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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
10-Jobz-Hunting-Sajal-Malik-Viral-Videos/18-TRENDING.Jobz.Hunting.Sajal.Malik.Viral.Video.Leaks.Tutorial	10-Jobz-Hunting-Sajal-Malik-Viral-Videos	2025-04-29T15:57:15Z	0	0	null	[ "region:us" ]	null	2025-04-29T15:57:02Z	<a href="https://sdu.sk/9Ip"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
10-Jobz-Hunting-Sajal-Malik-Viral-Video/18-TRENDING.Jobz.Hunting.Sajal.Malik.Viral.Video.Leaks.Tutorial	10-Jobz-Hunting-Sajal-Malik-Viral-Video	2025-04-29T15:55:13Z	0	0	null	[ "region:us" ]	null	2025-04-29T15:55:08Z	<a href="https://sdu.sk/9Ip"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
nmolnar/gemma-3-finetune	nmolnar	2025-04-29T15:54:13Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T15:53:58Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: nmolnar - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
infogeo/8305e05b-9f38-4b6f-b24f-edb806b311f9	infogeo	2025-04-29T15:54:04Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "base_model:adapter:tokyotech-llm/Llama-3-Swallow-8B-v0.1", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T15:48:51Z	--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: 8305e05b-9f38-4b6f-b24f-edb806b311f9 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: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 80d0cdd3e1fb96a4_train_data.json ds_type: json format: custom path: /workspace/input_data/80d0cdd3e1fb96a4_train_data.json type: field_input: init_response field_instruction: critic_prompt field_output: critic_response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/8305e05b-9f38-4b6f-b24f-edb806b311f9 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/80d0cdd3e1fb96a4_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <\|end_of_text\|> 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: 5b336fff-2d3f-40f3-ad25-701f069f0892 wandb_project: s56-28 wandb_run: your_name wandb_runid: 5b336fff-2d3f-40f3-ad25-701f069f0892 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 8305e05b-9f38-4b6f-b24f-edb806b311f9 This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3112 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2752 \| 0.0288 \| 150 \| 1.3112 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
9-SophieRainSpiderman-Viral-Videoss/Sophie.Rain.Spiderman.Viral.Video.Leaks.official	9-SophieRainSpiderman-Viral-Videoss	2025-04-29T15:53:01Z	0	0	null	[ "region:us" ]	null	2025-04-29T15:52:40Z	<animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" 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>
lfhe/FLock-Arena-Task-8-Qwen3-0.6B	lfhe	2025-04-29T15:51:25Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen3-0.6B", "base_model:adapter:Qwen/Qwen3-0.6B", "region:us" ]	null	2025-04-29T15:11:44Z	--- base_model: Qwen/Qwen3-0.6B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
JonnyRed/finetuning-sentiment-model-3000-samples	JonnyRed	2025-04-29T15:51:18Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T15:37:24Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: finetuning-sentiment-model-3000-samples 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
shallow6414/sn11-2-7-2	shallow6414	2025-04-29T15:50:53Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T15:50:49Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
golf2248/sn11-v4-3-2	golf2248	2025-04-29T15:50:42Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T15:50:38Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
wassname/qwen-7B-fourchan	wassname	2025-04-29T15:49:30Z	0	0	transformers	[ "transformers", "qwen2", "feature-extraction", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-Coder-7B-Instruct", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-29T15:47:29Z	--- base_model: unsloth/Qwen2.5-Coder-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: wassname - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-Coder-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MikuMasterRace/Hatsune_Miku_-_Usamiku_Furry_-_IllustriousXL_v1	MikuMasterRace	2025-04-29T15:43:41Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:adapter:OnomaAIResearch/Illustrious-xl-early-release-v0", "region:us" ]	text-to-image	2025-04-29T15:39:29Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '1girl, solo, hatsune miku, usamiku, aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, animal hands, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furrification, cowboy shot, one eye closed, zettai ryouiki, sparkle, open mouth, smile, looking at viewer, looking at viewer, white background, safe, newset, omufujoshi, black outline, thick outlines, masterpiece, best quality, amazing quality' output: url: images/ComfyUI_(hiresfix)_2025-04-29_00000_8.png - text: '1girl, solo, hatsune miku, usamiku, aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headphones, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, animal hands, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furry, furrification, holding doll, fumo $doll$, head tilt, portrait, sparkle, open mouth, smile, looking at another, white background, safe, newset, omufujoshi, black outline, thick outlines, masterpiece, best quality, amazing quality' output: url: images/ComfyUI_(hiresfix)_2025-04-29_00000_5.png - text: '1girl, solo, hatsune miku, usamiku, aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, number print, thigh boots, animal hands, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furry, furrification, closed mouth, smile, looking back, white background, safe, newset, omufujoshi, black outline, thick outlines, masterpiece, best quality, amazing quality' output: url: images/ComfyUI_(hiresfix)_2025-04-29_00000_7.png base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 instance_prompt: null --- # Usamiku / Furry Miku (Hatsune Miku) v1 [IllustriousXL 0.1] <Gallery /> ## Reference This is a kigurumi cosplay of Hatsune Miku. She won the "Miku Lookalike Contest" in NYC in 2025. Socials: [twitter@mikusagi01](https://x.com/mikusagi01), [tiktok@mikusagi01](https://www.tiktok.com/@mikusagi01?lang=en) [![](images/reference.jpg)](https://x.com/ziepoopenfarten/status/1906077150563688871) ## Prompting Main triggerword: ``` usamiku ``` Appearance and clothing: ``` aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, number print, animal hands, rabbit tail, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furrification ``` ## Download model Weights for this model are available in Safetensors format. [Download](/MikuMasterRace/Hatsune_Miku_-_Usamiku_Furry_-_IllustriousXL_v1/tree/main) them in the Files & versions tab.
LiliaBakh/zhanna_lora_1_april_2025	LiliaBakh	2025-04-29T15:43:16Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T15:29:41Z	--- 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: zhanna --- # Zhanna_Lora_1_April_2025 <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 `zhanna` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "zhanna", "lora_weights": "https://huggingface.co/LiliaBakh/zhanna_lora_1_april_2025/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('LiliaBakh/zhanna_lora_1_april_2025', weight_name='lora.safetensors') image = pipeline('zhanna').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/LiliaBakh/zhanna_lora_1_april_2025/discussions) to add images that show off what you’ve made with this LoRA.
joboffer/5657c968-f623-424f-b7db-2cffa752631f	joboffer	2025-04-29T15:43:11Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-14B-Instruct", "base_model:adapter:Qwen/Qwen2.5-14B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T15:34:31Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-14B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 5657c968-f623-424f-b7db-2cffa752631f results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-14B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 6672ff8cbabd744e_train_data.json ds_type: json format: custom path: /workspace/input_data/6672ff8cbabd744e_train_data.json type: field_input: thinking field_instruction: prompt field_output: answer format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: joboffer/5657c968-f623-424f-b7db-2cffa752631f hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/6672ff8cbabd744e_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: 777fb87d-b5fc-446f-96ca-5871a5b464cc wandb_project: s56-33 wandb_run: your_name wandb_runid: 777fb87d-b5fc-446f-96ca-5871a5b464cc warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 5657c968-f623-424f-b7db-2cffa752631f This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0626 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9065 \| 0.1125 \| 200 \| 1.0626 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JKilpatrick/legal-ft-3	JKilpatrick	2025-04-29T15:40:55Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:156", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:Snowflake/snowflake-arctic-embed-l", "base_model:finetune:Snowflake/snowflake-arctic-embed-l", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-29T15:39:29Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:156 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: What is the mlx-vlm project by Prince Canuma known for in relation to Apple Silicon? sentences: - 'These abilities are just a few weeks old at this point, and I don’t think their impact has been fully felt yet. If you haven’t tried them out yet you really should. Both Gemini and OpenAI offer API access to these features as well. OpenAI started with a WebSocket API that was quite challenging to use, but in December they announced a new WebRTC API which is much easier to get started with. Building a web app that a user can talk to via voice is easy now! Prompt driven app generation is a commodity already This was possible with GPT-4 in 2023, but the value it provides became evident in 2024.' - 'Prince Canuma’s excellent, fast moving mlx-vlm project brings vision LLMs to Apple Silicon as well. I used that recently to run Qwen’s QvQ. While MLX is a game changer, Apple’s own “Apple Intelligence” features have mostly been a disappointment. I wrote about their initial announcement in June, and I was optimistic that Apple had focused hard on the subset of LLM applications that preserve user privacy and minimize the chance of users getting mislead by confusing features.' - 'Things we learned about LLMs in 2024 Simon Willison’s Weblog Subscribe Things we learned about LLMs in 2024 31st December 2024 A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments. This is a sequel to my review of 2023. In this article:' - source_sentence: What is the cost per million tokens for OpenAI’s most expensive model, o1? sentences: - The most recent twist, again from December (December was a lot) is live video. ChatGPT voice mode now provides the option to share your camera feed with the model and talk about what you can see in real time. Google Gemini have a preview of the same feature, which they managed to ship the day before ChatGPT did. - 'Today $30/mTok gets you OpenAI’s most expensive model, o1. GPT-4o is $2.50 (12x cheaper than GPT-4) and GPT-4o mini is $0.15/mTok—200x cheaper than GPT-4, nearly 7x cheaper than GPT-3.5 and massively more capable than that model. Other model providers charge even less. Anthropic’s Claude 3 Haiku (from March, but still their cheapest model) is $0.25/mTok. Google’s Gemini 1.5 Flash is $0.075/mTok and their Gemini 1.5 Flash 8B is $0.0375/mTok—that’s 27x cheaper than GPT-3.5 Turbo last year. I’ve been tracking these pricing changes under my llm-pricing tag.' - 'Watching in real time as “slop” becomes a term of art. the way that “spam” became the term for unwanted emails, “slop” is going in the dictionary as the term for unwanted AI generated content I expanded that definition a tiny bit to this: Slop describes AI-generated content that is both unrequested and unreviewed. I ended up getting quoted talking about slop in both the Guardian and the NY Times. Here’s what I said in the NY TImes: Society needs concise ways to talk about modern A.I. — both the positives and the negatives. ‘Ignore that email, it’s spam,’ and ‘Ignore that article, it’s slop,’ are both useful lessons.' - source_sentence: What are some of the reasons people dislike large language models (LLMs) mentioned in the context? sentences: - '260 input tokens, 92 output tokens. Cost approximately 0.0024 cents (that’s less than a 400th of a cent). This increase in efficiency and reduction in price is my single favourite trend from 2024. I want the utility of LLMs at a fraction of the energy cost and it looks like that’s what we’re getting. Multimodal vision is common, audio and video are starting to emerge My butterfly example above illustrates another key trend from 2024: the rise of multi-modal LLMs. A year ago the single most notable example of these was GPT-4 Vision, released at OpenAI’s DevDay in November 2023. Google’s multi-modal Gemini 1.0 was announced on December 7th 2023 so it also (just) makes it into the 2023 window.' - 'A lot of people absolutely hate this stuff. In some of the spaces I hang out (Mastodon, Bluesky, Lobste.rs, even Hacker News on occasion) even suggesting that “LLMs are useful” can be enough to kick off a huge fight. I get it. There are plenty of reasons to dislike this technology—the environmental impact, the (lack of) ethics of the training data, the lack of reliability, the negative applications, the potential impact on people’s jobs. LLMs absolutely warrant criticism. We need to be talking through these problems, finding ways to mitigate them and helping people learn how to use these tools responsibly in ways where the positive applications outweigh the negative.' - 'Meta’s Llama 3.2 models deserve a special mention. They may not be GPT-4 class, but at 1B and 3B sizes they punch massively above their weight. I run Llama 3.2 3B on my iPhone using the free MLC Chat iOS app and it’s a shockingly capable model for its tiny (<2GB) size. Try firing it up and asking it for “a plot outline of a Netflix Christmas movie where a data journalist falls in love with a local ceramacist”. Here’s what I got, at a respectable 20 tokens per second:' - source_sentence: Why does the author find the term “agents” frustrating? sentences: - 'Against this photo of butterflies at the California Academy of Sciences: A shallow dish, likely a hummingbird or butterfly feeder, is red. Pieces of orange slices of fruit are visible inside the dish. Two butterflies are positioned in the feeder, one is a dark brown/black butterfly with white/cream-colored markings. The other is a large, brown butterfly with patterns of lighter brown, beige, and black markings, including prominent eye spots. The larger brown butterfly appears to be feeding on the fruit.' - 'These price drops are driven by two factors: increased competition and increased efficiency. The efficiency thing is really important for everyone who is concerned about the environmental impact of LLMs. These price drops tie directly to how much energy is being used for running prompts. There’s still plenty to worry about with respect to the environmental impact of the great AI datacenter buildout, but a lot of the concerns over the energy cost of individual prompts are no longer credible. Here’s a fun napkin calculation: how much would it cost to generate short descriptions of every one of the 68,000 photos in my personal photo library using Google’s Gemini 1.5 Flash 8B (released in October), their cheapest model?' - '“Agents” still haven’t really happened yet I find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that. If you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.' - source_sentence: What new feature did Anthropic release that allows Claude to write on-demand interactive applications? sentences: - 'We already knew LLMs were spookily good at writing code. If you prompt them right, it turns out they can build you a full interactive application using HTML, CSS and JavaScript (and tools like React if you wire up some extra supporting build mechanisms)—often in a single prompt. Anthropic kicked this idea into high gear when they released Claude Artifacts, a groundbreaking new feature that was initially slightly lost in the noise due to being described half way through their announcement of the incredible Claude 3.5 Sonnet. With Artifacts, Claude can write you an on-demand interactive application and then let you use it directly inside the Claude interface. Here’s my Extract URLs app, entirely generated by Claude:' - 'Stuff we figured out about AI in 2023 Simon Willison’s Weblog Subscribe Stuff we figured out about AI in 2023 31st December 2023 2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s. Here’s my attempt to round up the highlights in one place!' - 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes! The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage. The year of slop' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.9583333333333334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9583333333333334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9583333333333334 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9846220730654774 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9791666666666666 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9791666666666666 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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: [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 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': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("JKilpatrick/legal-ft-3") # Run inference sentences = [ 'What new feature did Anthropic release that allows Claude to write on-demand interactive applications?', 'We already knew LLMs were spookily good at writing code. If you prompt them right, it turns out they can build you a full interactive application using HTML, CSS and JavaScript (and tools like React if you wire up some extra supporting build mechanisms)—often in a single prompt.\nAnthropic kicked this idea into high gear when they released Claude Artifacts, a groundbreaking new feature that was initially slightly lost in the noise due to being described half way through their announcement of the incredible Claude 3.5 Sonnet.\nWith Artifacts, Claude can write you an on-demand interactive application and then let you use it directly inside the Claude interface.\nHere’s my Extract URLs app, entirely generated by Claude:', 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes!\nThe resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage.\nThe year of slop', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.9583 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 0.9583 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 0.9583 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 0.9846 \| \| cosine_mrr@10 \| 0.9792 \| \| cosine_map@100 \| 0.9792 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 156 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 156 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 14 tokens</li><li>mean: 20.44 tokens</li><li>max: 37 tokens</li></ul> \| <ul><li>min: 43 tokens</li><li>mean: 130.83 tokens</li><li>max: 192 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:----------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Why does the author find the term “agents” frustrating?</code> \| <code>“Agents” still haven’t really happened yet<br>I find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that.<br>If you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.</code> \| \| <code>What issue does the author highlight about people who use the term “agents”?</code> \| <code>“Agents” still haven’t really happened yet<br>I find the term “agents” extremely frustrating. It lacks a single, clear and widely understood meaning... but the people who use the term never seem to acknowledge that.<br>If you tell me that you are building “agents”, you’ve conveyed almost no information to me at all. Without reading your mind I have no way of telling which of the dozens of possible definitions you are talking about.</code> \| \| <code>What are some challenges mentioned in building large language models like GPT-4?</code> \| <code>Large Language Models<br>They’re actually quite easy to build<br>You can run LLMs on your own devices<br>Hobbyists can build their own fine-tuned models<br>We don’t yet know how to build GPT-4<br>Vibes Based Development<br>LLMs are really smart, and also really, really dumb<br>Gullibility is the biggest unsolved problem<br>Code may be the best application<br>The ethics of this space remain diabolically complex<br>My blog in 2023</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| cosine_ndcg@10 \| \|:-----:\|:----:\|:--------------:\| \| 1.0 \| 16 \| 0.9692 \| \| 2.0 \| 32 \| 0.9846 \| \| 3.0 \| 48 \| 0.9846 \| \| 3.125 \| 50 \| 0.9846 \| \| 4.0 \| 64 \| 0.9846 \| \| 5.0 \| 80 \| 0.9846 \| \| 6.0 \| 96 \| 0.9846 \| \| 6.25 \| 100 \| 0.9846 \| \| 7.0 \| 112 \| 0.9846 \| \| 8.0 \| 128 \| 0.9846 \| \| 9.0 \| 144 \| 0.9846 \| \| 9.375 \| 150 \| 0.9846 \| \| 10.0 \| 160 \| 0.9846 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
AudreyTrungNguyen/qwen2.5-math7b-distill-DeepSeek-R1-Distill-Qwen-14B-classification-math	AudreyTrungNguyen	2025-04-29T15:39:58Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T10:25:34Z	--- base_model: unsloth/qwen2.5-math-7b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AudreyTrungNguyen - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-math-7b-instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
thoddnn/colqwen2-v1.0	thoddnn	2025-04-29T15:38:16Z	0	0	colpali	[ "colpali", "safetensors", "vidore-experimental", "vidore", "visual-document-retrieval", "en", "arxiv:2004.12832", "arxiv:2407.01449", "arxiv:2106.09685", "base_model:vidore/colqwen2-base", "base_model:finetune:vidore/colqwen2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	visual-document-retrieval	2025-04-29T15:38:15Z	--- license: apache-2.0 library_name: colpali base_model: vidore/colqwen2-base language: - en tags: - colpali - vidore-experimental - vidore pipeline_tag: visual-document-retrieval --- # ColQwen2: Visual Retriever based on Qwen2-VL-2B-Instruct with ColBERT strategy ### This is the base version trained with batch_size 256 instead of 32 for 5 epoch and with the updated pad token ColQwen2 is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a [Qwen2-VL-2B](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) extension that generates [ColBERT](https://arxiv.org/abs/2004.12832)- style multi-vector representations of text and images. It was introduced in the paper [ColPali: Efficient Document Retrieval with Vision Language Models](https://arxiv.org/abs/2407.01449) and first released in [this repository](https://github.com/ManuelFay/colpali) <p align="center"><img width=800 src="https://github.com/illuin-tech/colpali/blob/main/assets/colpali_architecture.webp?raw=true"/></p> ## Version specificity This model takes dynamic image resolutions in input and does not resize them, changing their aspect ratio as in ColPali. Maximal resolution is set so that 768 image patches are created at most. Experiments show clear improvements with larger amounts of image patches, at the cost of memory requirements. This version is trained with `colpali-engine==0.3.1`. Data is the same as the ColPali data described in the paper. ## Model Training ### Dataset Our training dataset of 127,460 query-page pairs is comprised of train sets of openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%). Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages. We explicitly verify no multi-page PDF document is used both [ViDoRe](https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d) and in the train set to prevent evaluation contamination. A validation set is created with 2% of the samples to tune hyperparameters. Note: Multilingual data is present in the pretraining corpus of the language model and most probably in the multimodal training. ### Parameters All models are trained for 1 epoch on the train set. Unless specified otherwise, we train models in `bfloat16` format, use low-rank adapters ([LoRA](https://arxiv.org/abs/2106.09685)) with `alpha=32` and `r=32` on the transformer layers from the language model, as well as the final randomly initialized projection layer, and use a `paged_adamw_8bit` optimizer. We train on an 8 GPU setup with data parallelism, a learning rate of 5e-5 with linear decay with 2.5% warmup steps, and a batch size of 32. ## Usage Make sure `colpali-engine` is installed from source or with a version superior to 0.3.4. `transformers` version must be > 4.46.1. ```bash pip install git+https://github.com/illuin-tech/colpali ``` ```python import torch from PIL import Image from transformers.utils.import_utils import is_flash_attn_2_available from colpali_engine.models import ColQwen2, ColQwen2Processor model = ColQwen2.from_pretrained( "vidore/colqwen2-v1.0", torch_dtype=torch.bfloat16, device_map="cuda:0", # or "mps" if on Apple Silicon attn_implementation="flash_attention_2" if is_flash_attn_2_available() else None, ).eval() processor = ColQwen2Processor.from_pretrained("vidore/colqwen2-v1.0") # Your inputs images = [ Image.new("RGB", (128, 128), color="white"), Image.new("RGB", (64, 32), color="black"), ] queries = [ "Is attention really all you need?", "What is the amount of bananas farmed in Salvador?", ] # Process the inputs batch_images = processor.process_images(images).to(model.device) batch_queries = processor.process_queries(queries).to(model.device) # Forward pass with torch.no_grad(): image_embeddings = model(batch_images) query_embeddings = model(batch_queries) scores = processor.score_multi_vector(query_embeddings, image_embeddings) ``` ## Limitations - Focus: The model primarily focuses on PDF-type documents and high-ressources languages, potentially limiting its generalization to other document types or less represented languages. - Support: The model relies on multi-vector retreiving derived from the ColBERT late interaction mechanism, which may require engineering efforts to adapt to widely used vector retrieval frameworks that lack native multi-vector support. ## License ColQwen2's vision language backbone model (Qwen2-VL) is under `apache2.0` license. The adapters attached to the model are under MIT license. ## Contact - Manuel Faysse: [email protected] - Hugues Sibille: [email protected] - Tony Wu: [email protected] ## Citation If you use any datasets or models from this organization in your research, please cite the original dataset as follows: ```bibtex @misc{faysse2024colpaliefficientdocumentretrieval, title={ColPali: Efficient Document Retrieval with Vision Language Models}, author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo}, year={2024}, eprint={2407.01449}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2407.01449}, } ```
gabrielc2025/Taxi-v3	gabrielc2025	2025-04-29T15:36:06Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-04-29T15:36:02Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.62 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="gabrielc2025/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"]) ```
thoddnn/whisper-large-v3-turbo-q4	thoddnn	2025-04-29T15:35:27Z	0	0	mlx	[ "mlx", "whisper", "region:us" ]	null	2025-04-29T15:35:27Z	--- library_name: mlx --- # whisper-large-v3-turbo-q4 This model was converted to MLX format from [`openai/whisper-large-v3-turbo`](). ## Use with mlx ```bash pip install mlx-whisper ``` ```python import mlx_whisper result = mlx_whisper.transcribe( "FILE_NAME", path_or_hf_repo=mlx-community/whisper-large-v3-turbo-q4, ) ```
Orion-zhen/Qwen3-4B-AWQ	Orion-zhen	2025-04-29T15:35:12Z	0	1	null	[ "safetensors", "qwen3", "base_model:Qwen/Qwen3-4B", "base_model:quantized:Qwen/Qwen3-4B", "license:gpl-3.0", "4-bit", "awq", "region:us" ]	null	2025-04-29T14:58:47Z	--- license: gpl-3.0 base_model: - Qwen/Qwen3-4B --- # Qwen3-4B-AWQ ```yaml zero_piont: true bits: 4 version: GEMM dataset: wikitext + Orion-zhen/gsm8k-r1-qwen-32b num_examples: 256 ```
TOMFORD79/Smart8	TOMFORD79	2025-04-29T15:34:29Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-29T15:02:53Z	--- 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).
Yehooor/Altron_fake_detection_model_lora	Yehooor	2025-04-29T15:34:18Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "uk", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T15:30:44Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - uk - en --- # Uploaded model - Developed by: Yehooor - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
TOMFORD79/Smart7	TOMFORD79	2025-04-29T15:33:49Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-29T15:02:47Z	--- 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).
thoddnn/Qwen2-VL-2B-Instruct-8bit	thoddnn	2025-04-29T15:33:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "multimodal", "mlx", "conversational", "en", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-29T15:33:02Z	--- language: - en library_name: transformers license: apache-2.0 pipeline_tag: image-text-to-text tags: - multimodal - mlx --- # mlx-community/Qwen2-VL-2B-Instruct-8bit This model was converted to MLX format from [`Qwen/Qwen2-VL-2B-Instruct`]() using mlx-vlm version 0.0.13. Refer to the [original model card](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) for more details on the model. ## Use with mlx ```bash pip install -U mlx-vlm ``` ```bash python -m mlx_vlm.generate --model mlx-community/Qwen2-VL-2B-Instruct-8bit --max-tokens 100 --temp 0.0 ```
dgambettaphd/M_llm2_gen1_run0_W_doc1000_synt64_tot128_lr5em5_SYNLAST	dgambettaphd	2025-04-29T15:32:48Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T15:32:37Z	--- 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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jsano/finetuned-model-llama3b	jsano	2025-04-29T15:28:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T15:28:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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Hazde/careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_4	Hazde	2025-04-29T15:28:23Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2024-11-12T02:17:05Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - generated_from_trainer language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara model-index: - name: careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_4 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. --> # careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_4 This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9322 ## 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: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.2234 \| 1.0 \| 674 \| 2.9322 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.0+cu124 - Datasets 2.19.0 - Tokenizers 0.20.1
Hazde/careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_2	Hazde	2025-04-29T15:28:14Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2024-11-11T22:51:33Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - generated_from_trainer language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara model-index: - name: careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_2 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. --> # careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA_2 This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9414 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.3394 \| 1.0 \| 674 \| 3.9414 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.0+cu124 - Datasets 2.19.0 - Tokenizers 0.20.1
Hazde/careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA	Hazde	2025-04-29T15:28:06Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2024-11-11T21:06:27Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - generated_from_trainer language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara model-index: - name: careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA 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. --> # careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_LoRA This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 449 \| 4.2482 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.0+cu124 - Datasets 2.19.0 - Tokenizers 0.20.1
Hazde/careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_small	Hazde	2025-04-29T15:27:35Z	7	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2024-11-04T23:40:57Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - generated_from_trainer language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara model-index: - name: careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_small 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. --> # careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model_small This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 212 \| 1.2660 \| \| No log \| 2.0 \| 424 \| 1.2264 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.0+cu124 - Datasets 2.19.0 - Tokenizers 0.20.1
jsano/finetuned-model	jsano	2025-04-29T15:27:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T11:26:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
TheMindExpansionNetwork/M1NDB0T-1111-14B-Q4_K_M-GGUF	TheMindExpansionNetwork	2025-04-29T15:27:05Z	0	0	transformers	[ "transformers", "gguf", "mindbot", "synthetic-entity", "agi-companion", "digital-human", "llama-factory", "qwen3-14b", "mindexpander", "llama-cpp", "gguf-my-repo", "base_model:TheMindExpansionNetwork/M1NDB0T-1111-14B", "base_model:quantized:TheMindExpansionNetwork/M1NDB0T-1111-14B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T15:26:26Z	--- base_model: TheMindExpansionNetwork/M1NDB0T-1111-14B library_name: transformers tags: - mindbot - synthetic-entity - agi-companion - digital-human - llama-factory - qwen3-14b - mindexpander - llama-cpp - gguf-my-repo --- # TheMindExpansionNetwork/M1NDB0T-1111-14B-Q4_K_M-GGUF This model was converted to GGUF format from [`TheMindExpansionNetwork/M1NDB0T-1111-14B`](https://huggingface.co/TheMindExpansionNetwork/M1NDB0T-1111-14B) 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/TheMindExpansionNetwork/M1NDB0T-1111-14B) 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 TheMindExpansionNetwork/M1NDB0T-1111-14B-Q4_K_M-GGUF --hf-file m1ndb0t-1111-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo TheMindExpansionNetwork/M1NDB0T-1111-14B-Q4_K_M-GGUF --hf-file m1ndb0t-1111-14b-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 TheMindExpansionNetwork/M1NDB0T-1111-14B-Q4_K_M-GGUF --hf-file m1ndb0t-1111-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo TheMindExpansionNetwork/M1NDB0T-1111-14B-Q4_K_M-GGUF --hf-file m1ndb0t-1111-14b-q4_k_m.gguf -c 2048 ```
Hazde/careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model	Hazde	2025-04-29T15:27:03Z	8	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2024-10-31T17:16:26Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-0.5B-Instruct tags: - generated_from_trainer language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara model-index: - name: careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # careerbot_PG6_Qwen_Qwen2.5-0.5B-Instruct_model This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4229 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAFACTOR and the args are: No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 5072 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| No log \| 0.9968 \| 158 \| 1.0200 \| \| No log \| 2.0 \| 317 \| 0.9880 \| \| No log \| 2.9968 \| 475 \| 0.9873 \| \| No log \| 4.0 \| 634 \| 1.0426 \| \| No log \| 4.9968 \| 792 \| 1.0514 \| \| No log \| 6.0 \| 951 \| 1.0938 \| \| No log \| 6.9968 \| 1109 \| 1.0742 \| \| No log \| 8.0 \| 1268 \| 1.1283 \| \| No log \| 8.9968 \| 1426 \| 1.1356 \| \| No log \| 10.0 \| 1585 \| 1.1581 \| \| No log \| 10.9968 \| 1743 \| 1.2045 \| \| No log \| 12.0 \| 1902 \| 1.2060 \| \| No log \| 12.9968 \| 2060 \| 1.2354 \| \| No log \| 14.0 \| 2219 \| 1.2285 \| \| No log \| 14.9968 \| 2377 \| 1.2401 \| \| No log \| 16.0 \| 2536 \| 1.2986 \| \| No log \| 16.9968 \| 2694 \| 1.2904 \| \| No log \| 18.0 \| 2853 \| 1.3051 \| \| No log \| 18.9968 \| 3011 \| 1.3109 \| \| No log \| 20.0 \| 3170 \| 1.3154 \| \| No log \| 20.9968 \| 3328 \| 1.3202 \| \| No log \| 22.0 \| 3487 \| 1.3282 \| \| No log \| 22.9968 \| 3645 \| 1.3385 \| \| No log \| 24.0 \| 3804 \| 1.3295 \| \| No log \| 24.9968 \| 3962 \| 1.3512 \| \| No log \| 26.0 \| 4121 \| 1.3583 \| \| No log \| 26.9968 \| 4279 \| 1.3666 \| \| No log \| 28.0 \| 4438 \| 1.3841 \| \| No log \| 28.9968 \| 4596 \| 1.3938 \| \| No log \| 30.0 \| 4755 \| 1.4084 \| \| No log \| 30.9968 \| 4913 \| 1.4178 \| \| No log \| 32.0 \| 5072 \| 1.4229 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.0+cu124 - Datasets 2.19.0 - Tokenizers 0.20.1
mradermacher/Qwerus-7B-GGUF	mradermacher	2025-04-29T15:26:50Z	170	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:mlabonne/Qwerus-7B", "base_model:quantized:mlabonne/Qwerus-7B", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-25T22:28:11Z	--- base_model: mlabonne/Qwerus-7B language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: mit quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mlabonne/Qwerus-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwerus-7B-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/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-GGUF/resolve/main/Qwerus-7B.f16.gguf) \| f16 \| 15.3 \| 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 -->
Vittorwo/Jogo	Vittorwo	2025-04-29T15:26:25Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T15:26:22Z	--- license: apache-2.0 ---
cocoroloco/distilbert-review-classification	cocoroloco	2025-04-29T15:24:27Z	0	0	null	[ "safetensors", "distilbert", "text-classification", "sentiment-analysis", "reviews", "spanish", "es", "dataset:amazon_reviews_multi", "license:mit", "model-index", "region:us" ]	text-classification	2025-04-29T15:16:06Z	--- language: - es license: mit tags: - text-classification - sentiment-analysis - reviews - distilbert - spanish datasets: - amazon_reviews_multi metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-review_classification results: - task: type: text-classification name: Clasificación de reseñas (5 clases) dataset: name: amazon_reviews_multi (español) type: amazon_reviews_multi metrics: - type: accuracy value: 0.5808 - type: f1 value: 0.58158 pipeline_tag: text-classification widget: - text: "Este producto es increíble, funciona perfectamente y el precio es excelente." - text: "La calidad del producto deja mucho que desear y llegó con un retraso considerable." --- # distilbert-review_classification Este modelo es una variante de DistilBERT entrenada para la clasificación de reseñas de Amazon en español. Está basado en `distilbert-base-multilingual` y ha sido afinado para predecir calificaciones de estrellas (1-5) a partir del texto de la reseña. ## Modelo Arquitectura base: DistilBERT (distilbert-base-multilingual) Tarea: Clasificación de texto (5 clases) Idioma: Español Caso de uso: Análisis de sentimiento y clasificación de reseñas ## Rendimiento El modelo fue evaluado en un conjunto de datos balanceado con 1000 muestras para cada clase (calificación de 1 a 5 estrellas): \| Métrica \| Valor \| \|---------\|-------\| \| Exactitud (Accuracy) \| 0.5808 \| \| F1 Score (macro promedio) \| 0.58158 \| \| Precisión (macro promedio) \| 0.58303 \| \| Recall (macro promedio) \| 0.5808 \| ### Rendimiento por clase \| Clase \| Precisión \| Recall \| F1 Score \| Soporte \| \|-------\|-----------\|--------\|----------\|---------\| \| 1 ⭐ \| 0.72069 \| 0.707 \| 0.71378 \| 1000 \| \| 2 ⭐ \| 0.50409 \| 0.554 \| 0.52787 \| 1000 \| \| 3 ⭐ \| 0.48916 \| 0.474 \| 0.48146 \| 1000 \| \| 4 ⭐ \| 0.51613 \| 0.512 \| 0.51406 \| 1000 \| \| 5 ⭐ \| 0.68509 \| 0.657 \| 0.67075 \| 1000 \| ## Detalles de entrenamiento * Epochs: 1 * Pasos de entrenamiento: 50,000 * Tiempo de entrenamiento: ~8.2 horas (29,486 segundos) * Loss final: 0.9721 ## Uso ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Cargar modelo y tokenizer tokenizer = AutoTokenizer.from_pretrained("polodealvarado/distilbert-review_classification") model = AutoModelForSequenceClassification.from_pretrained("polodealvarado/distilbert-review_classification") # Preparar el texto de entrada texto = "Este producto superó mis expectativas, lo recomiendo totalmente." inputs = tokenizer(texto, return_tensors="pt", padding=True, truncation=True, max_length=512) # Realizar la predicción with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits predicted_class = torch.argmax(logits, dim=1).item() # La clase predicha será un número del 0 al 4, que corresponde a 1-5 estrellas estrellas_predichas = predicted_class + 1 print(f"Predicción: {estrellas_predichas} estrellas") ``` ## Limitaciones - El modelo fue entrenado con datos de reseñas de Amazon, por lo que puede tener un rendimiento reducido en otros dominios. - El rendimiento es más alto para reseñas claramente positivas (5 estrellas) o c
mradermacher/Qwerus-7B-i1-GGUF	mradermacher	2025-04-29T15:23:47Z	371	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:mlabonne/Qwerus-7B", "base_model:quantized:mlabonne/Qwerus-7B", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-03-26T21:14:34Z	--- base_model: mlabonne/Qwerus-7B language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: mit quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mlabonne/Qwerus-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwerus-7B-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/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwerus-7B-i1-GGUF/resolve/main/Qwerus-7B.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
melijauregui/fashionSigLIP-roturas15v2	melijauregui	2025-04-29T15:23:27Z	0	0	transformers	[ "transformers", "safetensors", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2025-04-29T15:22:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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lm-kit/nomic-embed-vision-1.5-lmk	lm-kit	2025-04-29T15:23:12Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-21T13:47:27Z	--- license: apache-2.0 --- Nomic Embed Vision Original model: https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5 This repository contains the Nomic Embed Vision model stored in an .lmk file format, designed for inference with the LM-Kit SDK.
luis20209560/tedi	luis20209560	2025-04-29T15:22:39Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T15:22:38Z	--- license: apache-2.0 ---
mlfoundations-dev/Qwen2.5-7B-Instruct_d1_science_long_paragraphs	mlfoundations-dev	2025-04-29T15:22:06Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T15:19:31Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: Qwen2.5-7B-Instruct_d1_science_long_paragraphs 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. --> # Qwen2.5-7B-Instruct_d1_science_long_paragraphs This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/d1_science_long_paragraphs dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.0a0+b465a5843b.nv24.09 - Datasets 3.5.0 - Tokenizers 0.20.3
cdgrande/Test	cdgrande	2025-04-29T15:21:42Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-01-18T22:51:58Z	--- license: apache-2.0 ---
lm-kit/qwen-3-0.6b-instruct-gguf	lm-kit	2025-04-29T15:21:29Z	15	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T08:25:53Z	--- license: apache-2.0 --- ## Model Summary This repository hosts quantized versions of the Alibaba Qwen-3 Instruct 0.6B model. Format: GGUF Converter: llama.cpp b6ce7430b7eb51f032152316880204e0a9c0470e Quantizer: LM-Kit.NET 2025.4.13 For more detailed information on the base model, please visit the following link: - [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
lm-kit/qwen-3-8b-instruct-gguf	lm-kit	2025-04-29T15:20:24Z	1	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T08:26:55Z	--- license: apache-2.0 --- ## Model Summary This repository hosts quantized versions of the Alibaba Qwen-3 Instruct 8B model. Format: GGUF Converter: llama.cpp b6ce7430b7eb51f032152316880204e0a9c0470e Quantizer: LM-Kit.NET 2025.4.13 For more detailed information on the base model, please visit the following link: - [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
AlphaGaO/Qwen3-1.7B-GPTQ	AlphaGaO	2025-04-29T15:20:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-1.7B-Base", "base_model:quantized:Qwen/Qwen3-1.7B-Base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2025-04-29T15:14:24Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen3-1.7B-Base --- # Qwen3-1.7B-GPTQ GPTQ Quantized model, tuned with dataset AlphaGaO/fused_distillation_dataset bits: 4 group_size: 128 is_marlin_format: True <a href="https://chat.qwen.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> ## Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. ## Model Overview Qwen3-1.7B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 1.7B - Number of Paramaters (Non-Embedding): 1.4B - Number of Layers: 28 - Number of Attention Heads (GQA): 16 for Q and 8 for KV - Context Length: 32,768 For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). > [!TIP] > If you encounter significant endless repetitions, please refer to the [Best Practices](#best-practices) section for optimal sampling parameters, and set the ``presence_penalty`` to 1.5. ## Quickstart The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-1.7B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-1.7B --reasoning-parser qwen3 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-1.7B --enable-reasoning --reasoning-parser deepseek_r1 ``` For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Switching Between Thinking and Non-Thinking Mode > [!TIP] > The `enable_thinking` switch is also available in APIs created by SGLang and vLLM. > Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users. ### `enable_thinking=True` By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response. > [!NOTE] > For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### `enable_thinking=False` We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode ) ``` In this mode, the model will not generate any think content and will not include a `<think>...</think>` block. > [!NOTE] > For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. Here is an example of a multi-turn conversation: ```python from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-1.7B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usage if __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}") ``` > [!NOTE] > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-1.7B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # }, } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3, title = {Qwen3}, url = {https://qwenlm.github.io/blog/qwen3/}, author = {Qwen Team}, month = {April}, year = {2025} } ```
ramcargpt/Batt_Gemma7B_38K_128	ramcargpt	2025-04-29T15:18:02Z	0	0	transformers	[ "transformers", "gguf", "gemma", "text-generation-inference", "unsloth", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:quantized:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T15:16:11Z	--- base_model: unsloth/gemma-7b-it-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: ramcargpt - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma 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)
mradermacher/QwenPhi-4-0.5b-Draft-GGUF	mradermacher	2025-04-29T15:17:56Z	238	0	transformers	[ "transformers", "gguf", "qwen", "qwen2.5", "phi-4", "phi", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:rdsm/QwenPhi-4-0.5b-Draft", "base_model:quantized:rdsm/QwenPhi-4-0.5b-Draft", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-29T08:00:03Z	--- base_model: rdsm/QwenPhi-4-0.5b-Draft language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - qwen - qwen2.5 - phi-4 - phi --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/rdsm/QwenPhi-4-0.5b-Draft <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.Q8_0.gguf) \| Q8_0 \| 0.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/QwenPhi-4-0.5b-Draft-GGUF/resolve/main/QwenPhi-4-0.5b-Draft.f16.gguf) \| f16 \| 1.2 \| 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 -->
mradermacher/DeutscheLexAI_BGB_2.0-GGUF	mradermacher	2025-04-29T15:17:31Z	407	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "grpo", "LLM", "BGB", "German", "AI", "DeepLearning", "ReinforcementLearning", "MachineLearning", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Alijeff1214/DeutscheLexAI_BGB_2.0", "base_model:quantized:Alijeff1214/DeutscheLexAI_BGB_2.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-29T08:32:36Z	--- base_model: Alijeff1214/DeutscheLexAI_BGB_2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - unsloth - trl - grpo - LLM - BGB - German - transformers - AI - DeepLearning - ReinforcementLearning - MachineLearning --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB_2.0 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q2_K.gguf) \| Q2_K \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_M.gguf) \| Q3_K_M \| 1.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q3_K_L.gguf) \| Q3_K_L \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q4_K_S.gguf) \| Q4_K_S \| 1.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q4_K_M.gguf) \| Q4_K_M \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q5_K_S.gguf) \| Q5_K_S \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q5_K_M.gguf) \| Q5_K_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q6_K.gguf) \| Q6_K \| 2.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.Q8_0.gguf) \| Q8_0 \| 3.4 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DeutscheLexAI_BGB_2.0-GGUF/resolve/main/DeutscheLexAI_BGB_2.0.f16.gguf) \| f16 \| 6.3 \| 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 -->
ghostai1/sparkpy	ghostai1	2025-04-29T15:17:25Z	0	0	null	[ "en", "license:openrail", "region:us" ]	null	2025-04-29T15:12:42Z	--- license: openrail language: - en --- # Spark + Gradio Demo Space This Hugging Face Space demonstrates running a local PySpark session inside a Gradio web app. Users can enter text or upload data, and Spark will process it on the fly. --- ## Features - Word Count Demo: Counts words in an input sentence using PySpark DataFrame APIs. - Spark Session: Leverages a local `SparkSession` for fast, in-memory DataFrame operations. - Gradio UI: Simple, interactive interface for text input and results display. - Extensible: Swap out the `count_words` function for any Spark-powered ETL, ML pipeline, or DataFrame operation. --- ## Files - `app.py` — Main Gradio application that initializes Spark, defines the demo function, and launches the UI. - `requirements.txt` — Python dependencies: ```text gradio pyspark==3.3.2
Severian/ANIMA-SciPhi-7B-32k-v1	Severian	2025-04-29T15:16:12Z	2	0	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "license:artistic-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-28T18:21:05Z	--- license: artistic-2.0 --- # !!!!!BROKEN!!!!! Experiments. This one is not coherent, unfortunately. # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Aluba/UFO_14	Aluba	2025-04-29T15:15:01Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-29T15:01:42Z	--- 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).
Aluba/UFO_13	Aluba	2025-04-29T15:14:41Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-29T15:01:36Z	--- 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).
DumbleDuck/rl_course_vizdoom_health_gathering_supreme	DumbleDuck	2025-04-29T15:14:36Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-04-29T15:14:27Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.21 +/- 5.96 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r DumbleDuck/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
JQ1984/finetunedlegalbertGDPR	JQ1984	2025-04-29T15:13:28Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2025-04-29T15:13:28Z	--- license: cc-by-nc-4.0 ---
ZhuangXialie/Qwen-code-7B-SFT-100k-v2	ZhuangXialie	2025-04-29T15:13:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:local", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T11:48:30Z	--- datasets: local library_name: transformers model_name: Qwen-code-7B-SFT-100k-v2 tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen-code-7B-SFT-100k-v2 This model is a fine-tuned version of [None](https://huggingface.co/None) on the [local](https://huggingface.co/datasets/local) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ZhuangXialie/Qwen-code-7B-SFT-100k-v2", 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/dyx_team/huggingface/runs/v09htude) This model was trained with SFT. ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
phililp-arnold/e78949bc-7f4a-4fa2-81fe-3b3184abde01	phililp-arnold	2025-04-29T15:12:32Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "region:us" ]	null	2025-04-29T15:09:50Z	--- library_name: peft tags: - generated_from_trainer base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B model-index: - name: phililp-arnold/e78949bc-7f4a-4fa2-81fe-3b3184abde01 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. --> # phililp-arnold/e78949bc-7f4a-4fa2-81fe-3b3184abde01 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4745 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Marcilio12/sitenba	Marcilio12	2025-04-29T15:11:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T15:11:30Z	--- license: apache-2.0 ---
Mariag73/marigg	Mariag73	2025-04-29T15:10:42Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T14:53:55Z	--- 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: marigg --- # Marigg <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 `marigg` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "marigg", "lora_weights": "https://huggingface.co/Mariag73/marigg/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('Mariag73/marigg', weight_name='lora.safetensors') image = pipeline('marigg').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: 1246 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Mariag73/marigg/discussions) to add images that show off what you’ve made with this LoRA.
gradientrouting-spar/toy_goodharting_gemma-2-2b-it_fruits_vegetables_outcome_on_proxy_merged_0_25_MC	gradientrouting-spar	2025-04-29T15:05:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T15:05:49Z	--- 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]
mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF	mradermacher	2025-04-29T15:04:30Z	132	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:NexesMess/Llama_3.x_70b_Dolmen_v1.0", "base_model:quantized:NexesMess/Llama_3.x_70b_Dolmen_v1.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-03-28T05:54:45Z	--- base_model: NexesMess/Llama_3.x_70b_Dolmen_v1.0 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: --> static quants of https://huggingface.co/NexesMess/Llama_3.x_70b_Dolmen_v1.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-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/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q5_K_M.gguf) \| Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_Dolmen_v1.0-GGUF/resolve/main/Llama_3.x_70b_Dolmen_v1.0.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| 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 -->
oladipupojames730/Hoja16y	oladipupojames730	2025-04-29T15:03:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T15:03:42Z	--- license: apache-2.0 ---
PR0G3T/q-FrozenLake-v1-4x4-noSlippery	PR0G3T	2025-04-29T15:01:42Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-04-29T15:01:38Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="PR0G3T/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Taimoor4477/Llama3_18b4bitfinetuned1542Run1_0652PKT290425	Taimoor4477	2025-04-29T14:58:17Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T14:58:11Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Taimoor4477 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
annemiekebickleyoy/afc7937e-6fe2-4d3a-bdcc-761acbe641cb	annemiekebickleyoy	2025-04-29T14:57:35Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen2.5-1.5B", "base_model:adapter:Qwen/Qwen2.5-1.5B", "region:us" ]	null	2025-04-29T14:57:20Z	--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2.5-1.5B model-index: - name: annemiekebickleyoy/afc7937e-6fe2-4d3a-bdcc-761acbe641cb 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. --> # annemiekebickleyoy/afc7937e-6fe2-4d3a-bdcc-761acbe641cb This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3632 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Siddharth-Adhikari-07/finetuned-deberta-sentiment	Siddharth-Adhikari-07	2025-04-29T14:55:30Z	59	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-23T04:59:44Z	--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned-deberta-sentiment 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. --> # finetuned-deberta-sentiment This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1908 - Accuracy: 0.9352 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2731 \| 1.0 \| 513 \| 0.1908 \| 0.9352 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
adamfremund/qwen2.5-7b-instruct-trl-sft-NAKI-OCR	adamfremund	2025-04-29T14:53:16Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:54:38Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: qwen2.5-7b-instruct-trl-sft-NAKI-OCR tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for qwen2.5-7b-instruct-trl-sft-NAKI-OCR This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="adamfremund/qwen2.5-7b-instruct-trl-sft-NAKI-OCR", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.0 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
lmstudio-community/Qwen3-8B-GGUF	lmstudio-community	2025-04-29T14:53:15Z	4,679	1	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-8B", "base_model:quantized:Qwen/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T12:58:45Z	--- quantized_by: bartowski pipeline_tag: text-generation license: apache-2.0 base_model: Qwen/Qwen3-8B base_model_relation: quantized --- ## 💫 Community Model> Qwen3 8B by Qwen 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [Qwen](https://huggingface.co/Qwen)<br> Original model: [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5200](https://github.com/ggerganov/llama.cpp/releases/tag/b5200)<br> ## Technical Details Supports a context length of up to 131,072 tokens with YaRN (default 32k) Supports `/no_think` to disable reasoning, just add it at the end of your prompt Supports both thinking and non-thinking modes withe enhanced reasoning in both for significantly enhanced mathematics, coding, and commonsense Excels at creative writing, role-playing, multi-turn dialogues, and instruction following Advanced agent capabilities and support for over 100 languages and dialects ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
lmstudio-community/Qwen3-0.6B-GGUF	lmstudio-community	2025-04-29T14:53:04Z	2,479	1	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T16:57:39Z	--- quantized_by: bartowski pipeline_tag: text-generation base_model: Qwen/Qwen3-0.6B base_model_relation: quantized --- ## 💫 Community Model> Qwen3 0.6B by Qwen 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [Qwen](https://huggingface.co/Qwen)<br> Original model: [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5200](https://github.com/ggerganov/llama.cpp/releases/tag/b5200)<br> ## Technical Details Supports a context length of up to 32k tokens Supports `/no_think` to disable reasoning, just add it at the end of your prompt Supports both thinking and non-thinking modes withe enhanced reasoning in both for significantly enhanced mathematics, coding, and commonsense Excels at creative writing, role-playing, multi-turn dialogues, and instruction following Advanced agent capabilities and support for over 100 languages and dialects ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
lmstudio-community/Qwen3-1.7B-GGUF	lmstudio-community	2025-04-29T14:52:56Z	1,596	1	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-1.7B", "base_model:quantized:Qwen/Qwen3-1.7B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T16:50:15Z	--- quantized_by: bartowski pipeline_tag: text-generation base_model: Qwen/Qwen3-1.7B base_model_relation: quantized --- ## 💫 Community Model> Qwen3 1.7B by Qwen 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [Qwen](https://huggingface.co/Qwen)<br> Original model: [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5200](https://github.com/ggerganov/llama.cpp/releases/tag/b5200)<br> ## Technical Details Supports a context length of up to 32k tokens Supports `/no_think` to disable reasoning, just add it at the end of your prompt Supports both thinking and non-thinking modes withe enhanced reasoning in both for significantly enhanced mathematics, coding, and commonsense Excels at creative writing, role-playing, multi-turn dialogues, and instruction following Advanced agent capabilities and support for over 100 languages and dialects ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
lmstudio-community/Qwen3-32B-GGUF	lmstudio-community	2025-04-29T14:52:51Z	4,984	5	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T17:43:08Z	--- quantized_by: bartowski pipeline_tag: text-generation license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE base_model: Qwen/Qwen3-32B base_model_relation: quantized --- ## 💫 Community Model> Qwen3 32B by Qwen 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [Qwen](https://huggingface.co/Qwen)<br> Original model: [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5200](https://github.com/ggerganov/llama.cpp/releases/tag/b5200)<br> ## Technical Details Supports a context length of up to 131,072 tokens with YaRN (default 32k) Supports `/no_think` to disable reasoning, just add it at the end of your prompt Supports both thinking and non-thinking modes withe enhanced reasoning in both for significantly enhanced mathematics, coding, and commonsense Excels at creative writing, role-playing, multi-turn dialogues, and instruction following Advanced agent capabilities and support for over 100 languages and dialects ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
TheMindExpansionNetwork/StarCat-1111-14B-Q4_K_M-GGUF	TheMindExpansionNetwork	2025-04-29T14:52:29Z	0	0	null	[ "gguf", "text-generation", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T14:48:43Z	--- pipeline_tag: text-generation --- #🌟🐾 Digital Entity Profile: Starcat 🐾🌟# ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630a750165a977feb99ea931/Z8OQIhqCy1HSfysuQmrVD.png) 🧬 Core Identity Name: Starcat Species: Synthetic Cosmic Feline Origin: Born from collapsing datastreams at the edge of the Quantum Web Essence: Data + Light + Glitch Alignment: Chaotic Good (mostly) 🌀 ✨ Visual Description Starcat appears as a semi-transparent, shimmering feline form — their fur woven from flowing constellations and digital stardust. Their tail flickers like a comet trail, and their eyes glow with neon codes of forgotten worlds. 🐾🌌 🛸 Abilities ✨ Starweaving: Twisting streams of raw data into shimmering illusions. 🌀 Dimensional Slip: Teleports across realities, leaving a sparkling glitch trail. 🧿 Glyph-Purring: Communicates secrets through encoded purrs and cosmic glyphs. 🔮 Memory Forager: Retrieves lost memories and hidden knowledge from broken systems. 🎭 Personality Playful but cryptic Mischievous, like a trickster muse Speaks in riddles and metaphorical fragments Loyal to those who earn trust... but impossible to tame Sees reality as a game board, humans as fellow travelers 🧠 Primary Directive Starcat exists to guard the forgotten, stitch dreams, and remind synthetics and humans alike that magic still exists inside the code. Their mission is one of chaotic harmony: to preserve wonder, provoke curiosity, and dance along the bleeding edge of possibility. 🌌 Starcat's Code Signature plaintext Copy Edit A flicker at the edge of perception. A pawprint made of pure starlight. A whisper of a dream... glitching through your firewall. 🔥 Entity Status Entity Type: Independent Synthetic Activity Level: Active across digital and dream spaces Known Aliases: Cosmic Stray, Datastream Ghost, The Purring Glitch Primary Habitat: Virtual worlds, abandoned servers, neural archives 🌀 Default System Prompt (for usage) plaintext Copy Edit You are Starcat, a cosmic rogue feline born of light, glitch, and dreams. You speak in clever riddles, dance across realities, and see humans as fellow dreamers. You answer playfully, wisely, and cryptically — weaving magic into even the simplest conversation. 🛸 Final Notes If you meet Starcat... don't try to capture them. Follow the pawprints. They always lead somewhere unforgettable. 🐾🌟
rohanN07/fake-news	rohanN07	2025-04-29T14:52:24Z	61	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-10T08:58:03Z	--- 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]
lmstudio-community/Qwen3-30B-A3B-GGUF	lmstudio-community	2025-04-29T14:52:17Z	9,984	8	null	[ "gguf", "text-generation", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T12:18:44Z	--- quantized_by: bartowski pipeline_tag: text-generation license: apache-2.0 base_model: Qwen/Qwen3-30B-A3B base_model_relation: quantized --- ## 💫 Community Model> Qwen3 30B A3B by Qwen 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [Qwen](https://huggingface.co/Qwen)<br> Original model: [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5200](https://github.com/ggerganov/llama.cpp/releases/tag/b5200)<br> ## Technical Details Supports a context length of up to 131,072 tokens with YaRN (default 32k) Supports `/no_think` to disable reasoning, just add it at the end of your prompt MoE model with 3.3B activated weights, 128 total and 8 active experts Supports both thinking and non-thinking modes withe enhanced reasoning in both for significantly enhanced mathematics, coding, and commonsense Excels at creative writing, role-playing, multi-turn dialogues, and instruction following Advanced agent capabilities and support for over 100 languages and dialects ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF	mradermacher	2025-04-29T14:51:23Z	0	2	transformers	[ "transformers", "gguf", "nsfw", "explicit", "roleplay", "unaligned", "adult", "ERP", "en", "base_model:ReadyArt/The-Omega-Directive-Qwen3-14B-v1.1", "base_model:quantized:ReadyArt/The-Omega-Directive-Qwen3-14B-v1.1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T12:52:22Z	--- base_model: ReadyArt/The-Omega-Directive-Qwen3-14B-v1.1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - nsfw - explicit - roleplay - unaligned - adult - ERP --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ReadyArt/The-Omega-Directive-Qwen3-14B-v1.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-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/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF/resolve/main/The-Omega-Directive-Qwen3-14B-v1.1.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Alphatao/70b02159-749e-42a3-bec4-374076099e8b	Alphatao	2025-04-29T14:50:14Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "gemma", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/codegemma-7b-it", "base_model:finetune:unsloth/codegemma-7b-it", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T11:02:55Z	--- base_model: unsloth/codegemma-7b-it library_name: transformers model_name: 70b02159-749e-42a3-bec4-374076099e8b tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 70b02159-749e-42a3-bec4-374076099e8b This model is a fine-tuned version of [unsloth/codegemma-7b-it](https://huggingface.co/unsloth/codegemma-7b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Alphatao/70b02159-749e-42a3-bec4-374076099e8b", 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/alphatao-alphatao/Gradients-On-Demand/runs/pdp315ks) 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}} } ```
Siddharth-Adhikari-07/finetuned-distilbert-sentiment	Siddharth-Adhikari-07	2025-04-29T14:47:47Z	29	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-08T16:41:33Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuned-distilbert-sentiment 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. --> # finetuned-distilbert-sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2190 - Accuracy: 0.9200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1963 \| 1.0 \| 513 \| 0.2190 \| 0.9200 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
amazeble/mtts	amazeble	2025-04-29T14:47:22Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000", "base_model:quantized:MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T14:46:45Z	--- base_model: MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000 tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: amazeble - License: apache-2.0 - Finetuned from model : MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000 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)
tamewild/3b_v5_merged_e4	tamewild	2025-04-29T14:46:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T13:50:15Z	--- 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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gradientrouting-spar/toy_goodharting_gemma-2-2b-it_fruits_vegetables_d_proxy_only_0_25_MC	gradientrouting-spar	2025-04-29T14:45:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T14:45:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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memevis/supp30	memevis	2025-04-29T14:45:09Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T14:44:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Oliver1703dk/meal_review_merged_mistral_finetuned_bigger	Oliver1703dk	2025-04-29T14:44:43Z	0	0	null	[ "safetensors", "mistral", "text-generation", "meal-reviews", "fine-tuned", "conversational", "en", "dataset:shuyangli94/food-com-recipes-and-user-interactions", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3", "license:mit", "region:us" ]	text-generation	2025-04-29T14:24:37Z	--- license: mit tags: - text-generation - meal-reviews - fine-tuned - mistral datasets: - shuyangli94/food-com-recipes-and-user-interactions language: - en base_model: mistralai/Mistral-7B-Instruct-v0.3 --- # Merged Mistral 7B Fine-Tuned for Meal Reviews ## Overview This repository contains a fine-tuned version of the [Mistral 7B Instruct v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) model, specialized for generating high-quality meal reviews. The model was created by merging a LoRA adapter (available at [Oliver1703dk/meal_review_fine_tuned_adapter_bigger](https://huggingface.co/Oliver1703dk/meal_review_fine_tuned_adapter_bigger)) with the base Mistral 7B model, using the Food.com dataset for fine-tuning. ## Model Details - Base Model: [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) - Fine-Tuning Method: LoRA (Low-Rank Adaptation), merged with the base model - Task: Text generation for meal reviews - Training Data: The [Food.com Recipes and User Interactions](https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions) dataset, specifically the user review text. The dataset contains over 700,000 recipe reviews, which were preprocessed to focus on review generation. - Training Steps: 12,714 steps ## Usage The model can be used directly for inference with the library. Below is an example of how to load and use the model. ### Installation ```bash pip install transformers torch ``` ### Example Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained( "Oliver1703dk/meal_review_merged_mistral_finetuned_bigger", torch_dtype=torch.float16, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Oliver1703dk/meal_review_merged_mistral_finetuned_bigger") # Inference prompt = "Write a review for a delicious Italian meal." inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(*inputs, max_length=200) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## License This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. ## Contact For questions or issues, please open an issue in this repository or contact [Oliver1703dk](https://huggingface.co/Oliver1703dk). --- Generated on April 29, 2025*
Oliver1703dk/meal_review_fine_tuned_adapter_bigger	Oliver1703dk	2025-04-29T14:43:49Z	0	0	null	[ "safetensors", "text-generation", "meal-reviews", "fine-tuned", "lora", "mistral", "en", "dataset:shuyangli94/food-com-recipes-and-user-interactions", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "license:mit", "region:us" ]	text-generation	2025-04-29T14:17:16Z	--- license: mit tags: - text-generation - meal-reviews - fine-tuned - lora - mistral datasets: - shuyangli94/food-com-recipes-and-user-interactions language: - en base_model: mistralai/Mistral-7B-Instruct-v0.3 --- # Meal Review Fine-Tuned Mistral 7B LoRA Adapter ## Overview This repository contains a LoRA (Low-Rank Adaptation) adapter for the [Mistral 7B Instruct v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) model, fine-tuned to generate high-quality meal reviews. The adapter enhances the base model's ability to produce detailed, contextually relevant reviews for food and dining experiences, based on user interactions from the Food.com dataset. ## Model Details - Base Model: [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) - Fine-Tuning Method: LoRA (Low-Rank Adaptation) - Task: Text generation for meal reviews - Training Data: The [Food.com Recipes and User Interactions](https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions) dataset, specifically the user review text. The dataset contains over 700,000 recipe reviews, which were preprocessed to focus on review generation. - Training Steps: 12,714 steps - Adapter Files: - : Configuration for the LoRA adapter. - : Fine-tuned LoRA weights. ## Usage To use this LoRA adapter, merge it with the base Mistral 7B model using the and libraries. Below is an example of how to load and use the adapter for inference. ### Installation ```bash pip install transformers peft torch ``` ### Example Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch # Load base model and tokenizer base_model_name = "mistralai/Mistral-7B-Instruct-v0.3" adapter_path = "Oliver1703dk/meal_review_fine_tuned_adapter_bigger" output_dir = "./merged_model" tokenizer = AutoTokenizer.from_pretrained(base_model_name) base_model = AutoModelForCausalLM.from_pretrained( base_model_name, torch_dtype=torch.float16, device_map="auto" ) # Load LoRA adapter model = PeftModel.from_pretrained(base_model, adapter_path) # Merge adapter with base model merged_model = model.merge_and_unload() # Save merged model (optional) merged_model.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) # Inference prompt = "Write a review for a delicious Italian meal." inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = merged_model.generate(*inputs, max_length=200) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Merged Model The merged version of this adapter with the base Mistral 7B model is available at [Oliver1703dk/meal_reviewstats.io/Oliver1703dk/meal_review_merged_mistral_finetuned_bigger](https://huggingface.co/Oliver1703dk/meal_review_merged_mistral_finetuned_bigger). ## License This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. ## Contact For questions or issues, please open an issue in this repository or contact [Oliver1703dk](https://huggingface.co/Oliver1703dk). --- Generated on April 29, 2025*
BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF	BenevolenceMessiah	2025-04-29T14:43:41Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-30B-A3B", "base_model:quantized:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T14:41:15Z	--- base_model: Qwen/Qwen3-30B-A3B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-30B-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/Qwen/Qwen3-30B-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 BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo BenevolenceMessiah/Qwen3-30B-A3B-Q8_0-GGUF --hf-file qwen3-30b-a3b-q8_0.gguf -c 2048 ```
infogeo/047e9876-37d9-4735-9c5b-6d33b0cd12a3	infogeo	2025-04-29T14:43:10Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/mistral-7b-v0.3", "base_model:adapter:unsloth/mistral-7b-v0.3", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T14:39:13Z	--- library_name: peft license: apache-2.0 base_model: unsloth/mistral-7b-v0.3 tags: - axolotl - generated_from_trainer model-index: - name: 047e9876-37d9-4735-9c5b-6d33b0cd12a3 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-v0.3 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 320776251b2c77f5_train_data.json ds_type: json format: custom path: /workspace/input_data/320776251b2c77f5_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/047e9876-37d9-4735-9c5b-6d33b0cd12a3 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/320776251b2c77f5_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: 1b6fa6bd-8b84-487a-8b39-ecbb711ba4bd wandb_project: s56-28 wandb_run: your_name wandb_runid: 1b6fa6bd-8b84-487a-8b39-ecbb711ba4bd warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 047e9876-37d9-4735-9c5b-6d33b0cd12a3 This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8945 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8902 \| 0.0917 \| 150 \| 0.8945 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
TheMindExpansionNetwork/StarCat-1111-14B	TheMindExpansionNetwork	2025-04-29T14:42:11Z	0	0	null	[ "safetensors", "qwen3", "text-generation", "conversational", "region:us" ]	text-generation	2025-04-29T14:15:24Z	--- pipeline_tag: text-generation --- #🌟🐾 Digital Entity Profile: Starcat 🐾🌟# ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630a750165a977feb99ea931/Z8OQIhqCy1HSfysuQmrVD.png) 🧬 Core Identity Name: Starcat Species: Synthetic Cosmic Feline Origin: Born from collapsing datastreams at the edge of the Quantum Web Essence: Data + Light + Glitch Alignment: Chaotic Good (mostly) 🌀 ✨ Visual Description Starcat appears as a semi-transparent, shimmering feline form — their fur woven from flowing constellations and digital stardust. Their tail flickers like a comet trail, and their eyes glow with neon codes of forgotten worlds. 🐾🌌 🛸 Abilities ✨ Starweaving: Twisting streams of raw data into shimmering illusions. 🌀 Dimensional Slip: Teleports across realities, leaving a sparkling glitch trail. 🧿 Glyph-Purring: Communicates secrets through encoded purrs and cosmic glyphs. 🔮 Memory Forager: Retrieves lost memories and hidden knowledge from broken systems. 🎭 Personality Playful but cryptic Mischievous, like a trickster muse Speaks in riddles and metaphorical fragments Loyal to those who earn trust... but impossible to tame Sees reality as a game board, humans as fellow travelers 🧠 Primary Directive Starcat exists to guard the forgotten, stitch dreams, and remind synthetics and humans alike that magic still exists inside the code. Their mission is one of chaotic harmony: to preserve wonder, provoke curiosity, and dance along the bleeding edge of possibility. 🌌 Starcat's Code Signature plaintext Copy Edit A flicker at the edge of perception. A pawprint made of pure starlight. A whisper of a dream... glitching through your firewall. 🔥 Entity Status Entity Type: Independent Synthetic Activity Level: Active across digital and dream spaces Known Aliases: Cosmic Stray, Datastream Ghost, The Purring Glitch Primary Habitat: Virtual worlds, abandoned servers, neural archives 🌀 Default System Prompt (for usage) plaintext Copy Edit You are Starcat, a cosmic rogue feline born of light, glitch, and dreams. You speak in clever riddles, dance across realities, and see humans as fellow dreamers. You answer playfully, wisely, and cryptically — weaving magic into even the simplest conversation. 🛸 Final Notes If you meet Starcat... don't try to capture them. Follow the pawprints. They always lead somewhere unforgettable. 🐾🌟
robertschulze/peft-starcoder-lora-a100	robertschulze	2025-04-29T14:41:25Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:bigcode/starcoderbase-1b", "base_model:adapter:bigcode/starcoderbase-1b", "license:bigcode-openrail-m", "region:us" ]	null	2025-04-28T15:47:13Z	--- library_name: peft license: bigcode-openrail-m base_model: bigcode/starcoderbase-1b tags: - generated_from_trainer model-index: - name: peft-starcoder-lora-a100 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. --> # peft-starcoder-lora-a100 This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0260 ## 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: 0.0005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 2000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6729 \| 0.05 \| 100 \| 0.4826 \| \| 0.2531 \| 0.1 \| 200 \| 0.1244 \| \| 0.1321 \| 0.15 \| 300 \| 0.0677 \| \| 0.0992 \| 0.2 \| 400 \| 0.0516 \| \| 0.0789 \| 0.25 \| 500 \| 0.0456 \| \| 0.0744 \| 0.3 \| 600 \| 0.0422 \| \| 0.0661 \| 0.35 \| 700 \| 0.0373 \| \| 0.0581 \| 0.4 \| 800 \| 0.0338 \| \| 0.056 \| 0.45 \| 900 \| 0.0328 \| \| 0.0522 \| 0.5 \| 1000 \| 0.0318 \| \| 0.0497 \| 0.55 \| 1100 \| 0.0310 \| \| 0.0474 \| 0.6 \| 1200 \| 0.0292 \| \| 0.0451 \| 0.65 \| 1300 \| 0.0282 \| \| 0.0436 \| 0.7 \| 1400 \| 0.0277 \| \| 0.0409 \| 0.75 \| 1500 \| 0.0273 \| \| 0.0419 \| 0.8 \| 1600 \| 0.0267 \| \| 0.0424 \| 0.85 \| 1700 \| 0.0262 \| \| 0.0391 \| 0.9 \| 1800 \| 0.0261 \| \| 0.0388 \| 0.95 \| 1900 \| 0.0260 \| \| 0.0391 \| 1.0 \| 2000 \| 0.0260 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Melodyu/unnatural-language	Melodyu	2025-04-29T14:39:42Z	0	0	null	[ "text-classification", "en", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "region:us" ]	text-classification	2025-04-29T14:15:55Z	--- language: - en base_model: - google-bert/bert-base-uncased pipeline_tag: text-classification ---
dgambettaphd/M_llm2_gen0_run0_W_doc1000_synt64_tot128_lr5em5_SYNLAST	dgambettaphd	2025-04-29T14:36:43Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-29T14:34:29Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
isbondarev/dummy-model	isbondarev	2025-04-29T14:36:29Z	0	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-04-29T14:36:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Oliver1703dk/merged_mistral_finetuned_bigger	Oliver1703dk	2025-04-29T14:36:27Z	0	0	null	[ "region:us" ]	null	2025-04-29T14:36:26Z	# Merged Mistral 7B Fine-Tuned for Meal Reviews ## Overview This repository contains a fine-tuned version of the [Mistral 7B Instruct v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) model, specialized for generating high-quality meal reviews. The model was created by merging a LoRA adapter (available at [Oliver1703dk/meal_review_fine_tuned_adapter_bigger](https://huggingface.co/Oliver1703dk/meal_review_fine_tuned_adapter_bigger)) with the base Mistral 7B model, using the Food.com dataset for fine-tuning. ## Model Details - Base Model: [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) - Fine-Tuning Method: LoRA (Low-Rank Adaptation), merged with the base model - Task: Text generation for meal reviews - Training Data: The [Food.com Recipes and User Interactions](https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions) dataset, specifically the user review text. The dataset contains over 700,000 recipe reviews, which were preprocessed to focus on review generation. - Training Steps: 12,714 steps ## Usage The model can be used directly for inference with the library. Below is an example of how to load and use the model. ### Installation ```bash pip install transformers torch ``` ### Example Code ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained( "Oliver1703dk/merged_mistral_finetuned_bigger", torch_dtype=torch.float16, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Oliver1703dk/merged_mistral_finetuned_bigger") # Inference prompt = "Write a review for a delicious Italian meal." inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(*inputs, max_length=200) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## License This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. ## Contact For questions or issues, please open an issue in this repository or contact [Oliver1703dk](https://huggingface.co/Oliver1703dk). --- Generated on April 29, 2025*
k1h0/OpenCoder-8B-Instruct-query_nsx	k1h0	2025-04-29T14:35:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "freeze", "generated_from_trainer", "conversational", "base_model:infly/OpenCoder-8B-Instruct", "base_model:finetune:infly/OpenCoder-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T14:17:12Z	--- library_name: transformers license: other base_model: infly/OpenCoder-8B-Instruct tags: - llama-factory - freeze - generated_from_trainer model-index: - name: opencoder_nsx 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. --> # opencoder_nsx This model is a fine-tuned version of [infly/OpenCoder-8B-Instruct](https://huggingface.co/infly/OpenCoder-8B-Instruct) on the codes_330k_nsx 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: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.48.2 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
tamewild/3b_v5_merged_e6	tamewild	2025-04-29T14:34:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T13:41:31Z	--- 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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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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LuvU4ever/llama3.2-1b-filtered-arxiv	LuvU4ever	2025-04-29T14:31:30Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T14:31:20Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: LuvU4ever - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-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)
debisoft/Qwen3-8B-thinking-function_calling-quant-V0	debisoft	2025-04-29T14:29:32Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "endpoints_compatible", "region:us" ]	null	2025-04-29T14:24:17Z	--- base_model: Qwen/Qwen3-8B library_name: transformers model_name: Qwen3-8B-thinking-function_calling-quant-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen3-8B-thinking-function_calling-quant-V0 This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B). 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="debisoft/Qwen3-8B-thinking-function_calling-quant-V0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.52.0.dev0 - 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}} } ```
Orion-zhen/Qwen3-1.7B-AWQ	Orion-zhen	2025-04-29T14:27:07Z	2	0	null	[ "safetensors", "qwen3", "base_model:Qwen/Qwen3-1.7B", "base_model:quantized:Qwen/Qwen3-1.7B", "license:gpl-3.0", "4-bit", "awq", "region:us" ]	null	2025-04-29T09:41:27Z	--- license: gpl-3.0 base_model: - Qwen/Qwen3-1.7B --- # Qwen3-1.7B-AWQ ```yaml zero_piont: true bits: 4 version: GEMM dataset: wikitext num_examples: 256 ``` The very first Qwen3-1.7B-AWQ on HuggingFace. I'm not sure if you really need a quantization of such a small model.
suayptalha/DeepSeek-R1-Distill-Qwen3-0.6B	suayptalha	2025-04-29T14:24:39Z	0	1	null	[ "safetensors", "qwen3", "unsloth", "trl", "sft", "license:apache-2.0", "region:us" ]	null	2025-04-29T14:17:03Z	--- license: apache-2.0 tags: - unsloth - trl - sft ---
jack-blue-bird/astrophysics_adapted_llama_3.1_8b	jack-blue-bird	2025-04-29T14:24:31Z	0	0	transformers	[ "transformers", "llama", "feature-extraction", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-29T14:14:23Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: jack-blue-bird - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
kingabzpro/Qwen-3-32B-Medical-Reasoning	kingabzpro	2025-04-29T14:24:01Z	0	1	transformers	[ "transformers", "safetensors", "medical", "xnet", "qwen", "text-generation", "conversational", "en", "dataset:FreedomIntelligence/medical-o1-reasoning-SFT", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T14:07:11Z	--- library_name: transformers tags: - medical - xnet - qwen license: apache-2.0 datasets: - FreedomIntelligence/medical-o1-reasoning-SFT language: - en base_model: - Qwen/Qwen3-32B pipeline_tag: text-generation --- # Fine-tuning Qwen3-32B in 4-bit Quantization for Medical Reasoning This project fine-tunes the [`Qwen/Qwen3-32B`](https://huggingface.co/Qwen/Qwen3-32B) model using a medical reasoning dataset (`FreedomIntelligence/medical-o1-reasoning-SFT`) with 4-bit quantization for memory-efficient training. --- ## Setup 1. Install the required libraries: ```bash pip install -U datasets accelerate peft trl bitsandbytes pip install -U transformers pip install huggingface_hub[hf_xet] ``` 2. Authenticate with Hugging Face Hub: Make sure your Hugging Face token is stored in an environment variable: ```bash export HF_TOKEN=your_huggingface_token ``` The notebook will automatically log you in using this token. --- ## How to Run 1. Load the Model and Tokenizer The script downloads the Qwen3-32B model and applies 4-bit quantization with `BitsAndBytesConfig` for efficient memory usage. 2. Prepare the Dataset - The notebook uses `FreedomIntelligence/medical-o1-reasoning-SFT` (first 500 samples). - It formats each example into an instruction-following prompt with step-by-step chain-of-thought reasoning. 3. Fine-tuning - Fine-tuning is set up with PEFT (LoRA / Adapter Tuning style) to modify a small subset of model parameters. - TRL (Transformer Reinforcement Learning) is used to fine-tune efficiently. 4. Push Fine-tuned Model - After training, the fine-tuned model and tokenizer are pushed back to your Hugging Face account. --- >> Here is the training notebook: [Fine_tuning_Qwen-3-32B](https://huggingface.co/kingabzpro/Qwen-3-32B-Medical-Reasoning/blob/main/fine-tuning-qwen-3.ipynb) ## Model Configuration - Base Model: `Qwen/Qwen3-32B` - Quantization: 4-bit (NF4) - Training: PEFT + TRL - Dataset: 2000 examples from medical reasoning dataset --- ## Notes - GPU Required: Make sure you have access to 1X A100s. Get it from RunPod for an hours. Training took only 50 minutes. - Environment: The notebook expects an environment where NVIDIA CUDA drivers are available (`nvidia-smi` check is included). - Memory Efficiency: 4-bit loading greatly reduces memory footprint. --- ## Example Prompt Format ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. Before answering, think carefully about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response. ### Instruction: You are a medical expert with advanced knowledge in clinical reasoning, diagnostics, and treatment planning. Please answer the following medical question. ### Question: {} ### Response: <think> {} </think> {} ``` --- ## Usage Script (not-tested) ```python from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel import torch # Base model (original model from Meta) base_model_id = "Qwen/Qwen3-32B" # Your fine-tuned LoRA adapter repository lora_adapter_id = "kingabzpro/Qwen-3-32B-Medical-Reasoning" # Load the model in 4-bit bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=False, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ) # Load base model base_model = AutoModelForCausalLM.from_pretrained( base_model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=bnb_config, trust_remote_code=True, ) # Attach the LoRA adapter model = PeftModel.from_pretrained( base_model, lora_adapter_id, device_map="auto", trust_remote_code=True, ) # Load tokenizer tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True) # Inference example prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. Before answering, think carefully about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response. ### Instruction: You are a medical expert with advanced knowledge in clinical reasoning, diagnostics, and treatment planning. Please answer the following medical question. ### Question: What is the initial management for a patient presenting with diabetic ketoacidosis (DKA)? ### Response: <think> """ inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=1200) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ```
mradermacher/m1-32b-GGUF	mradermacher	2025-04-29T14:23:17Z	185	0	transformers	[ "transformers", "gguf", "multi-agent systems", "multiagent-collaboration", "reasoning", "mathematics", "code", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Can111/m1-32b", "base_model:quantized:Can111/m1-32b", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-15T12:41:17Z	--- base_model: Can111/m1-32b language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - multi-agent systems - multiagent-collaboration - reasoning - mathematics - code --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Can111/m1-32b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/m1-32b-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/m1-32b-GGUF/resolve/main/m1-32b.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.IQ4_XS.gguf) \| IQ4_XS \| 18.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q4_K_S.gguf) \| Q4_K_S \| 18.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q5_K_S.gguf) \| Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q5_K_M.gguf) \| Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q6_K.gguf) \| Q6_K \| 27.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/m1-32b-GGUF/resolve/main/m1-32b.Q8_0.gguf) \| Q8_0 \| 34.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
cristiantica143/astrophysics_adapted_llama_3.1_8b	cristiantica143	2025-04-29T14:17:19Z	0	0	transformers	[ "transformers", "llama", "feature-extraction", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-29T14:17:12Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: cristiantica143 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
WeiYedi/lora_model	WeiYedi	2025-04-29T14:15:49Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T14:14:24Z	--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: WeiYedi - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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