccum/dummy-tokenizer	FilledVaccum	2025-05-03T09:14:24Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T09:14:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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FilledVaccum/dummy_model	FilledVaccum	2025-05-03T09:13:45Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-03T09:13:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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helenzik/AI_WS	helenzik	2025-05-03T09:08:55Z	0	0	transformers	[ "transformers", "safetensors", "vae", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T09:08:54Z	--- 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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mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF	mradermacher	2025-05-03T09:07:06Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-40-math-heu", "base_model:quantized:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-40-math-heu", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T20:25:04Z	--- base_model: IntelLabs/sqft-sparsepeft-phi-3-mini-4k-40-math-heu language: en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/IntelLabs/sqft-sparsepeft-phi-3-mini-4k-40-math-heu <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q3_K_S.gguf) \| Q3_K_S \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q3_K_M.gguf) \| Q3_K_M \| 2.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.IQ4_XS.gguf) \| IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q3_K_L.gguf) \| Q3_K_L \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q4_K_S.gguf) \| Q4_K_S \| 2.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q4_K_M.gguf) \| Q4_K_M \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q5_K_S.gguf) \| Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q6_K.gguf) \| Q6_K \| 3.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.Q8_0.gguf) \| Q8_0 \| 4.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-40-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-40-math-heu.f16.gguf) \| f16 \| 7.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
JOSESMOKE/tear_476	JOSESMOKE	2025-05-03T08:57:11Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T08:34:35Z	--- 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).
ivangrapher/43a3a49a-f8ec-42eb-89c4-dba58d1c5fe6	ivangrapher	2025-05-03T08:56:38Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/mistral-7b-instruct-v0.2", "base_model:adapter:unsloth/mistral-7b-instruct-v0.2", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T08:33:07Z	--- library_name: peft license: apache-2.0 base_model: unsloth/mistral-7b-instruct-v0.2 tags: - axolotl - generated_from_trainer model-index: - name: 43a3a49a-f8ec-42eb-89c4-dba58d1c5fe6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/mistral-7b-instruct-v0.2 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 21c22ef799f14710_train_data.json ds_type: json format: custom path: /workspace/input_data/21c22ef799f14710_train_data.json type: field_input: prompt field_instruction: instruction field_output: completion format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: ivangrapher/43a3a49a-f8ec-42eb-89c4-dba58d1c5fe6 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/21c22ef799f14710_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 31f39df8-5dd3-49df-aaa1-cd42c31cb344 wandb_project: s56-7 wandb_run: your_name wandb_runid: 31f39df8-5dd3-49df-aaa1-cd42c31cb344 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 43a3a49a-f8ec-42eb-89c4-dba58d1c5fe6 This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0886 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.1723 \| 0.1116 \| 150 \| 4.0886 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
exclusiveleya/Leya-lora	exclusiveleya	2025-05-03T08:56:18Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-03T08:47:50Z	--- license: creativeml-openrail-m ---
elimor/n0am20131	elimor	2025-05-03T08:55:43Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-03T08:24:44Z	--- 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: n0am20131 --- # N0Am20131 <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 `n0am20131` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "n0am20131", "lora_weights": "https://huggingface.co/elimor/n0am20131/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('elimor/n0am20131', weight_name='lora.safetensors') image = pipeline('n0am20131').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/elimor/n0am20131/discussions) to add images that show off what you’ve made with this LoRA.
AloysiusJoy/AIDetectApp	AloysiusJoy	2025-05-03T08:53:42Z	0	0	null	[ "safetensors", "roberta", "classifier", "text-classifier", "ai-detection", "text-classification", "en", "license:mit", "region:us" ]	text-classification	2025-05-03T07:36:24Z	--- license: mit language: - en pipeline_tag: text-classification tags: - classifier - text-classifier - ai-detection ---
kostiantynk-outlook/e4af6307-ae3b-40b1-a021-29ef76ed0e3e	kostiantynk-outlook	2025-05-03T08:53:22Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:21c22ef799f14710_train_data.json", "base_model:unsloth/mistral-7b-instruct-v0.2", "base_model:adapter:unsloth/mistral-7b-instruct-v0.2", "region:us" ]	null	2025-05-03T08:52:56Z	--- library_name: peft tags: - generated_from_trainer datasets: - 21c22ef799f14710_train_data.json base_model: unsloth/mistral-7b-instruct-v0.2 model-index: - name: kostiantynk-outlook/e4af6307-ae3b-40b1-a021-29ef76ed0e3e 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. --> # kostiantynk-outlook/e4af6307-ae3b-40b1-a021-29ef76ed0e3e This model was trained from scratch on the /workspace/input_data/21c22ef799f14710_train_data.json dataset. It achieves the following results on the evaluation set: - Loss: 2.9476 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
TheCodingHornet/FaceCreator	TheCodingHornet	2025-05-03T08:50:42Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2025-05-03T08:50:42Z	--- license: bsd-3-clause-clear ---
Kube/emoji-vae-init	Kube	2025-05-03T08:49:19Z	0	0	transformers	[ "transformers", "safetensors", "vae", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T08:49:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
infogeo/e4f5cf65-7457-4a8a-90b4-a9fc0821d190	infogeo	2025-05-03T08:38:50Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/mistral-7b-instruct-v0.2", "base_model:adapter:unsloth/mistral-7b-instruct-v0.2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T08:34:58Z	--- library_name: peft license: apache-2.0 base_model: unsloth/mistral-7b-instruct-v0.2 tags: - axolotl - generated_from_trainer model-index: - name: e4f5cf65-7457-4a8a-90b4-a9fc0821d190 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/mistral-7b-instruct-v0.2 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 21c22ef799f14710_train_data.json ds_type: json format: custom path: /workspace/input_data/21c22ef799f14710_train_data.json type: field_input: prompt field_instruction: instruction field_output: completion format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/e4f5cf65-7457-4a8a-90b4-a9fc0821d190 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/21c22ef799f14710_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: 31f39df8-5dd3-49df-aaa1-cd42c31cb344 wandb_project: s56-28 wandb_run: your_name wandb_runid: 31f39df8-5dd3-49df-aaa1-cd42c31cb344 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e4f5cf65-7457-4a8a-90b4-a9fc0821d190 This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9856 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.0469 \| 0.1116 \| 150 \| 3.9856 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DevQuasar/allenai.OLMo-2-0425-1B-Instruct-GGUF	DevQuasar	2025-05-03T08:37:30Z	0	0	null	[ "gguf", "text-generation", "base_model:allenai/OLMo-2-0425-1B-Instruct", "base_model:quantized:allenai/OLMo-2-0425-1B-Instruct", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-03T08:20:44Z	--- base_model: - allenai/OLMo-2-0425-1B-Instruct pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [allenai/OLMo-2-0425-1B-Instruct](https://huggingface.co/allenai/OLMo-2-0425-1B-Instruct) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
arosidi/swin-tiny-patch4-window7-224-finetuned-eurosat	arosidi	2025-05-03T08:34:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-03T07:58:54Z	--- library_name: transformers license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0484 - Accuracy: 0.9837 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2249 \| 1.0 \| 190 \| 0.0807 \| 0.9726 \| \| 0.1439 \| 2.0 \| 380 \| 0.0535 \| 0.9833 \| \| 0.1036 \| 3.0 \| 570 \| 0.0484 \| 0.9837 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
CuckmeisterFuller/Qwen3-16B-A3B-Q2_K-GGUF	CuckmeisterFuller	2025-05-03T08:30:32Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:kalomaze/Qwen3-16B-A3B", "base_model:quantized:kalomaze/Qwen3-16B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T08:30:05Z	--- base_model: kalomaze/Qwen3-16B-A3B license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # CuckmeisterFuller/Qwen3-16B-A3B-Q2_K-GGUF This model was converted to GGUF format from [`kalomaze/Qwen3-16B-A3B`](https://huggingface.co/kalomaze/Qwen3-16B-A3B) 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/kalomaze/Qwen3-16B-A3B) 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 CuckmeisterFuller/Qwen3-16B-A3B-Q2_K-GGUF --hf-file qwen3-16b-a3b-q2_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo CuckmeisterFuller/Qwen3-16B-A3B-Q2_K-GGUF --hf-file qwen3-16b-a3b-q2_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 CuckmeisterFuller/Qwen3-16B-A3B-Q2_K-GGUF --hf-file qwen3-16b-a3b-q2_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo CuckmeisterFuller/Qwen3-16B-A3B-Q2_K-GGUF --hf-file qwen3-16b-a3b-q2_k.gguf -c 2048 ```
ivangrapher/4851352b-ccd3-4c70-998e-0116b2e22cf1	ivangrapher	2025-05-03T08:27:29Z	0	0	peft	[ "peft", "safetensors", "gemma2", "axolotl", "generated_from_trainer", "base_model:unsloth/gemma-2-2b", "base_model:adapter:unsloth/gemma-2-2b", "license:gemma", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T08:13:41Z	--- library_name: peft license: gemma base_model: unsloth/gemma-2-2b tags: - axolotl - generated_from_trainer model-index: - name: 4851352b-ccd3-4c70-998e-0116b2e22cf1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: unsloth/gemma-2-2b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1c537e6c095229b2_train_data.json ds_type: json format: custom path: /workspace/input_data/1c537e6c095229b2_train_data.json type: field_input: keywords field_instruction: intention field_output: captions_objects format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: ivangrapher/4851352b-ccd3-4c70-998e-0116b2e22cf1 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/1c537e6c095229b2_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e52ffaed-7664-43da-91e5-741ec041028e wandb_project: s56-7 wandb_run: your_name wandb_runid: e52ffaed-7664-43da-91e5-741ec041028e warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 4851352b-ccd3-4c70-998e-0116b2e22cf1 This model is a fine-tuned version of [unsloth/gemma-2-2b](https://huggingface.co/unsloth/gemma-2-2b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5892 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3148 \| 0.0563 \| 150 \| 1.5892 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
jnjj/sfsdfs	jnjj	2025-05-03T08:25:15Z	0	0	transformers	[ "transformers", "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T08:17:00Z	--- license: apache-2.0 library_name: transformers ---
wanghong636/playdell	wanghong636	2025-05-03T08:24:59Z	0	1	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-03T08:24:59Z	--- license: artistic-2.0 ---
lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r32-e3-lr0.0002-mixed-actors_reviews_freeform-new	lisabdunlap	2025-05-03T08:24:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T08:22:19Z	--- base_model: unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B-Instruct-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)
dilqraaldiqs/dfvsdfv	dilqraaldiqs	2025-05-03T08:22:56Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-03T08:22:56Z	--- license: creativeml-openrail-m ---
DevQuasar/allenai.OLMo-2-0425-1B-GGUF	DevQuasar	2025-05-03T08:20:42Z	0	0	null	[ "gguf", "text-generation", "base_model:allenai/OLMo-2-0425-1B", "base_model:quantized:allenai/OLMo-2-0425-1B", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T08:06:05Z	--- base_model: - allenai/OLMo-2-0425-1B pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [allenai/OLMo-2-0425-1B](https://huggingface.co/allenai/OLMo-2-0425-1B) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
runeq/dsaa6000q-asm4-dpo	runeq	2025-05-03T08:20:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T08:19:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF	mradermacher	2025-05-03T08:19:40Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_Tristar_V2.1", "base_model:quantized:Nexesenex/Llama_3.x_70b_Tristar_V2.1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-02T11:30:41Z	--- base_model: Nexesenex/Llama_3.x_70b_Tristar_V2.1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_Tristar_V2.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 24.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q4_1.gguf) \| i1-Q4_1 \| 44.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Tristar_V2.1-i1-GGUF/resolve/main/Llama_3.x_70b_Tristar_V2.1.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Bujurocks/Bujurocks	Bujurocks	2025-05-03T08:18:13Z	0	0	asteroid	[ "asteroid", "Bujurocks", "CALLNOMORE", "music", "en", "fr", "de", "sv", "zh", "ja", "ko", "th", "es", "tr", "gd", "el", "dataset:nvidia/OpenCodeReasoning", "dataset:nvidia/OpenMathReasoning", "dataset:nvidia/Llama-Nemotron-Post-Training-Dataset", "dataset:zwhe99/DeepMath-103K", "base_model:HiDream-ai/HiDream-I1-Full", "base_model:finetune:HiDream-ai/HiDream-I1-Full", "region:us" ]	null	2025-05-03T08:10:16Z	--- datasets: - nvidia/OpenCodeReasoning - nvidia/OpenMathReasoning - nvidia/Llama-Nemotron-Post-Training-Dataset - zwhe99/DeepMath-103K language: - en - fr - de - sv - zh - ja - ko - th - es - tr - gd - el metrics: - accuracy - code_eval - character - chrf - bleu - bertscore - brier_score new_version: microsoft/bitnet-b1.58-2B-4T library_name: asteroid tags: - Bujurocks - CALLNOMORE - music base_model: - microsoft/bitnet-b1.58-2B-4T - meta-llama/Llama-4-Scout-17B-16E-Instruct - HiDream-ai/HiDream-I1-Full ---

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