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train · 839k rows

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
asm3515/merged-llama3-agnews-lora	asm3515	2025-04-28T15:26:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-04-28T15:14:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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gaianet/Qwen2.5-3B-Instruct-GGUF	gaianet	2025-04-28T15:25:22Z	840	2	null	[ "gguf", "qwen2", "chat", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-3B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-19T02:32:16Z	--- base_model: Qwen/Qwen2.5-3B-Instruct license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSEE model_creator: Qwen model_name: QQwen2.5-3B-Instruct quantized_by: Second State Inc. language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation tags: - chat --- # Qwen2.5-3B-Instruct-GGUF ## Original Model [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) ## Run with Gaianet Prompt template prompt template: `chatml` Context size chat_ctx_size: `32000` Run with GaiaNet - Quick start: https://docs.gaianet.ai/node-guide/quick-start - Customize your node: https://docs.gaianet.ai/node-guide/customize
Triangle104/QwQ-32B-ArliAI-RpR-v3-Q5_K_M-GGUF	Triangle104	2025-04-28T15:24:08Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v3", "base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T15:21:04Z	--- base_model: ArliAI/QwQ-32B-ArliAI-RpR-v3 language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo thumbnail: https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/coilCTGeL0OUYr9PA9zna.jpeg --- # Triangle104/QwQ-32B-ArliAI-RpR-v3-Q5_K_M-GGUF This model was converted to GGUF format from [`ArliAI/QwQ-32B-ArliAI-RpR-v3`](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3) 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/ArliAI/QwQ-32B-ArliAI-RpR-v3) for more details on the model. --- RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series. RpR models use the same curated, deduplicated RP and creative writing dataset used for RPMax, with a focus on variety to ensure high creativity and minimize cross-context repetition. Users familiar with RPMax will recognize the unique, non-repetitive writing style unlike other finetuned-for-RP models. With the release of QwQ as the first high performing open-source reasoning model that can be easily trained, it was clear that the available instruct and creative writing reasoning datasets contains only one response per example. This is type of single response dataset used for training reasoning models causes degraded output quality in long multi-turn chats. Which is why Arli AI decided to create a real RP model capable of long multi-turn chat with reasoning. In order to create RpR, we first had to actually create the reasoning RP dataset by re-processing our existing known-good RPMax dataset into a reasoning dataset. This was possible by using the base QwQ Instruct model itself to create the reasoning process for every turn in the RPMax dataset conversation examples, which is then further refined in order to make sure the reasoning is in-line with the actual response examples from the dataset. Another important thing to get right is to make sure the model is trained on examples that present reasoning blocks in the same way as it encounters it during inference. Which is, never seeing the reasoning blocks in it's context. In order to do this, the training run was completed using axolotl with manual template-free segments dataset in order to make sure that the model is never trained to see the reasoning block in the context. Just like how the model will be used during inference time. The result of training QwQ on this dataset with this method are consistently coherent and interesting outputs even in long multi-turn RP chats. This is as far as we know the first true correctly-trained reasoning model trained for RP and creative writing. You can access the model at https://arliai.com and we also have a models ranking page at https://www.arliai.com/models-ranking Ask questions in our new Discord Server https://discord.com/invite/t75KbPgwhk or on our subreddit https://www.reddit.com/r/ArliAI/ --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q5_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v3-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q5_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v3-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q5_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v3-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q5_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v3-q5_k_m.gguf -c 2048 ```
fhaslam/Llama-3.2-1B-Financial-Sentiment21	fhaslam	2025-04-28T15:23:50Z	0	0	transformers	[ "transformers", "safetensors", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T15:23:28Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. 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Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
llamafy/Qwen-Qwen2.5-0.5B-Instruct-llamafied	llamafy	2025-04-28T15:23:17Z	53	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-16T22:12:30Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct pipeline_tag: text-generation library_name: transformers language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Qwen/Qwen2.5-0.5B-Instruct (llamafied) This is a version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) converted to the Llama format. It should be compatible with all programs that support Llama.
ESITime/SFT-1-Final-1.5B	ESITime	2025-04-28T15:22:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T15:20:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ESITime/SFT-1-Final-0.5B	ESITime	2025-04-28T15:18:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T15:18:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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tanthinhdt/Cytotoxicity-Nanoparticles_Llamma-3.1_20250428-152327	tanthinhdt	2025-04-28T15:16:37Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T08:23:31Z	--- 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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deswaq/juh91	deswaq	2025-04-28T15:15:35Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T15:12:09Z	--- 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. 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PriyankAnantha/my-finetuned-torgo-model	PriyankAnantha	2025-04-28T15:11:44Z	0	0	null	[ "tensorboard", "safetensors", "wav2vec2", "generated_from_trainer", "base_model:jmaczan/wav2vec2-large-xls-r-300m-dysarthria", "base_model:finetune:jmaczan/wav2vec2-large-xls-r-300m-dysarthria", "license:apache-2.0", "region:us" ]	null	2025-04-28T13:03:03Z	--- license: apache-2.0 base_model: jmaczan/wav2vec2-large-xls-r-300m-dysarthria tags: - generated_from_trainer metrics: - wer model-index: - name: my-finetuned-torgo-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my-finetuned-torgo-model This model is a fine-tuned version of [jmaczan/wav2vec2-large-xls-r-300m-dysarthria](https://huggingface.co/jmaczan/wav2vec2-large-xls-r-300m-dysarthria) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.0369 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.9149 \| 0.2 \| 50 \| 4.3421 \| 1.0 \| \| 3.4989 \| 0.4 \| 100 \| 4.2367 \| 0.9980 \| \| 3.3711 \| 0.6 \| 150 \| 3.8820 \| 1.0 \| \| 3.3513 \| 0.8 \| 200 \| 4.0342 \| 1.0 \| \| 3.2224 \| 1.0 \| 250 \| 4.0369 \| 1.0 \| ### Framework versions - Transformers 4.43.2 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.19.1
radm/forerunner-qwen7b-orpo	radm	2025-04-28T15:10:35Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "orpo", "unsloth", "philosophical", "esoteric", "multilingual", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T12:17:27Z	--- library_name: transformers tags: - qwen2 - orpo - unsloth - philosophical - esoteric - multilingual base_model: - Qwen/Qwen2.5-7B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Model Card for forerunner-qwen7b-orpo <img src="https://huggingface.co/radm/forerunner-qwen7b-orpo/resolve/main/model-image.png" alt="Model Image" width="100%"> ## Model Details ### Model Description This model is a fine-tuned version of `Qwen/Qwen2.5-7B-Instruct` using the ORPO algorithm with Unsloth for optimization. It has been trained on a custom dataset with a focus on philosophical and esoteric topics. The model is multilingual. - Developed by: radm - Finetuned from model: `Qwen/Qwen2.5-7B-Instruct` - Model type: Causal LM based on Qwen2 architecture - Language(s): Multilingual - License: Apache 2.0 (inherited from base model) ## Uses Example Output (with temperature=0.6, repetition_penalty=1.15): User: As a result of what mental process does duality appear in a person? Model: Dualistic thinking arises from the interaction between an individual and their environment. When a person's consciousness is not yet developed to a certain level, they experience the world through sensory perception. This perception leads to the formation of concepts such as "good" and "bad," "right" and "wrong." These oppositions manifest themselves in actions or emotions, leading to dualism. The mind then begins to differentiate between these opposites, creating a division into two parts: one that experiences and another that observes this experience. This observation is necessary for the development of language and thought processes, which are based on distinguishing and naming objects according to their properties. As a result, the mind learns to distinguish between things, people, events, and their qualities, forming a complex system of relationships. This process is similar to how a child learns to recognize and name objects around them. However, it's important to note that the duality formed within the mind does not necessarily reflect external reality but rather is a product of internal cognitive processes. ### Out-of-Scope Use The model is not designed for generating harmful, unethical, biased, or factually incorrect content. Performance on tasks outside its training domain (philosophical/esoteric chat) may be suboptimal. ## Bias, Risks, and Limitations The model inherits biases from its base model (`Qwen/Qwen2.5-7B-Instruct`) and the fine-tuning dataset. It may generate plausible-sounding but incorrect or nonsensical information, especially on complex topics. Its understanding is based on patterns in the data, not genuine comprehension or consciousness. Use the outputs with critical judgment. ## Training Details ### Training Data The model was fine-tuned using the ORPO algorithm on a custom dataset. This dataset contains pairs of preferred and rejected responses for given prompts, focusing on philosophical and esoteric themes. A small validation split (0.5%) was used. ### Training Procedure The training was performed using the `Unsloth` library integrated with `trl`'s `ORPOTrainer`. #### Training Hyperparameters - Framework: Unsloth + ORPO - Base Model: `Qwen/Qwen2.5-7B-Instruct` - LoRA Configuration: - `r`: 64 - `lora_alpha`: 64 - `lora_dropout`: 0.0 - `bias`: "none" - `target_modules`: ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"] - `use_rslora`: True - Precision: bfloat16 mixed precision - Quantization: Loaded in 4-bit - Optimizer: Paged AdamW 8-bit - Learning Rate: 1e-5 - LR Scheduler: Cosine - Warmup Steps: 8 - Batch Size (per device): 1 - Gradient Accumulation Steps: 32 (Effective Batch Size: 32) - Max Sequence Length: 3072 - Epochs: 1 - ORPO Beta: 0.1
asm3515/merged-gptneo-imdb-lora	asm3515	2025-04-28T15:09:39Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neo", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-28T15:09:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ogulcanakca/llama3-8b-epdk-domain-adapter-v1	ogulcanakca	2025-04-28T15:07:40Z	3	0	peft	[ "peft", "safetensors", "llama-3", "lora", "qlora", "domain-adaptation", "text-generation", "turkish", "tr", "epdk", "mevzuat", "ogulcanakca", "conversational", "arxiv:2106.09685", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	text-generation	2025-04-25T12:32:57Z	--- base_model: meta-llama/Meta-Llama-3-8B-Instruct library_name: peft tags: - llama-3 - lora - qlora - domain-adaptation - text-generation - turkish - tr - epdk - mevzuat - ogulcanakca language: tr license: llama3 --- # Model Card for ogulcanakca/llama3-8b-epdk-domain-adapter-v1 ## Model Description This repository contains a PEFT LoRA adapter fine-tuned on the `meta-llama/Meta-Llama-3-8B-Instruct` base model for Domain Adaptation. The adapter was trained on a Turkish dataset comprising various documents related to the Turkish Energy Market Regulatory Authority (EPDK), such as legislation, laws, communiqués, and board decisions. The goal is to make the base Llama 3 8B Instruct model more familiar with the language, terminology, style, and concepts specific to the Turkish energy market regulation domain. When used with the base model, this adapter potentially offers improved understanding of texts from this domain and can generate more consistent outputs related to it. Note: This repository does not contain a full model, only the LoRA weights that need to be applied to the base model. This adapter was trained using 4-bit quantization (QLoRA) via the PEFT library. * Developed by: ogulcanakca * Model type: Transformer-based Causal Language Model (Llama 3) + PEFT LoRA adapter * Language(s) (NLP): Turkish (tr) * License: Llama 3 Community License ([https://llama.meta.com/llama3/license/](https://llama.meta.com/llama3/license/)) * Finetuned from model: `meta-llama/Meta-Llama-3-8B-Instruct` ### Model Sources * Repository: [https://huggingface.co/ogulcanakca/llama3-8b-epdk-domain-adapter-v1](https://huggingface.co/ogulcanakca/llama3-8b-epdk-domain-adapter-v1) * Base Model: [https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) * Dataset Used (Raw Cleaned): [https://huggingface.co/datasets/ogulcanakca/epdk_elektrik_piyasasi_mevzuat](https://huggingface.co/datasets/ogulcanakca/epdk_elektrik_piyasasi_mevzuat) ## Uses ### Direct Use This LoRA adapter can be used by loading it onto the `meta-llama/Meta-Llama-3-8B-Instruct` base model. It is expected that the base model will then better understand, summarize, or provide more consistent answers to prompts related to EPDK legislation. It can be used with the code example provided in the "How to Get Started with the Model" section below. Note: As this adapter was trained for a very short trial period (200 steps), the domain adaptation effect is limited. More extensive training or evaluation is recommended before using it for critical tasks. ### Downstream Use This adapter could serve as a better starting point for further fine-tuning on more specific tasks related to EPDK regulations (e.g., a specialized Q&A model, text classification, summarization). ### Out-of-Scope Use * Not suitable for use as a general-purpose chatbot (the base Instruct model is better for that). * Not expected to show significant improvement over the base model on topics outside Turkish energy market regulations. * May generate incorrect, incomplete, or outdated information. Should absolutely not be used for legal or financial advice. * Should not be used to generate harmful, unethical, discriminatory, or biased content. ## Bias, Risks, and Limitations * This adapter inherits potential biases and risks present in the base `meta-llama/Meta-Llama-3-8B-Instruct` model. * The training data (EPDK documents) covers a specific time range and may contain outdated information. Information generated by the model may not be current. * The model may generate incorrect information ("hallucinations"), especially on topics not well-represented or contradictory in the training data. * Potential OCR errors or imperfections during the cleaning phase in the training data might affect the model's performance. * Limited Training: This adapter was trained for only 200 steps, which is very short for domain adaptation. Therefore, the adaptation effect is minimal, and the model's knowledge or stylistic alignment within this domain may not be significantly improved. This version should be considered more of a "pipeline test". ### Recommendations * The accuracy of information generated by the model should always be verified against reliable sources. * Users should be aware of the model's limitations and avoid using it for critical applications (e.g., legal interpretation). * For more reliable results, further training with significantly more steps/epochs is recommended. ## How to Get Started with the Model You need the `transformers`, `peft`, `accelerate`, and `bitsandbytes` libraries to use this LoRA adapter. The code below loads the base model with 4-bit quantization and applies this adapter: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel import os # Ensure you are logged in to Hugging Face if the repo is private or requires Llama access # from huggingface_hub import login # login(token="YOUR_HF_TOKEN") # Or use huggingface-cli login # Base model ID base_model_id = "meta-llama/Meta-Llama-3-8B-Instruct" # This adapter's Hub ID adapter_id = "ogulcanakca/llama3-8b-epdk-domain-adapter-v1" # 4-bit Quantization Config bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 # Use bfloat16 if supported ) # Load the base model quantized print(f"Loading base model: {base_model_id}") base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config=bnb_config, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True # May be needed ) # Load the tokenizer print("Loading tokenizer...") tokenizer = AutoTokenizer.from_pretrained(base_model_id) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token # Load the LoRA adapter onto the base model print(f"Loading adapter: {adapter_id}") model = PeftModel.from_pretrained(base_model, adapter_id) print("Adapter loaded successfully.") model.eval() # Set model to evaluation mode # Inference Example prompt = "EPDK'nın elektrik piyasasındaki temel görevleri nelerdir?" # Example prompt in Turkish messages = [ {"role": "system", "content": "You are a helpful assistant knowledgeable about Turkish energy market regulations."}, {"role": "user", "content": prompt} ] # Llama 3 Instruct prompt format print("Generating response...") input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) # Terminator IDs to stop generation terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] # Generate text with torch.no_grad(): outputs = model.generate( input_ids, max_new_tokens=512, # Max new tokens to generate eos_token_id=terminators, do_sample=True, # For more creative outputs temperature=0.6, top_p=0.9, ) # Decode the generated output response_ids = outputs[0][input_ids.shape[-1]:] response_text = tokenizer.decode(response_ids, skip_special_tokens=True) print("\nModel Output:") print(response_text) ``` ## Training Details ### Training Data The model was fine-tuned on cleaned text from the `ogulcanakca/epdk_elektrik_piyasasi_mevzuat` Hugging Face dataset. This dataset was derived from ~3300 various Turkish documents (PDF, DOCX, etc.) related to the Turkish Energy Market Regulatory Authority (EPDK), obtained through text extraction, OCR, and basic NLP cleaning processes. Dataset Link (Raw Cleaned): [https://huggingface.co/datasets/ogulcanakca/epdk_elektrik_piyasasi_mevzuat](https://huggingface.co/datasets/ogulcanakca/epdk_elektrik_piyasasi_mevzuat) Before training, the texts in this dataset were chunked using the `meta-llama/Meta-Llama-3-8B-Instruct` tokenizer into segments of 2048 tokens with an overlap of 200 tokens. The resulting `domain_adaptation_data.jsonl` file (or equivalent `Dataset` object) containing 31,271 chunks was used for training. ### Training Procedure * Fine-tuning Type: Domain Adaptation (Causal LM objective). * Technique: QLoRA (4-bit NormalFloat Quantization + Low-Rank Adaptation) using the PEFT library. * Libraries: `transformers`, `peft`, `accelerate`, `bitsandbytes`, `datasets`. #### Preprocessing Cleaning steps mentioned above (whitespace, header/footer removal etc.) and tokenizer-based chunking were applied. `DataCollatorForLanguageModeling` was used during training. #### Training Hyperparameters * base_model: `meta-llama/Meta-Llama-3-8B-Instruct` * quantization: `4-bit` (NF4, compute_dtype=bfloat16) * lora_r: 16 * lora_alpha: 32 * lora_dropout: 0.05 * lora_target_modules: `["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]` * learning_rate: 2e-4 (0.0002) * batch_size: 1 * gradient_accumulation_steps: 8 (Effective batch size: 8) * optimizer: Paged AdamW (8-bit) * lr_scheduler_type: cosine * warmup_ratio: 0.05 * max_steps: 200 (Short trial run) * seq_length: 2048 * precision: bf16 (mixed precision) * gradient_checkpointing: True #### Speeds, Sizes, Times * Training was performed on a single GPU in Kaggle's free tier (likely T4 or P100 - exact type not logged). * The 200-step training run took approximately 8.5 hours. Flash Attention 2 could not be used. * Final training loss: ~2.151 * The saved LoRA adapter size is relatively small (typically tens or hundreds of MBs). ## Evaluation No formal evaluation metrics were calculated for this initial trial run (200 steps). The model's performance was only observed via the training loss. After more extensive training, standard language modeling metrics (like Perplexity) or performance on domain-specific downstream tasks could be evaluated. ### Testing Data, Factors & Metrics #### Testing Data ``` EPDK'nın elektrik piyasasındaki temel görevleri nelerdir? --------------------------------------------------------- --- Model Response --- EPDK, Elektrik Piyasası Kanunu'nun 5 inci maddesine göre, elektrik piyasasının işletilmesi ve gelişimi için gerekli ortamı oluşturmak, piyasada rekabetin gelişmesi için gerekli düzenlemeleri yapmak, serbest tüketim ve üretim faaliyetlerine ilişkin usul ve esasları belirlemek, denetlemek ve lisanslama işlemlerini yapmak, görevleri arasındadır. EPDK'nın elektrik piyasasındaki temel görevleri şu şekilde özetlenebilir: 1) Elektrik piyasasının gelişimi için gerekli düzenlemeleri yapmak: EPDK, elektrik piyasasının gelişimi için gerekli düzenlemeleri yapmakla yükümlüdür. Bu kapsamda, EPDK tarafından hazırlanan düzenlemeler, Kurul tarafından onaylanmak suretiyle yürürlüğe girer. 2) Piyasa işletimine ilişkin düzenlemeleri yapmak: EPDK, elektrik piyasasının işletilmesi için gerekli düzenlemeleri yapmakla yükümlüdür. Bu kapsamda, EPDK tarafından hazırlanan düzenlemeler, Kurul tarafından onaylanmak suretiyle yürürlüğe girer. 3) Lisans alma ve lisanslama işlemlerini yapmak: EPDK, lisans alma ve lisanslama işlemlerini yapmakla yükümlüdür. EPDK, lisans alma ve lisanslama işlemlerini yapmak için gerekli işlemleri yapar, lisansları verir, tadil ve iptal işlemlerini gerçekleştirir. 4) Denetlemek: EPDK, lisans sahibi tüzel kişilerden, lisanslarına ilişkin yükümlülüklerini yerine getirmelerini sağlamak amacıyla gerekli denetimleri yapmakla yükümlüdür. EPDK, denetimleri yapmak için gerekli işlemleri yapar, denetim sonuçlarını raporlar ve Kurul tarafından onaylanmak suretiyle yayımlar. 5) Elektrik piyasasının işletilmesi için gerekli ortamı oluşturmak: EPDK, elektrik piyasasının işletilmesi için gerekli ortamı oluşturmakla yükümlüdür. Bu kapsamda, EPDK tarafından hazırlanan düzenlemeler, Kurul tarafından onaylanmak suretiyle yürürlüğe girer. 6) Piyasa işletimine ilişkin usul ve esasları belirlemek: EPDK, elektrik piyasasının işletilmesi için gerekli usul ve esasları belirlemekle yükümlüdür. Bu kapsamda, EPDK tarafından hazırlanan düzenlemeler, Kurul tarafından onaylanmak suretiyle yürürlüğe girer. 7) Serbest tüketim ve üretim faaliyetlerine ilişkin usul ve esasları belirlemek: EPDK, serbest tüketim ve üretim faaliyetlerine ilişkin usul ve esasları belirlemekle yükümlüdür. Bu kapsamda, EPDK tarafından hazırlanan düzenlemeler, Kurul tarafından onaylanmak suretiyle yürürlüğe girer. 8) Lisans sahibi tüzel kişilerin faaliyetlerini izlemek: EPDK, lisans sahibi tüzel kişilerin faaliyetlerini izlemekle yükümlüdür. Bu kapsamda, EPDK, lisans sahibi tüzel kişilerin faaliyetlerini izlemek için gerekli işlemleri yapar. 9) Lisans sahibi tüzel kişilerin faaliyetlerine ilişkin incelemeleri yapmak: EPDK, lisans sahibi tüzel kişilerin faaliyetlerine ilişkin incelemeleri yapmakla yükümlüdür. Bu kapsamda, EPDK, lisans sahibi tüzel kişilerin faaliyetlerine ilişkin incelemeleri yapmak için gerekli işlemleri yapar. 10) Lisans sahibi tüzel kişilerin faaliyetlerine ilişkin inceleme sonuçlarını raporlamak: EPDK, lisans sahibi tüzel kişilerin faaliyetlerine ilişkin inceleme sonuçlarını raporlamakla yüküml --- End of Response --- ``` #### Summary The short 200-step training demonstrated that the fine-tuning pipeline works, but was insufficient for significant domain adaptation. A slight decrease in training loss was observed. ## Environmental Impact * Hardware Type: Kaggle GPU (Likely T4 or P100 tier) * Hours used: ~8.5 hours (for 200 steps) * Cloud Provider: Kaggle (using Google Cloud infrastructure) * Compute Region: Unknown (Managed by Kaggle) * Carbon Emitted: Can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute), but estimating accurately requires specific GPU power consumption data, which is difficult to obtain for Kaggle free tiers. ## Technical Specifications ### Model Architecture and Objective The base model relies on the Llama 3 architecture (Transformer decoder-only). This adapter adds LoRA layers without changing the base model weights, trained with a Causal Language Modeling objective for domain adaptation. ### Compute Infrastructure #### Hardware Kaggle Notebook environment with a single NVIDIA GPU (likely T4 or P100). #### Software * PyTorch (`torch==2.5.1` recommended based on Kaggle env) * Transformers (`transformers`) * PEFT (`peft==0.15.2`) * Accelerate (`accelerate`) * BitsAndBytes (`bitsandbytes`) * Datasets (`datasets`) * Python 3.10/3.11 (Kaggle default) ## Citation Please cite the base Llama 3 model and the relevant PEFT/LoRA techniques: * Llama 3: [https://ai.meta.com/blog/meta-llama-3/](https://ai.meta.com/blog/meta-llama-3/) * PEFT: [https://github.com/huggingface/peft](https://github.com/huggingface/peft) * LoRA: Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., ... & Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv preprint arXiv:2106.09685. ``` @misc{ogulcanakca_llama3_epdk_adapter_2025, author = {ogulcanakca}, title = {llama3-8b-epdk-domain-adapter-v1: A LoRA Adapter for Llama 3 8B Instruct Domain-Adapted to Turkish EPDK Legislation}, year = {2025}, publisher = {Hugging Face}, version = {v1}, url = {https://huggingface.co/ogulcanakca/llama3-8b-epdk-domain-adapter-v1} } ``` ## Model Card Contact - ogulcanakca (Hugging Face) - Mail: [email protected]
Nabushika/QwQ-32B-ArliAI-RpR-v3-8bpw-h8-exl2	Nabushika	2025-04-28T15:07:17Z	0	0	null	[ "qwen2", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v3", "base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v3", "license:apache-2.0", "region:us" ]	null	2025-04-28T14:53:16Z	--- license: apache-2.0 thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/coilCTGeL0OUYr9PA9zna.jpeg language: - en base_model: - ArliAI/QwQ-32B-ArliAI-RpR-v3 base_model_relation: quantized --- # QwQ-32B-ArliAI-RpR-v3 <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/coilCTGeL0OUYr9PA9zna.jpeg" alt="clickbait" width="500"> <small>Image generated using Arli AI Image Generation https://www.arliai.com/image-generation</small> ## RpR v3 Changes: - The best model from ArliAI yet: Extreme creativity and out of the box thinking. - No longer use QwQ-abliterated as base: v3 is a re-do of v2 but without the problems stemming from starting out with a QwQ-lorablated base. This turned out to not be a good move as it clearly lobotomizes the model more and was even visible from higher training and eval loss values. - Fixed dissasociated thoughts: A lot of effort have been made to completely re-run the RpR dataset generation in order to make sure the generated thinking tokens now always match what the model responses are. - Fixed random refusals: The previous RpR v1 dataset was generated with vanilla QwQ which caused some refusals in both the thinking and response examples, with RpR v3 the dataset generation is now done using QwQ-abliterated which prevents any refusals from coming through. - Fixed nonsense words found in dataset: There were a bunch of presumably censoring attempts found on the open datasets used for the RPMax/RpR datasets and these misplaced words/phrases has now been fixed to prevent the model from copying this behavior. - Rex scheduler: v3 is trained using the newer and better Rex scheduler instead of the regular cosine scheduler in order to improve the model learning nuances from more of the dataset as this scheduler keeps the learning rate higher for longer. ## RpR Series Overview: Building on RPMax with Reasoning RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series. RpR models use the same curated, deduplicated RP and creative writing dataset used for RPMax, with a focus on variety to ensure high creativity and minimize cross-context repetition. Users familiar with RPMax will recognize the unique, non-repetitive writing style unlike other finetuned-for-RP models. With the release of QwQ as the first high performing open-source reasoning model that can be easily trained, it was clear that the available instruct and creative writing reasoning datasets contains only one response per example. This is type of single response dataset used for training reasoning models causes degraded output quality in long multi-turn chats. Which is why Arli AI decided to create a real RP model capable of long multi-turn chat with reasoning. In order to create RpR, we first had to actually create the reasoning RP dataset by re-processing our existing known-good RPMax dataset into a reasoning dataset. This was possible by using the base QwQ Instruct model itself to create the reasoning process for every turn in the RPMax dataset conversation examples, which is then further refined in order to make sure the reasoning is in-line with the actual response examples from the dataset. Another important thing to get right is to make sure the model is trained on examples that present reasoning blocks in the same way as it encounters it during inference. Which is, never seeing the reasoning blocks in it's context. In order to do this, the training run was completed using axolotl with manual template-free segments dataset in order to make sure that the model is never trained to see the reasoning block in the context. Just like how the model will be used during inference time. The result of training QwQ on this dataset with this method are consistently coherent and interesting outputs even in long multi-turn RP chats. This is as far as we know the first true correctly-trained reasoning model trained for RP and creative writing. You can access the model at https://arliai.com and we also have a models ranking page at https://www.arliai.com/models-ranking Ask questions in our new Discord Server https://discord.com/invite/t75KbPgwhk or on our subreddit https://www.reddit.com/r/ArliAI/ ## Model Description QwQ-32B-ArliAI-RpR-v3 is the third release in the RpR series. It is a 32-billion parameter model fine-tuned using the RpR dataset based on the curated RPMax dataset combined with techniques to maintain reasoning abilities in long multi-turn chats. ### Specs * Base Model: QwQ-32B * Max Context Length: Max 128K (Realistically 32K) * Parameters: 32B * Reasoning Model: Yes ### Training Details * Sequence Length: 8192 * Epochs: 1 epoch training (Inherited from RPMax methods) * Fine-tuning Method: RS-QLORA+ (Rank-Stabilized LoRA + LoRA Plus 8x) * Rank/Alpha: 128-rank 128-alpha * Learning Rate: 0.00001 * Scheduler: Rex * Gradient accumulation: 32 ### Very Nice Training graphs :) <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/gBGmhMB0kgoJTmxs-fvtk.png" alt="Train Loss" width="600"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/DtdMtuoA4bX8mKmxOSY10.png" alt="Eval Loss" width="600"> ### Quantization * BF16: https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3 * GGUF: https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3-GGUF ### How to use reasoning models correctly in ST <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/njVt2Vir8Isd3ApjTBmoI.png" alt="RpR ST Settings" width="600"> For any reasoning models in general, you need to make sure to set: * Prefix is set to ONLY \<think> and the suffix is set to ONLY \</think> without any spaces or newlines (enter) * Reply starts with \<think> * Always add character names is unchecked * Include names is set to never * As always the chat template should also conform to the model being used Note: Reasoning models work properly only if include names is set to never, since they always expect the eos token of the user turn followed by the \<think> token in order to start reasoning before outputting their response. If you set include names to enabled, then it will always append the character name at the end like "Seraphina:\<eos_token>" which confuses the model on whether it should respond or reason first. The rest of your sampler parameters can be set as you wish as usual. If you don't see the reasoning wrapped inside the thinking block, then either your settings is still wrong and doesn't follow my example or that your ST version is too old without reasoning block auto parsing. If you see the whole response is in the reasoning block, then your \<think> and \</think> reasoning token suffix and prefix might have an extra space or newline. Or the model just isn't a reasoning model that is smart enough to always put reasoning in between those tokens. ### If you set everything up correctly, it should look like this: <img src="https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/IDs6FooZgVTIBNHFHZUZB.png" alt="RpR example response" width="600"> --- <details> <summary>Details: The RPMax Foundation (Dataset & Training Philosophy)</summary> The following sections detail the core philosophy behind the dataset and training methodology originally developed for RPMax, which serves as the foundation for the RpR series. ### The Goal: Reduced Repetition and Higher Creativity The goal of the dataset curation used for both RPMax and RpR is to reduce repetitions and increase the models ability to creatively write in different situations presented to it. What this means is it is a model that will output responses very differently without falling into predictable tropes across different situations. ### What is repetition and creativity? First of all, creativity should mean the variety in output that the model is capable of creating. You should not confuse creativity with writing prose. When a model writes in a way that can be said to be pleasant like writers would write in a novel, this is not creative writing. This is just a model having a certain pleasant type of writing prose. So a model that writes nicely is not necessarily a creative model. Repetition and creativity are essentially intertwined with each other, so if a model is repetitive then a model can also be said to be un-creative as it cannot write new things and can only repeat similar responses that it has created before. For repetition there are actually two very different forms of repetition. In-context repetition: When people mention a model is repetitive, this usually mean a model that likes to repeat the same phrases in a single conversation. An example of this is when a model says that a character "flicks her hair and...." and then starts to prepend that "flicks her hair and..." into every other action that character does. It can be said that the model is boring, but even in real people's writing it is possible that this kind of repetition could be intentional to subtly prove a point or showcase a character's traits in some scenarios. So this type of repetition is not always bad and completely discouraging a model from doing this does not always lead to improve a model's writing ability. In this regard, RPMax and RpR is not yet focused on eliminating this type of repetition so there might be some in-context repetition that can be seen in the outputs. Eliminating this will be the next big step of the RPMax and RpR series of models. Cross-context repetition: A second worse type of repetition is a model's tendency to repeat the same phrases or tropes in very different situations. An example is a model that likes to repeat the infamous "shivers down my spine" phrase in wildly different conversations that don't necessarily fit with that phrase. This type of repetition is ALWAYS bad as it is a sign that the model has over-fitted into that style of "creative writing" that it has often seen in the training dataset. A model's tendency to have cross-context repetition is also usually visible in how a model likes to choose similar repetitive names when writing stories. Such as the infamous "elara" and "whispering woods" names. The primary goal of the dataset curation for RPMax and RpR is to create a highly creative model by reducing cross-context repetition, as that is the type of repetition that follows you through different conversations. This is combated by making sure the dataset does not have repetitions of the same situations or characters in different example entries. ### Dataset Curation The success of models trained on this dataset (including RPMax and now RpR) is thanks to the training method and the unique dataset created for fine-tuning. It contains as many open source creative writing and RP datasets that can be found (all from Hugging Face), from which have been curated to weed out datasets that are purely synthetic generations as they often only serve to dumb down the model and make the model learn GPT-isms (slop) rather than help. Then Llama 3.1 8B (or a similarly capable model) is used to create a database of the characters and situations that are portrayed in these datasets, which is then used to de-dupe these datasets to make sure that there is only a single entry of any character or situation. ### The Golden Rule of Fine-Tuning Unlike the initial pre-training stage where the more data you throw at it the better it becomes for the most part, the golden rule for fine-tuning models isn't quantity, but instead quality over quantity. So the dataset used here is actually orders of magnitude smaller than it would be if it included repeated characters and situations in the dataset, but the end result is a model that does not feel like just another "in-breed" of another creative writing/RP model. ### Training Parameters and Unconventional Approach The usual way is to have a low learning rate and high gradient accumulation for better loss stability, and then run multiple epochs of the training run until the loss is acceptable. The RPMax and RpR methodology, however, uses only one single epoch, a low gradient accumulation, and a higher than normal learning rate. The loss curve during training is actually unstable and jumps up and down a lot, but if it is smoothed out, it is steadily decreasing over time. The theory is that this allows the models to learn from each individual example in the dataset much more, and by not showing the model the same example twice using multiple epochs, it stops the model from latching on and reinforcing a single character or story trope. The jumping up and down of loss during training is because as the model gets trained on a new entry from the dataset, the model will have never seen a similar example before and therefore can't really predict an answer similar to the example entry. While the relatively high end loss of 1.0 or slightly above is actually acceptable because the goal was never to create a model that can output exactly like the dataset that is being used to train it. Rather to create a model that is creative enough to make up it's own style of responses. This is different from training a model in a particular domain and needing the model to reliably be able to output like the example dataset, such as when training a model on a company's internal knowledge base. </details> --- ## Try It Out! Model preference is subjective, so please do try QwQ-32B-ArliAI-RpR-v3 for yourself. Your feedback both good and bad is always valueable and will help us improve the future RPMax and RpR models.
Walid23/phi-2-fine-Qtuned	Walid23	2025-04-28T15:06:47Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "region:us" ]	null	2025-04-28T12:49:29Z	--- base_model: microsoft/phi-2 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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DevQuasar/allura-org.GLM4-9B-Neon-v2-GGUF	DevQuasar	2025-04-28T15:06:23Z	0	0	null	[ "gguf", "text-generation", "base_model:allura-org/GLM4-9B-Neon-v2", "base_model:quantized:allura-org/GLM4-9B-Neon-v2", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-28T13:50:09Z	--- base_model: - allura-org/GLM4-9B-Neon-v2 pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [allura-org/GLM4-9B-Neon-v2](https://huggingface.co/allura-org/GLM4-9B-Neon-v2) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
Artorias-23/finetuned-TinyLlama_TinyLlama-1.1B-Chat-v1.0	Artorias-23	2025-04-28T15:02:25Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2025-04-28T15:02:18Z	--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
davesalvi/ispl_safe	davesalvi	2025-04-28T15:01:12Z	0	0	null	[ "region:us" ]	null	2025-03-20T23:22:49Z	# SAFE ISPL Submission Submission of the ISPL team fro Politecnico di Milano (Italy) for the SAFE Challenge, organized for IH&MMSEC 2025. -- The key requirements is to have a `script.py` file in the top level directory of the repo.
maksf8486/7f9fdc5d-9b46-4502-9c6a-3e38abe6d724	maksf8486	2025-04-28T14:59:07Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.1-Storm-8B", "base_model:adapter:unsloth/Llama-3.1-Storm-8B", "license:llama3.1", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T14:36:00Z	--- library_name: peft license: llama3.1 base_model: unsloth/Llama-3.1-Storm-8B tags: - axolotl - generated_from_trainer model-index: - name: 7f9fdc5d-9b46-4502-9c6a-3e38abe6d724 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/Llama-3.1-Storm-8B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4f7fb6b1395b4ddc_train_data.json ds_type: json format: custom path: /workspace/input_data/4f7fb6b1395b4ddc_train_data.json type: field_instruction: question field_output: solution format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: false reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: maksf8486/7f9fdc5d-9b46-4502-9c6a-3e38abe6d724 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/4f7fb6b1395b4ddc_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: 70a7f19f-63fd-4556-b3b8-dab1053f3f07 wandb_project: s56-2 wandb_run: your_name wandb_runid: 70a7f19f-63fd-4556-b3b8-dab1053f3f07 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 7f9fdc5d-9b46-4502-9c6a-3e38abe6d724 This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6659 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5163 \| 0.0157 \| 200 \| 0.6659 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
AllIllusion/q-FrozenLake-v1-8x8	AllIllusion	2025-04-28T14:57:34Z	0	0	null	[ "FrozenLake-v1-8x8", "q-learning", "reinforcement-learning", "custom-implementation", "SL-Sprout", "model-index", "region:us" ]	reinforcement-learning	2025-04-25T23:17:26Z	--- tags: - FrozenLake-v1-8x8 - q-learning - reinforcement-learning - custom-implementation - SL-Sprout model-index: - name: q-FrozenLake-v1-8x8 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-8x8 type: FrozenLake-v1-8x8 metrics: - type: mean_reward value: 0.69 +/- 0.46 name: mean_reward verified: false --- # sl_Sprout Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1. ## Usage ```python model = load_from_hub(repo_id="AllIllusion/q-FrozenLake-v1-8x8", filename="sl_TabularModel_FrozenLake-v1.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["FrozenLake-v1"]) ```
danielbyiringiro/classifier	danielbyiringiro	2025-04-28T14:57:06Z	3	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-18T02:58:14Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: classifier results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # classifier This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4750 - Accuracy: 0.8828 - F1 Score: 0.8802 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 Score \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5848 \| 1.0 \| 12666 \| 0.5676 \| 0.8173 \| 0.8134 \| \| 0.4484 \| 2.0 \| 25332 \| 0.4573 \| 0.8582 \| 0.8525 \| \| 0.2773 \| 3.0 \| 37998 \| 0.4750 \| 0.8828 \| 0.8802 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
dangdangde/Hate-Llama3.2-1B.Human_Lgb.2_label	dangdangde	2025-04-28T14:57:05Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-04-28T14:57:00Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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Triangle104/QwQ-32B-ArliAI-RpR-v3-Q4_K_M-GGUF	Triangle104	2025-04-28T14:53:46Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v3", "base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T14:51:05Z	--- base_model: ArliAI/QwQ-32B-ArliAI-RpR-v3 language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo thumbnail: https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/coilCTGeL0OUYr9PA9zna.jpeg --- # Triangle104/QwQ-32B-ArliAI-RpR-v3-Q4_K_M-GGUF This model was converted to GGUF format from [`ArliAI/QwQ-32B-ArliAI-RpR-v3`](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3) 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/ArliAI/QwQ-32B-ArliAI-RpR-v3) for more details on the model. RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series. --- RpR models use the same curated, deduplicated RP and creative writing dataset used for RPMax, with a focus on variety to ensure high creativity and minimize cross-context repetition. Users familiar with RPMax will recognize the unique, non-repetitive writing style unlike other finetuned-for-RP models. With the release of QwQ as the first high performing open-source reasoning model that can be easily trained, it was clear that the available instruct and creative writing reasoning datasets contains only one response per example. This is type of single response dataset used for training reasoning models causes degraded output quality in long multi-turn chats. Which is why Arli AI decided to create a real RP model capable of long multi-turn chat with reasoning. In order to create RpR, we first had to actually create the reasoning RP dataset by re-processing our existing known-good RPMax dataset into a reasoning dataset. This was possible by using the base QwQ Instruct model itself to create the reasoning process for every turn in the RPMax dataset conversation examples, which is then further refined in order to make sure the reasoning is in-line with the actual response examples from the dataset. Another important thing to get right is to make sure the model is trained on examples that present reasoning blocks in the same way as it encounters it during inference. Which is, never seeing the reasoning blocks in it's context. In order to do this, the training run was completed using axolotl with manual template-free segments dataset in order to make sure that the model is never trained to see the reasoning block in the context. Just like how the model will be used during inference time. The result of training QwQ on this dataset with this method are consistently coherent and interesting outputs even in long multi-turn RP chats. This is as far as we know the first true correctly-trained reasoning model trained for RP and creative writing. You can access the model at https://arliai.com and we also have a models ranking page at https://www.arliai.com/models-ranking Ask questions in our new Discord Server https://discord.com/invite/t75KbPgwhk or on our subreddit https://www.reddit.com/r/ArliAI/ --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v3-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q4_K_M-GGUF --hf-file qwq-32b-arliai-rpr-v3-q4_k_m.gguf -c 2048 ```
mradermacher/ZR1-1.5B-ft-GGUF	mradermacher	2025-04-28T14:52:39Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "en", "base_model:qwertyuiopasdfg/ZR1-1.5B-ft", "base_model:quantized:qwertyuiopasdfg/ZR1-1.5B-ft", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T14:37:55Z	--- base_model: qwertyuiopasdfg/ZR1-1.5B-ft language: - en library_name: transformers license: other quantized_by: mradermacher tags: - llama-factory - full - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/qwertyuiopasdfg/ZR1-1.5B-ft <!-- 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/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q2_K.gguf) \| Q2_K \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q3_K_S.gguf) \| Q3_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q3_K_M.gguf) \| Q3_K_M \| 1.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q3_K_L.gguf) \| Q3_K_L \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.IQ4_XS.gguf) \| IQ4_XS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q4_K_S.gguf) \| Q4_K_S \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q4_K_M.gguf) \| Q4_K_M \| 1.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q5_K_S.gguf) \| Q5_K_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q5_K_M.gguf) \| Q5_K_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q6_K.gguf) \| Q6_K \| 1.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.Q8_0.gguf) \| Q8_0 \| 2.0 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/ZR1-1.5B-ft-GGUF/resolve/main/ZR1-1.5B-ft.f16.gguf) \| f16 \| 3.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/kodama-final-GGUF	mradermacher	2025-04-28T14:42:30Z	0	0	transformers	[ "transformers", "gguf", "judge", "evaluation", "scoring", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:rpeddu/kodama-updated", "base_model:rpeddu/kodama-final", "base_model:quantized:rpeddu/kodama-final", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T14:35:23Z	--- base_model: rpeddu/kodama-final datasets: - rpeddu/kodama-updated language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: mit quantized_by: mradermacher tags: - judge - evaluation - scoring --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/rpeddu/kodama-final <!-- 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/kodama-final-GGUF/resolve/main/kodama-final.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.Q8_0.gguf) \| Q8_0 \| 0.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/kodama-final-GGUF/resolve/main/kodama-final.f16.gguf) \| f16 \| 1.1 \| 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 -->
tonyzhang666/dc-ae-f32c32-in-1.0-w4-v25	tonyzhang666	2025-04-28T14:41:24Z	0	0	null	[ "safetensors", "base_model:mit-han-lab/dc-ae-f32c32-in-1.0", "base_model:finetune:mit-han-lab/dc-ae-f32c32-in-1.0", "license:mit", "region:us" ]	null	2025-04-28T13:37:09Z	--- license: mit base_model: - mit-han-lab/dc-ae-f32c32-in-1.0 --- # Deep Conpression AutoDecoder via Distillation <center>Jingyuan Zhang [email protected]</center> <center>School of Electronic Information and Electrical Engineering</center> <center>Shanghai Jiao Tong University</center> ## Abstract This project constructs a pipeline to get light models with minimal loss via distillation. First, we prune model structure of decoder and arquire light student models. Then, we do distillation training with teacher model [dc-ae-f32c32-in-1.0](https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0) to reduce the gap as much as possible. During traing, we always freeze the encoder part and project out part of decoder. Furthermore, we involved tricks like AdamW optimizer, CosineAnnealingWarmRestarts Scheduler, Dynamic Loss Weight Adjustment Method, Batch Accumulation and Segment Training. Components of loss function include L1 distillation loss, L1 image loss, LPIPS loss, PatchGAN loss etc. After that, we choose FID, PSNR, SSIM and LPIPS as our evaluation matrice for image quality and MACs/inference time as indicators for speed. Finally, new light models outperform benchmark in PSNR and SSIM, the quality of generated images. Also, there is no significant difference in visualization between the generated image and teacher model. ## Environment Setup 1. In this folder, run the command below to create a new environment named "myenv", or it will create an environment named efficientvit by default. ``` bash conda env create -f environment.yml -n myenv ``` 2. Notice that the efficientvit package in path efficientvit/applications/dc_ae/scripts/efficientvit and efficientvit/efficientvit have been modified due to the changes in new model configurations. ## Usage ### Demo The command of VAE model demonstration is in file "efficientvit/applications/dc_ae/scripts/demo_recons.sh", you may also use the command: ``` bash CUDA_VISIBLE_DEVICES=1 python /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/scripts/demo-dc-ae-recons.py \ --pretrained_model /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pruned_models/dc-ae-f32c32-in-1.0-w4-v4 ``` The demo picture will be stored in path "efficientvit/applications/dc_ae/reconstruction_results". ### Modify Model Structure Code for modifying layers and pruning components is in "dc_de_modify_layer.py", and run the command: ``` bash CUDA_VISIBLE_DEVICES=3 python /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/scripts/dc_de_modify_layer.py \ --prune_method direct --prune_version w4-v3 \ --pretrained_model /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pretrained_models/dc-ae-f32c32-in-1.0 \ --save_path /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pruned_models/dc-ae-f32c32-in-1.0-w4-v3/model.safetensors ``` There are three pruning methods. "Direct" means if I want to take 15 out of 30 parameters, the first 15 parameters will be taken directly. "gap" method will take 15 at intervals and "random" method will initialize to normally distributed random numbers. Parameter pretrained_model are path for pretrained teacher model and save_path are for target model. In model save path, there're two other files, config.json for model_name which has been registered in ae_model_zoo.py and dc_ae.py and training_loss.txt for records during training. Remember changing file/model path to the local path on your device. If you want to create new models by modifying layers, remember adding corresponding model info in "efficient/model/efficient/dc_ae.py and efficient/ae_model_zoo.py". ### Model List We have several versions of pruned models list below and you may download them from Google Drive links. \| Model Name \| Description \| Training Dataset \| Note \| \| :---------------------------------------------------------------------------------: \| :-----------------------------------------: \| :-------------------: \| :----: \| \| [dc-ae-f32c32-in-1.0-w3-v2](https://huggingface.co/tonyzhang666/dc-ae-f32c32-in-1.0-w3-v2) \| Base Model, decoder.depth_list=[0,5,10,2,2,2] -> [0,5,10,1,1,2], Decoder Compression Ratio 10%, MACs reduce 1.5% \| ImageNet \| \| \| [dc-ae-f32c32-in-1.0-w4-v1](https://huggingface.co/tonyzhang666/dc-ae-f32c32-in-1.0-w4-v1) \| Base Model, decoder.depth_list=[0,5,10,2,2,2] -> [0,3,5,2,2,2], Compression Ratio 8%, 22% reduction in total MACs and 40% reduction in decoder MACs \| ImageNet \| \| \| [dc-ae-f32c32-in-1.0-w4-v2](https://huggingface.co/tonyzhang666/dc-ae-f32c32-in-1.0-w4-v2) \| Base Model, decoder.depth_list=[0,5,10,2,2,2] -> [0,3,5,1,1,2], Compression Ratio 14%, 24% reduction in total MACs and 42% reduction in decoder MACs \| ImageNet \| \| \| [dc-ae-f32c32-in-1.0-w4-v3](https://huggingface.co/tonyzhang666/dc-ae-f32c32-in-1.0-w4-v3) \| Base Model, decoder.depth_list=[0,5,10,2,2,2] -> [0,1,2,1,1,2], Compression Ratio 12%, 40% reduction in total MACs, 65% reduction in decoder MACs \| ImageNet \| \| \| [dc-ae-f32c32-in-1.0-w4-v4](https://huggingface.co/tonyzhang666/dc-ae-f32c32-in-1.0-w4-v4) \| Based on dc-ae-f32c32-in-1.0-w3-v2, distillation training with GAN, Loss = 100 * L1_Dis + 1 * L1 + 0.1 * LPIPS + 0.05 * PatchGAN, GAN training ratio 300:1 \| ImageNet \| \| \| [dc-ae-f32c32-in-1.0-w4-v8](https://huggingface.co/tonyzhang666/dc-ae-f32c32-in-1.0-w4-v8) \| Based on dc-ae-f32c32-in-1.0-w4-v1, distillation training with GAN, Loss = 100 * L1_Dis + 1 * L1 + 0.1 * LPIPS + 0.1 * PatchGAN, GAN training ratio 300:1 \| ImageNet \| \| \| [dc-ae-f32c32-in-1.0-w4-v25](https://huggingface.co/tonyzhang666/dc-ae-f32c32-in-1.0-w4-v25) \| Based on dc-ae-f32c32-in-1.0-w4-v3, distillation training with GAN, Loss = 100 * L1_Dis + 1 * L1 + 0.1 * LPIPS + 0.3 * PatchGAN, GAN training ratio 300:1, Dynamic Loss Training \| ImageNet \| \| ### Distillation Training and Evaluation Normally, for all the training, we set 15 epochs and use 600 pictures randomly chosen from ImageNet for each epoch, costing about 75 mins on a single A6000 GPU. During all the training, we fix the encoder and project out layer of decoder. There're two versions of pipeline, with GAN Loss and without GAN Loss but the main ideas of them are similar. For distillation part, there're three choices, before project_out layer, after TritonRMSNorm2d and after ReLU activation function. From ablation study, we choose to align the matrix after ReLU activation function using L1 Loss. For the image generation loss part, we combine L1 loss, LPIPS loss and PatchGAN loss together with different weights. For training techniques, we tried adding AdamW optimizer, CosineAnnealingWarmRestarts Scheduler, Dynamic Loss Weight Adjustment Method, Batch Accumulation and Segment Training, but only some of them seems to work. After that, we chose to use AdamW optimizer, CosineAnnealingWarmRestarts Scheduler and Dynamic Loss Weight Adjustment Method. During experiments of parameter tuning, we found that distillation loss has much more importance than image generation loss. Even if the image evaluation matrices have reach a satisfactory result, the pictures are still not good enough due to high diatillation loss. Therefore, we give distillation loss a much higher weight during the first 10 epoch and let it gradually decrease to half of its original value according to the cosine law. The idea is to ensure the accuracy of distillation first! During the last 5 epoch, we focus more on image loss, and gradually increase their weights to double of their original value according to the cosine law. For example, to train a student model based on w4-v3 pruned model, Loss = 100 * L1_Distillation + 1 * L1_Image + 0.1 * LPIPS_Image + 0.3 * PatchGAN_Image. And the training proportion of generation model(student model) and discrimination model is 300, which means we train student model for 300 samples and then train GAN discriminator model once, in case the discriminator learns too fast so that the student model will get lost and don't know how to optimize toward arquiring real images. The training command are as followed (the complete training command for three best models are in file train_distillation.sh): ``` bash # Training Code for dc-ae-f32c32-in-1.0-w4-v25 # Expected FID: 2.22879, PSNR: 26.22011, SSIM: 0.72431, LPIPS: 0.12579 # Based on model w4-v3, decoder.depth_list=[0,5,10,2,2,2] -> [0,1,2,1,1,2], # Compression Ratio 12%, 40% reduction in total MACs, 65% reduction in decoder MACs CUDA_VISIBLE_DEVICES=7 python /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/scripts/dc_de_distillation_gan.py \ --batch_size 4 --learning_rate_G 1e-4 --learning_rate_D 1e-4 --num_epochs 15 --train_samples 600 \ --student_model_path /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pruned_models/dc-ae-f32c32-in-1.0-w4-v3 \ --model_save_dir /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pruned_models/dc-ae-f32c32-in-1.0-w4-v25 \ --pic_save_dir /data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pruned_models/pic_results_w4_v25 \ --alpha_disti 100 --alpha_img 1 --beta 0.1 --gamma 0.3 --gan_ratio 300 --align 3 --freeze_proj_out True --freeze_encoder True \ --cosine_T_0_G 5 --cosine_T_mult_G 1 --eta_min_G 1e-6 --weight_decay_G 0.01 \ --cosine_T_0_D 5 --cosine_T_mult_D 1 --eta_min_D 1e-6 --weight_decay_D 0.01 \ --dynamic_loss True --division_epoch 10 \ --accumulate_batch False --accumulation_steps 4 \ --shallow_train False --shallow_training_epochs 5 --model_config dc-ae-f32c32-in-1.0-pruned-w4-v3 ``` You can measure the evaluation matrice (FID, PSNR, SSIM, LPIPS) of models through the command below, be aware to substitute the args "model" with your target model path. ``` bash CUDA_VISIBLE_DEVICES=7 torchrun --nnodes=1 --nproc_per_node=1 --master_port 29505 -m applications.dc_ae.eval_dc_ae_model dataset=imagenet_512 model=/data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pruned_models/dc-ae-f32c32-in-1.0-w4-v25 run_dir=tmp ``` During training, the generation pictures (ground truth, teacher model, student model) at the end of each epoch will be stored in "pic_save_dir" and the loss information will be save to "model_save_dir/training_losses.txt". So, we can better monitor the training process and analysis the problems. Batch accumulation method use "accumulation_step" parameter to update parameter after certain batches to virtually increase batch size. In Segement train method, we intend to train shallow layers/blocks first and deep ones later to resuce the training cost and improve efficiency. Unfortunately, these attempts seem to fail. For exact and detailed meaning, type, default value etc. of each parameter, please refer to code or Appendix. If you are interested in more detailed training and debugging process, you may also refer to [this Feishu Docs](https://sjtu.feishu.cn/docx/TaexdtRxfoLwsoxbrQQcS9nynRe). ## Demo of DC_DE - Demo of training results \| Model \| Description \| Result \| Epoch 15 \| \| :---------------------------------------------------------------------------------: \| :-----------------------------------------: \| :-------------------: \| :---------------: \| \| [dc-ae-f32c32-in-1.0-w4-v4](https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0) \| Based on dc-ae-f32c32-in-1.0-w3-v2, distillation training with GAN, Loss = 100 * L1_Dis + 1 * L1 + 0.1 * LPIPS + 0.05 * PatchGAN, GAN training ratio 300:1, Compression Ratio 10%, 1.5% reduction in total MACs \| Ground Truth \| ![alt text](assets/image.png) \| \| \| \| Teacher Model\| ![alt text](assets/image-9.png) \| \| \| \| Ours\| ![alt text](assets/image-10.png) \| \| [dc-ae-f32c32-in-1.0-w4-v8](https://huggingface.co/mit-han-lab/dc-ae-f32c32-mix-1.0) \| Based on dc-ae-f32c32-in-1.0-w4-v1, distillation training with GAN, Loss = 100 * L1_Dis + 1 * L1 + 0.1 * LPIPS + 0.1 * PatchGAN, GAN training ratio 300:1, Compression Ratio 8%, 22% reduction in total MACs and 40% reduction in decoder MACs \| Ground Truth \| ![alt text](assets/image-3.png) \| \| \| \| Teacher Model \| ![alt text](assets/image-11.png) \| \| \| \| Ours \| ![alt text](assets/image-12.png) \| \| [dc-ae-f32c32-in-1.0-w4-v25](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0) \| Based on dc-ae-f32c32-in-1.0-w4-v3, distillation training with GAN, Loss = 100 * L1_Dis + 1 * L1 + 0.1 * LPIPS + 0.3 * PatchGAN, GAN training ratio 300:1, Dynamic Loss Training, Compression Ratio 12%, 40% reduction in total MACs, 65% reduction in decoder MACs \| Ground Truth \| ![alt text](assets/image-6.png) \| \| \| \|Teacher Model\| ![alt text](assets/image-7.png) \| \| \| \| Ours \| ![alt text](assets/image-8.png) \| - Demo of Picture Girls via Different Models \| Ground Truth \| Teacher Model \| dc-ae-f32c32-in-1.0-w4-v4 \| dc-ae-f32c32-in-1.0-w4-v8 \| dc-ae-f32c32-in-1.0-w4-v25 \| \| :-------------------: \| :-------------------: \| :-------------------: \| :-------------------: \| :-------------------: \| \| ![alt text](assets/image-13.png) \| ![alt text](assets/image-14.png) \| ![alt text](assets/image-15.png) \| ![alt text](assets/image-16.png) \| ![alt text](assets/image-17.png) \| - Evaluation Matrices of Models \| Model Name \| FID(↓) \| PSNR(↑) \| SSIM(↑) \| LPIPS(↓) \| \| :---------------: \| :-------------------: \| :-------------------: \| :---------------: \| :---------------: \| \| dc-ae-f32c32-in-1.0(benchmark) \| 0.2047 \| 26.2547 \| 0.7136\| 0.0783 \| \| dc-ae-f32c32-in-1.0-w4-v4 \| 0.83769 \| 26.5646 \| 0.73160\| 0.09752 \| \| dc-ae-f32c32-in-1.0-w4-v8 \| 1.69890 \| 26.55881 \| 0.73866\| 0.11312 \| \| dc-ae-f32c32-in-1.0-w4-v25 \| 2.22879 \| 26.22011 \| 0.72431\| 0.12579 \| ## Appendix Generally the parameters and corresponding description are as followed: ``` bash parser.add_argument("--teacher_model_path", type=str, default="/data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pretrained_models/dc-ae-f32c32-in-1.0", required=False, help="Path to the teacher model.") parser.add_argument("--student_model_path", type=str, default="/data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pruned_models/dc-ae-f32c32-in-1.0-v1" ,required=False, help="Path to the student model.") parser.add_argument("--model_config", type=str, default="dc-ae-f32c32-in-1.0-pruned-w4-v3" ,required=True, help="Config name of the model.") parser.add_argument("--dataset_path", type=str, default="/home/jyzhang/dataset/imagenet/train", required=False, help="Path to the dataset (e.g., ImageNet).") parser.add_argument("--batch_size", type=int, default=16, help="Batch size for training.") parser.add_argument("--learning_rate_G", type=float, default=1e-4, help="Learning rate for training Generator (student model).") parser.add_argument("--learning_rate_D", type=float, default=1e-4, help="Learning rate for training Discriminator.") parser.add_argument("--alpha_disti", type=float, default=1.0, help="Weight for L1 Loss.") parser.add_argument("--alpha_img", type=float, default=0.8, help="Weight for L1 Loss.") parser.add_argument("--beta", type=float, default=0.1, help="Weight for LPIPS Loss.") parser.add_argument("--gamma", type=float, default=0.05, help="Weight for PatchGAN Loss.") parser.add_argument("--num_epochs", type=int, default=10, help="Number of epochs for training.") parser.add_argument("--shallow_train", type=bool, default=False, required=False, help="Whether to train shallow layers first and full layers later.") parser.add_argument("--shallow_training_epochs", type=int, default=5, help="Number of epochs for shallow layers training.") parser.add_argument("--gan_ratio", type=int, default=10000, help="Number of epochs for training.") parser.add_argument("--align", type=int, default=0, required=False, help="Latent to align with, 0 for final feature after project_out, 1 for feature before project_out, 2 for feature after Norm, 3 for feature after ReLu.") parser.add_argument("--train_samples", type=int, default=1281167, help="Number of image samples for training. 1281167 is the whole num of samples in imagenet") parser.add_argument("--pic_save_dir", type=str, default="/data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/reconstruction_results", required=False, help="Path to the save sampled image.") parser.add_argument("--model_save_dir", type=str, default="/data2/user/jyzhang/MIT/efficientvit/applications/dc_ae/pruned_models", required=False, help="Path to the save distillated model.") parser.add_argument("--freeze_proj_out", type=bool, default=True, required=False, help="Whether to freeze the proj_out layer during training. It should be freezed for distillation training.") parser.add_argument("--freeze_encoder", type=bool, default=True, required=False, help="Whether to freeze the encoder layer during training. It should be freezed for distillation training.") parser.add_argument("--weight_decay_G", type=float, default=0.01, help="Weight decay for Generater AdamW optimizer.") parser.add_argument("--weight_decay_D", type=float, default=0.01, help="Weight decay for Discriminator AdamW optimizer.") parser.add_argument("--cosine_T_0_G", type=int, default=10, help="Number of iterations for the first restart for Generator.") parser.add_argument("--cosine_T_0_D", type=int, default=10, help="Number of iterations for the first restart for Discriminator.") parser.add_argument("--cosine_T_mult_G", type=int, default=1, help="A factor to increase T_i after each restart for Generator.") parser.add_argument("--cosine_T_mult_D", type=int, default=1, help="A factor to increase T_i after each restart for Discriminator.") parser.add_argument("--eta_min_G", type=float, default=1e-6, help="Minimum learning rate for Generator.") parser.add_argument("--eta_min_D", type=float, default=1e-6, help="Minimum learning rate for Discriminator.") parser.add_argument("--dynamic_loss", type=bool, default=False, required=False, help="Whether to use dynamic loss adatation strategy.") parser.add_argument("--division_epoch", type=int, default=10, required=False, help="Before division epoch, focus more on distillation loss, After that, focus more on image results.") parser.add_argument("--accumulate_batch", type=bool, default=False, required=False, help="Whether to batch accumulation training strategy.") parser.add_argument("--accumulation_steps", type=int, default=4, required=False, help="For how much batches, update optimizer once.") ```
Sophie-Rain-SpiderMan-Leaked-Videox/Sophie.Rain.Spiderman.Video.Tutorial	Sophie-Rain-SpiderMan-Leaked-Videox	2025-04-28T14:41:04Z	0	0	null	[ "region:us" ]	null	2025-04-28T14:40:41Z	<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a> <a href="https://sdu.sk/9Ip" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a> <a href="https://sdu.sk/9Ip" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
Aasdasd/test123	Aasdasd	2025-04-28T14:39:48Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "conversational", "en", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-1.5B", "base_model:finetune:Qwen/Qwen2.5-1.5B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T14:37:18Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-1.5B tags: - chat library_name: transformers --- # Qwen2.5-1.5B-Instruct ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the instruction-tuned 1.5B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 1.54B - Number of Paramaters (Non-Embedding): 1.31B - Number of Layers: 28 - Number of Attention Heads (GQA): 12 for Q and 2 for KV - Context Length: Full 32,768 tokens and generation 8192 tokens For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-1.5B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
Masterd2025/llama-3-malaria-severity-fine-turned-1	Masterd2025	2025-04-28T14:39:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T14:39:14Z	--- 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: Masterd2025 - 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)
Asgar1993/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-regal_slimy_cow	Asgar1993	2025-04-28T14:37:52Z	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am regal slimy cow", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-09T09:42:15Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-regal_slimy_cow tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am regal slimy cow - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-regal_slimy_cow This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Asgar1993/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-regal_slimy_cow", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
poolespike/penny	poolespike	2025-04-28T14:37:40Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-28T13:42:42Z	--- 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 ---
Nexesenex/Llama_3.x_70b_Doppelectra_v2	Nexesenex	2025-04-28T14:35:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Steelskull/L3.3-Electra-R1-70b", "base_model:merge:Steelskull/L3.3-Electra-R1-70b", "base_model:nbeerbower/Llama3.1-Gutenberg-Doppel-70B", "base_model:merge:nbeerbower/Llama3.1-Gutenberg-Doppel-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:06:33Z	--- base_model: - nbeerbower/Llama3.1-Gutenberg-Doppel-70B - Steelskull/L3.3-Electra-R1-70b library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Arcee Fusion](https://arcee.ai) merge method using [nbeerbower/Llama3.1-Gutenberg-Doppel-70B](https://huggingface.co/nbeerbower/Llama3.1-Gutenberg-Doppel-70B) as a base. ### Models Merged The following models were included in the merge: * [Steelskull/L3.3-Electra-R1-70b](https://huggingface.co/Steelskull/L3.3-Electra-R1-70b) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: arcee_fusion models: - model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B parameters: weight: 1.0 - model: Steelskull/L3.3-Electra-R1-70b parameters: weight: 1.0 base_model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B dtype: float32 out_dtype: bfloat16 parameters: int8_mask: true normalize: true chat_template: auto tokenizer: source: union ```
calebnwokocha/jarvis	calebnwokocha	2025-04-28T14:32:45Z	0	0	null	[ "code", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-72B-Instruct", "base_model:finetune:Qwen/Qwen2.5-72B-Instruct", "doi:10.57967/hf/3363", "license:mit", "region:us" ]	text-generation	2024-10-28T05:11:58Z	--- license: mit language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-72B-Instruct pipeline_tag: text-generation tags: - code --- jarvis source code: https://github.com/calebnwokocha/jarvis
li1212/opus-mt-en-zh-finetuned-wmt19-accelerate-tf	li1212	2025-04-28T14:17:42Z	0	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:li1212/opus-mt-en-zh-finetuned-wmt19-accelerate", "base_model:finetune:li1212/opus-mt-en-zh-finetuned-wmt19-accelerate", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-28T12:16:55Z	--- library_name: transformers base_model: li1212/opus-mt-en-zh-finetuned-wmt19-accelerate tags: - generated_from_keras_callback model-index: - name: li1212/opus-mt-en-zh-finetuned-wmt19-accelerate-tf results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # li1212/opus-mt-en-zh-finetuned-wmt19-accelerate-tf This model is a fine-tuned version of [li1212/opus-mt-en-zh-finetuned-wmt19-accelerate](https://huggingface.co/li1212/opus-mt-en-zh-finetuned-wmt19-accelerate) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5901 - Validation Loss: 1.7468 - Epoch: 1 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 16875, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.7955 \| 1.7727 \| 0 \| \| 1.5901 \| 1.7468 \| 1 \| ### Framework versions - Transformers 4.51.1 - TensorFlow 2.18.0 - Datasets 3.5.0 - Tokenizers 0.21.0
edwingitau101/Qr_estate	edwingitau101	2025-04-28T14:13:46Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T14:13:46Z	--- license: apache-2.0 ---
Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF	Triangle104	2025-04-28T14:08:59Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v3", "base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v3", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T14:05:45Z	--- base_model: ArliAI/QwQ-32B-ArliAI-RpR-v3 language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo thumbnail: https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/coilCTGeL0OUYr9PA9zna.jpeg --- # Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF This model was converted to GGUF format from [`ArliAI/QwQ-32B-ArliAI-RpR-v3`](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v3) 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/ArliAI/QwQ-32B-ArliAI-RpR-v3) for more details on the model. --- RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series. RpR models use the same curated, deduplicated RP and creative writing dataset used for RPMax, with a focus on variety to ensure high creativity and minimize cross-context repetition. Users familiar with RPMax will recognize the unique, non-repetitive writing style unlike other finetuned-for-RP models. With the release of QwQ as the first high performing open-source reasoning model that can be easily trained, it was clear that the available instruct and creative writing reasoning datasets contains only one response per example. This is type of single response dataset used for training reasoning models causes degraded output quality in long multi-turn chats. Which is why Arli AI decided to create a real RP model capable of long multi-turn chat with reasoning. In order to create RpR, we first had to actually create the reasoning RP dataset by re-processing our existing known-good RPMax dataset into a reasoning dataset. This was possible by using the base QwQ Instruct model itself to create the reasoning process for every turn in the RPMax dataset conversation examples, which is then further refined in order to make sure the reasoning is in-line with the actual response examples from the dataset. Another important thing to get right is to make sure the model is trained on examples that present reasoning blocks in the same way as it encounters it during inference. Which is, never seeing the reasoning blocks in it's context. In order to do this, the training run was completed using axolotl with manual template-free segments dataset in order to make sure that the model is never trained to see the reasoning block in the context. Just like how the model will be used during inference time. The result of training QwQ on this dataset with this method are consistently coherent and interesting outputs even in long multi-turn RP chats. This is as far as we know the first true correctly-trained reasoning model trained for RP and creative writing. You can access the model at https://arliai.com and we also have a models ranking page at https://www.arliai.com/models-ranking Ask questions in our new Discord Server https://discord.com/invite/t75KbPgwhk or on our subreddit https://www.reddit.com/r/ArliAI/ --- Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF --hf-file qwq-32b-arliai-rpr-v3-q3_k_l.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF --hf-file qwq-32b-arliai-rpr-v3-q3_k_l.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF --hf-file qwq-32b-arliai-rpr-v3-q3_k_l.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v3-Q3_K_L-GGUF --hf-file qwq-32b-arliai-rpr-v3-q3_k_l.gguf -c 2048 ```
dougssa/teste	dougssa	2025-04-28T14:08:00Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2025-04-28T14:06:50Z	--- base_model: mistralai/Mistral-7B-v0.1 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 0.14.0
SamsungSAILMontreal/ByteCraft	SamsungSAILMontreal	2025-04-28T14:00:23Z	8	10	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-19T14:55:15Z	--- license: mit license_link: https://huggingface.co/SamsungSAILMontreal/ByteCraft/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-7B-Instruct tags: - chat library_name: transformers --- # ByteCraft ByteCraft is the world's first generative model of SWF video games and animations through bytes conditional on prompt. <p align="center"> <img src="https://emygervais.github.io/assets/images/screenshots.png" width="80%"/> <p> For more details, please refer to our [Blog](https://emygervais.github.io/2025/03/15/bytecraft.html), [Paper/Tech-report](https://github.com/SamsungSAILMontreal/ByteCraft/blob/main/paper/ByteCraft.pdf), and [Inference Code](https://github.com/SamsungSAILMontreal/ByteCraft). # Reference If you find our work useful, please consider citing: ```bib @article{202503.1962, doi = {10.20944/preprints202503.1962.v1}, url = {https://www.preprints.org/manuscript/202503.1962/v1}, year = 2025, month = {March}, publisher = {Preprints}, author = {Alexia Jolicoeur-Martineau and Emy Gervais}, title = {ByteCraft: Generating Video Games and Animations Through Bytes}, journal = {Preprints} } ```
altkachenko11/gpt2-finetuned	altkachenko11	2025-04-28T13:56:30Z	23	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-24T16:38:20Z	--- library_name: transformers license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-finetuned This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.1998 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cpu - Datasets 3.5.0 - Tokenizers 0.21.1
MaestrAI/elara-lora-1745847739	MaestrAI	2025-04-28T13:54:08Z	0	0	null	[ "region:us" ]	null	2025-04-28T13:42:18Z	# elara LORA Model This is a LORA model for character Elara Created at 2025-04-28 15:42:25
fhaslam/Llama-3.2-1B-Financial-Sentiment19	fhaslam	2025-04-28T13:53:00Z	0	0	transformers	[ "transformers", "safetensors", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T13:52:49Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
anrbk/pdf-word	anrbk	2025-04-28T13:50:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T13:50:27Z	--- license: apache-2.0 ---
DevQuasar/INSAIT-Institute.MamayLM-Gemma-2-9B-IT-v0.1-GGUF	DevQuasar	2025-04-28T13:50:00Z	0	0	null	[ "text-generation", "base_model:INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1", "base_model:finetune:INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1", "region:us" ]	text-generation	2025-04-28T13:43:04Z	--- base_model: - INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1 pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1](https://huggingface.co/INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
danyush/qwen2.5_vl_3B_virat_lr_r4	danyush	2025-04-28T13:49:40Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "llama-factory", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-28T13:42:10Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BootesVoid/cm92u8ajv0000yy2pmswoj77u_cma13wc0p009i12tvp1k3l1di	BootesVoid	2025-04-28T13:44:26Z	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	2025-04-28T13:44:24Z	--- 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: GRMGRL --- # Cm92U8Ajv0000Yy2Pmswoj77U_Cma13Wc0P009I12Tvp1K3L1Di <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 `GRMGRL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "GRMGRL", "lora_weights": "https://huggingface.co/BootesVoid/cm92u8ajv0000yy2pmswoj77u_cma13wc0p009i12tvp1k3l1di/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cm92u8ajv0000yy2pmswoj77u_cma13wc0p009i12tvp1k3l1di', weight_name='lora.safetensors') image = pipeline('GRMGRL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cm92u8ajv0000yy2pmswoj77u_cma13wc0p009i12tvp1k3l1di/discussions) to add images that show off what you’ve made with this LoRA.
daishen/openfin-3B-ZH-optimal-sft_lxl3129_audit_regulation	daishen	2025-04-28T13:43:33Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T10:58:32Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
KhaledLakhdher/finetuned	KhaledLakhdher	2025-04-28T13:40:15Z	12	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-04-21T11:47:40Z	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: finetuned 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 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4653 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.7128 \| 8.3137 \| 100 \| 0.5822 \| \| 0.5591 \| 16.6275 \| 200 \| 0.4858 \| \| 0.5278 \| 24.9412 \| 300 \| 0.4698 \| \| 0.5222 \| 33.3137 \| 400 \| 0.4682 \| \| 0.5103 \| 41.6275 \| 500 \| 0.4653 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
talha23527/DeepSeek-R1-Distill-Llama-8B	talha23527	2025-04-28T13:39:39Z	36	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "text-classification", "en", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "base_model:quantized:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-classification	2025-03-25T06:59:14Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en pipeline_tag: text-classification --- # Uploaded model - Developed by: talha23527 - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
EunjiChoi/train_250428	EunjiChoi	2025-04-28T13:29:37Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T13:28:12Z	--- license: apache-2.0 ---
Ederson13/donut-cord-v2-menu-sample-demo	Ederson13	2025-04-28T13:28:33Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-28T11:15:14Z	--- 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]
ail-sa/rahul_test	ail-sa	2025-04-28T13:26:26Z	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	2025-04-28T12:54:47Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Sid --- # Rahul_Test <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/rahul_test/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ail-sa/rahul_test', weight_name='lora.safetensors') image = pipeline('Sid').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/rahul_test/discussions) to add images that show off what you’ve made with this LoRA.
prashantbhoutika1989/flux-dev-lora	prashantbhoutika1989	2025-04-28T13:23:33Z	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	2025-04-28T12:49:47Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Prashant --- # Flux Dev Lora <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 `Prashant` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Prashant", "lora_weights": "https://huggingface.co/prashantbhoutika1989/flux-dev-lora/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('prashantbhoutika1989/flux-dev-lora', weight_name='lora.safetensors') image = pipeline('Prashant').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: 1500 - Learning rate: 0.0004 - LoRA rank: 60 ## Contribute your own examples You can use the [community tab](https://huggingface.co/prashantbhoutika1989/flux-dev-lora/discussions) to add images that show off what you’ve made with this LoRA.
nathanialhunt2000/a769678d-d022-408e-a4ce-a566fef75893	nathanialhunt2000	2025-04-28T13:21:29Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2025-04-28T13:21:11Z	--- library_name: peft tags: - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: nathanialhunt2000/a769678d-d022-408e-a4ce-a566fef75893 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. --> # nathanialhunt2000/a769678d-d022-408e-a4ce-a566fef75893 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8708 ## 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
Tung177/KLTN_Qwen2.5-7B-Instruct-SS-lr0.0002e3r16bs64	Tung177	2025-04-28T13:21:27Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-04-28T13:20:16Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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 0.14.0
RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf	RichardErkhov	2025-04-28T13:17:43Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T11:52:30Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) hp_ablations_qwen_adambeta2_0.99 - GGUF - Model creator: https://huggingface.co/mlfoundations-dev/ - Original model: https://huggingface.co/mlfoundations-dev/hp_ablations_qwen_adambeta2_0.99/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [hp_ablations_qwen_adambeta2_0.99.Q2_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q2_K.gguf) \| Q2_K \| 2.81GB \| \| [hp_ablations_qwen_adambeta2_0.99.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.IQ3_XS.gguf) \| IQ3_XS \| 3.12GB \| \| [hp_ablations_qwen_adambeta2_0.99.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.IQ3_S.gguf) \| IQ3_S \| 3.26GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q3_K_S.gguf) \| Q3_K_S \| 3.25GB \| \| [hp_ablations_qwen_adambeta2_0.99.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.IQ3_M.gguf) \| IQ3_M \| 3.33GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q3_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q3_K.gguf) \| Q3_K \| 3.55GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q3_K_M.gguf) \| Q3_K_M \| 3.55GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q3_K_L.gguf) \| Q3_K_L \| 3.81GB \| \| [hp_ablations_qwen_adambeta2_0.99.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.IQ4_XS.gguf) \| IQ4_XS \| 3.96GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q4_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q4_0.gguf) \| Q4_0 \| 4.13GB \| \| [hp_ablations_qwen_adambeta2_0.99.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.IQ4_NL.gguf) \| IQ4_NL \| 4.16GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q4_K_S.gguf) \| Q4_K_S \| 4.15GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q4_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q4_K.gguf) \| Q4_K \| 4.36GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q4_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q4_1.gguf) \| Q4_1 \| 4.54GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q5_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q5_0.gguf) \| Q5_0 \| 4.95GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q5_K_S.gguf) \| Q5_K_S \| 4.95GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q5_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q5_K.gguf) \| Q5_K \| 5.07GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q5_K_M.gguf) \| Q5_K_M \| 5.07GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q5_1.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q5_1.gguf) \| Q5_1 \| 5.36GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q6_K.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q6_K.gguf) \| Q6_K \| 5.82GB \| \| [hp_ablations_qwen_adambeta2_0.99.Q8_0.gguf](https://huggingface.co/RichardErkhov/mlfoundations-dev_-_hp_ablations_qwen_adambeta2_0.99-gguf/blob/main/hp_ablations_qwen_adambeta2_0.99.Q8_0.gguf) \| Q8_0 \| 7.54GB \| Original model description: --- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B tags: - llama-factory - full - generated_from_trainer model-index: - name: hp_ablations_qwen_adambeta2_0.99 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. --> # hp_ablations_qwen_adambeta2_0.99 This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the mlfoundations-dev/oh-dcft-v3.1-gpt-4o-mini dataset. It achieves the following results on the evaluation set: - Loss: 0.6187 ## 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 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.99) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 1738 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6345 \| 0.9983 \| 438 \| 0.6252 \| \| 0.5962 \| 1.9994 \| 877 \| 0.6187 \| \| 0.5752 \| 2.9960 \| 1314 \| 0.6187 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.3.0 - Datasets 3.0.2 - Tokenizers 0.20.3
Triangle104/Athena-3.5-7B-Q6_K-GGUF	Triangle104	2025-04-28T13:17:13Z	0	0	transformers	[ "transformers", "gguf", "unsloth", "trl", "sft", "llama-cpp", "gguf-my-repo", "base_model:Spestly/Athena-3.5-7B", "base_model:quantized:Spestly/Athena-3.5-7B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T13:16:43Z	--- base_model: Spestly/Athena-3.5-7B library_name: transformers tags: - unsloth - trl - sft - llama-cpp - gguf-my-repo --- # Triangle104/Athena-3.5-7B-Q6_K-GGUF This model was converted to GGUF format from [`Spestly/Athena-3.5-7B`](https://huggingface.co/Spestly/Athena-3.5-7B) 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/Spestly/Athena-3.5-7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Athena-3.5-7B-Q6_K-GGUF --hf-file athena-3.5-7b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Athena-3.5-7B-Q6_K-GGUF --hf-file athena-3.5-7b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Athena-3.5-7B-Q6_K-GGUF --hf-file athena-3.5-7b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Athena-3.5-7B-Q6_K-GGUF --hf-file athena-3.5-7b-q6_k.gguf -c 2048 ```
Amfavoured247/powo	Amfavoured247	2025-04-28T12:25:07Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-28T12:25:02Z	--- license: other license_name: praiseotah license_link: LICENSE ---
AdoCleanCode/real_model_cifar10_complex_v1	AdoCleanCode	2025-04-28T12:24:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T10:21:42Z	--- library_name: transformers license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: real_model_cifar10_complex_v1 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. --> # real_model_cifar10_complex_v1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3563 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.4935 \| 1.0 \| 3000 \| 0.4303 \| \| 0.4272 \| 2.0 \| 6000 \| 0.3854 \| \| 0.3997 \| 3.0 \| 9000 \| 0.3704 \| \| 0.386 \| 4.0 \| 12000 \| 0.3605 \| \| 0.375 \| 5.0 \| 15000 \| 0.3563 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.4.1+cu121 - Datasets 2.19.1 - Tokenizers 0.20.3
shubhamprshr/Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_gaussian_0.5_0.5_True_300	shubhamprshr	2025-04-28T12:23:44Z	10	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "dataset:blocksworld-dataset", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-18T20:27:16Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: blocksworld-dataset library_name: transformers model_name: Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_gaussian_0.5_0.5_True_300 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_gaussian_0.5_0.5_True_300 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [blocksworld-dataset](https://huggingface.co/datasets/blocksworld-dataset) 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="shubhamprshr/Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_gaussian_0.5_0.5_True_300", 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/shubhamprshr27-tamu/BW2/runs/atbzi39b) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.14.0 - Transformers: 4.48.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
kingabzpro/mistral_7b_guanaco	kingabzpro	2025-04-28T12:21:46Z	88	3	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "pretrained", "en", "dataset:mlabonne/guanaco-llama2-1k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-25T14:14:04Z	--- license: apache-2.0 datasets: - mlabonne/guanaco-llama2-1k pipeline_tag: text-generation language: - en tags: - pretrained inference: parameters: temperature: 0.7 --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0 - PEFT 0.5.0
Triangle104/Holland-4B-V1-Q4_K_M-GGUF	Triangle104	2025-04-28T12:18:43Z	0	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:PJMixers/lodrick-the-lafted_OpusStories-ShareGPT", "dataset:NewEden/Gryphe-3.5-16k-Subset", "dataset:Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned", "base_model:Delta-Vector/Holland-4B-V1", "base_model:quantized:Delta-Vector/Holland-4B-V1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T12:17:18Z	--- base_model: Delta-Vector/Holland-4B-V1 datasets: - anthracite-org/kalo-opus-instruct-22k-no-refusal - PJMixers/lodrick-the-lafted_OpusStories-ShareGPT - NewEden/Gryphe-3.5-16k-Subset - Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned license: mit tags: - chat - llama-cpp - gguf-my-repo Language: - En Pipeline_tag: text-generation Base_model: nvidia/Llama-3.1-Minitron-4 B-Width-Base Tags: - Chat --- # Triangle104/Holland-4B-V1-Q4_K_M-GGUF This model was converted to GGUF format from [`Delta-Vector/Holland-4B-V1`](https://huggingface.co/Delta-Vector/Holland-4B-V1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Delta-Vector/Holland-4B-V1) for more details on the model. --- A model made to continue off previous work on Magnum 4B, A small model made for creative writing / General assistant tasks, finetuned ontop of IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml, this model is made to be more coherent and generally be better then the 4B at both writing and assistant tasks. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Holland-4B-V1-Q4_K_M-GGUF --hf-file holland-4b-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Holland-4B-V1-Q4_K_M-GGUF --hf-file holland-4b-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Holland-4B-V1-Q4_K_M-GGUF --hf-file holland-4b-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Holland-4B-V1-Q4_K_M-GGUF --hf-file holland-4b-v1-q4_k_m.gguf -c 2048 ```
Kenazin/Llama-3.1-8B-peft-p-tuning-v4-9	Kenazin	2025-04-28T12:18:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T12:17:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
dgambettaphd/M_llm2_gen2_run0_S_doc1000_synt64_tot128_SYNLAST	dgambettaphd	2025-04-28T12:15:14Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T12:14:50Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
majesticlindsay/majesticlindsay21	majesticlindsay	2025-04-28T12:14:57Z	0	0	null	[ "license:bsd-2-clause", "region:us" ]	null	2025-04-28T12:14:53Z	--- license: bsd-2-clause ---
mlfoundations-dev/opencodereasoning_3k	mlfoundations-dev	2025-04-28T12:11:17Z	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	2025-04-28T12:08:14Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: opencodereasoning_3k 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. --> # opencodereasoning_3k 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/opencodereasoning_3k 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: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 24 - total_train_batch_size: 96 - 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.0a0+b465a5843b.nv24.09 - Datasets 3.5.0 - Tokenizers 0.20.3
ikkiren/research_TS_corr	ikkiren	2025-04-28T12:11:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:42:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF	mradermacher	2025-04-28T12:08:07Z	53	2	transformers	[ "transformers", "gguf", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:nbeerbower/gutenberg2-dpo", "base_model:nbeerbower/Qwen2.5-Gutenberg-Doppel-14B", "base_model:quantized:nbeerbower/Qwen2.5-Gutenberg-Doppel-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-11T23:15:31Z	--- base_model: nbeerbower/Qwen2.5-Gutenberg-Doppel-14B datasets: - jondurbin/gutenberg-dpo-v0.1 - nbeerbower/gutenberg2-dpo language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nbeerbower/Qwen2.5-Gutenberg-Doppel-14B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-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/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 8.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Gutenberg-Doppel-14B-GGUF/resolve/main/Qwen2.5-Gutenberg-Doppel-14B.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 -->
ApacheOne/liveportrait	ApacheOne	2025-04-28T12:04:04Z	0	0	null	[ "onnx", "custom", "code", "en", "region:us" ]	null	2025-02-22T07:51:21Z	--- language: - en tags: - code --- ## ComfyUI-liveportrait-models For KJnodes in ComfyUI, he got the animal models to work easy on windows. More safe for everyone to share around then the google drive. (the human liveportrait models are still SOTA but the animal models are from a 3rd party.)
mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF	mradermacher	2025-04-28T12:03:18Z	72	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130", "base_model:quantized:LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-12-01T13:15:29Z	--- base_model: LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-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/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130-i1-GGUF/resolve/main/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
LuckyLukke/grpo_onesided_1_starter_change-320	LuckyLukke	2025-04-28T12:00:18Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:57:48Z	--- 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]
dzanbek/ea5a3b64-e495-4fc7-80c6-2b9e9c35310e	dzanbek	2025-04-28T11:59:29Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:tiiuae/Falcon3-1B-Base", "base_model:adapter:tiiuae/Falcon3-1B-Base", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T11:55:32Z	--- library_name: peft license: other base_model: tiiuae/Falcon3-1B-Base tags: - axolotl - generated_from_trainer model-index: - name: ea5a3b64-e495-4fc7-80c6-2b9e9c35310e 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: tiiuae/Falcon3-1B-Base bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 6cc0fe21f0332fa7_train_data.json ds_type: json format: custom path: /workspace/input_data/6cc0fe21f0332fa7_train_data.json type: field_instruction: question field_output: answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/ea5a3b64-e495-4fc7-80c6-2b9e9c35310e hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/6cc0fe21f0332fa7_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: 72923dc5-5ef8-423a-919a-0a486181f7ff wandb_project: s56-2 wandb_run: your_name wandb_runid: 72923dc5-5ef8-423a-919a-0a486181f7ff warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # ea5a3b64-e495-4fc7-80c6-2b9e9c35310e This model is a fine-tuned version of [tiiuae/Falcon3-1B-Base](https://huggingface.co/tiiuae/Falcon3-1B-Base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6014 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5658 \| 0.2128 \| 200 \| 0.6014 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Qwenvergence-14B-v3-Prose-GGUF	mradermacher	2025-04-28T11:58:08Z	78	3	transformers	[ "transformers", "gguf", "mergekit", "merge", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:sometimesanotion/Qwenvergence-14B-v3-Prose", "base_model:quantized:sometimesanotion/Qwenvergence-14B-v3-Prose", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-21T10:49:51Z	--- base_model: sometimesanotion/Qwenvergence-14B-v3-Prose 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: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/sometimesanotion/Qwenvergence-14B-v3-Prose <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-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/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwenvergence-14B-v3-Prose-GGUF/resolve/main/Qwenvergence-14B-v3-Prose.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 -->
data-skills/lora_model	data-skills	2025-04-28T11:57:32Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:57:21Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: data-skills - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-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)
phospho-app/so100_Orange2Green-dwrw27gxja	phospho-app	2025-04-28T11:57:00Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-04-28T09:49:00Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [RasmusP/so100_Orange2Green](https://huggingface.co/datasets/RasmusP/so100_Orange2Green) - Wandb run URL: None - Epochs: 10 - Batch size: 64 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
safouene99999/Mistral_7b_Cyber_Instruct	safouene99999	2025-04-28T11:54:46Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:54:22Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: safouene99999 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral 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)
Flo0620/Qwen2_5_7B_r128_a128_d0_1_lr2e-4_const	Flo0620	2025-04-28T11:53:47Z	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	2025-04-28T08:33:51Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: transformers model_name: Qwen2_5_7B_r128_a128_d0_1_lr2e-4_const tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2_5_7B_r128_a128_d0_1_lr2e-4_const This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Flo0620/Qwen2_5_7B_r128_a128_d0_1_lr2e-4_const", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mideind/bytemistral-isl-60m	mideind	2025-04-28T11:51:42Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "bytemistral", "isl", "60m", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:49:14Z	--- library_name: transformers tags: - bytemistral - isl - 60m --- # 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]
Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_3	Detomo	2025-04-28T11:51:10Z	16	0	sentence-transformers	[ "sentence-transformers", "onnx", "safetensors", "openvino", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:11961", "loss:CustomBatchAllTripletLoss", "arxiv:1908.10084", "arxiv:1703.07737", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-28T00:26:42Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:11961 - loss:CustomBatchAllTripletLoss widget: - source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ（地上部）。 sentences: - 科目：タイル。名称：ｱﾌﾟﾛｰﾁﾃﾗｽ床床ﾀｲﾙA。 - 科目：ユニット及びその他。名称：通用口ｻｲﾝ。 - 科目：コンクリート。名称：構造体強度補正。 - source_sentence: 科目：タイル。名称：床ﾀｲﾙC。 sentences: - 科目：ユニット及びその他。名称：市章･国旗ｻｲﾝ。 - 科目：ユニット及びその他。名称：ﾊﾞｯｸﾔｰﾄﾞ室名ｻｲﾝ。 - 科目：ユニット及びその他。名称：P-#市章･国旗ｻｲﾝ。 - source_sentence: 科目：ユニット及びその他。名称：B-#立入禁止ｻｲﾝ。 sentences: - 科目：ユニット及びその他。名称：Co-#入口ｻｲﾝ。 - 科目：ユニット及びその他。名称：#~#F一般EVﾎｰﾙｶｳﾝﾀｰ。 - 科目：ユニット及びその他。名称： MWC､WWC姿見鏡。 - source_sentence: 科目：ユニット及びその他。名称：#FNICUｶｳﾝﾀｰ。 sentences: - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC33+ΔS（構造体補正）S15　粗骨材20AE減水剤遅延型･防水剤入。備考：刊-ｺﾝ 3315TB基礎部ﾏｽｺﾝ。 - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S15粗骨材基礎部。備考：代価表 0065。 - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ（個別）。摘要：Ｆ0＝24N/mm2 Ｓ＝１５徳島１。備考：B1-111111 H2906BD 個別基礎部躯体ｺﾝｸﾘｰﾄ。 - source_sentence: 科目：ユニット及びその他。名称：HWC荷物棚。 sentences: - 科目：コンクリート。名称：地上部暑中ｺﾝｸﾘｰﾄ。 - 科目：タイル。名称：階段蹴上ﾀｲﾙP。 - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC=36 S18粗骨材20 高性能AE減水剤。備考：刊ｺﾝｸﾘｰﾄ 2。 pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}) ) ``` ## 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("Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_3") # Run inference sentences = [ '科目：ユニット及びその他。名称：HWC荷物棚。', '科目：コンクリート。名称：地上部暑中ｺﾝｸﾘｰﾄ。', '科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：JIS A5308 FC=36 S18粗骨材20 高性能AE減水剤。備考：刊ｺﾝｸﾘｰﾄ 2。', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 11,961 training samples * Columns: <code>sentence</code> and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence \| label \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| type \| string \| int \| \| details \| <ul><li>min: 11 tokens</li><li>mean: 18.2 tokens</li><li>max: 54 tokens</li></ul> \| <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.30%</li><li>5: ~1.10%</li><li>6: ~0.30%</li><li>7: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.30%</li><li>11: ~0.30%</li><li>12: ~0.30%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.30%</li><li>16: ~0.40%</li><li>17: ~0.30%</li><li>18: ~0.30%</li><li>19: ~0.30%</li><li>20: ~0.90%</li><li>21: ~0.30%</li><li>22: ~0.40%</li><li>23: ~0.30%</li><li>24: ~1.10%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.30%</li><li>30: ~0.30%</li><li>31: ~0.30%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.30%</li><li>41: ~0.30%</li><li>42: ~0.40%</li><li>43: ~0.30%</li><li>44: ~0.30%</li><li>45: ~0.30%</li><li>46: ~0.60%</li><li>47: ~0.70%</li><li>48: ~0.30%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.30%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.30%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.30%</li><li>61: ~0.50%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.30%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.30%</li><li>70: ~0.30%</li><li>71: ~0.30%</li><li>72: ~0.30%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.80%</li><li>78: ~0.60%</li><li>79: ~0.30%</li><li>80: ~0.30%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.30%</li><li>84: ~0.30%</li><li>85: ~0.30%</li><li>86: ~0.50%</li><li>87: ~0.30%</li><li>88: ~0.30%</li><li>89: ~0.30%</li><li>90: ~0.30%</li><li>91: ~0.80%</li><li>92: ~0.60%</li><li>93: ~0.50%</li><li>94: ~0.30%</li><li>95: ~0.30%</li><li>96: ~16.50%</li><li>97: ~0.30%</li><li>98: ~0.30%</li><li>99: ~0.30%</li><li>100: ~0.30%</li><li>101: ~0.30%</li><li>102: ~0.30%</li><li>103: ~0.30%</li><li>104: ~0.30%</li><li>105: ~0.50%</li><li>106: ~0.30%</li><li>107: ~0.30%</li><li>108: ~0.30%</li><li>109: ~0.30%</li><li>110: ~0.30%</li><li>111: ~0.30%</li><li>112: ~0.30%</li><li>113: ~0.30%</li><li>114: ~0.70%</li><li>115: ~0.30%</li><li>116: ~0.30%</li><li>117: ~0.30%</li><li>118: ~0.40%</li><li>119: ~2.10%</li><li>120: ~2.10%</li><li>121: ~0.30%</li><li>122: ~0.30%</li><li>123: ~0.50%</li><li>124: ~0.50%</li><li>125: ~0.50%</li><li>126: ~0.40%</li><li>127: ~0.30%</li><li>128: ~0.30%</li><li>129: ~0.30%</li><li>130: ~0.80%</li><li>131: ~0.30%</li><li>132: ~0.30%</li><li>133: ~0.30%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.50%</li><li>144: ~0.30%</li><li>145: ~0.40%</li><li>146: ~0.30%</li><li>147: ~0.30%</li><li>148: ~0.30%</li><li>149: ~0.30%</li><li>150: ~0.30%</li><li>151: ~0.30%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~0.30%</li><li>155: ~0.30%</li><li>156: ~0.30%</li><li>157: ~0.40%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~0.30%</li><li>161: ~0.30%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.70%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~1.30%</li><li>169: ~0.30%</li><li>170: ~0.40%</li><li>171: ~0.30%</li><li>172: ~0.30%</li><li>173: ~0.30%</li><li>174: ~1.50%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~0.30%</li><li>178: ~0.30%</li><li>179: ~0.30%</li><li>180: ~0.30%</li><li>181: ~0.30%</li><li>182: ~1.60%</li><li>183: ~0.30%</li><li>184: ~0.30%</li><li>185: ~7.20%</li><li>186: ~0.30%</li><li>187: ~1.00%</li><li>188: ~0.30%</li><li>189: ~0.30%</li><li>190: ~0.30%</li><li>191: ~1.80%</li><li>192: ~0.30%</li><li>193: ~0.50%</li><li>194: ~0.70%</li><li>195: ~0.30%</li></ul> \| * Samples: \| sentence \| label \| \|:-----------------------------------------\|:---------------\| \| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> \| <code>0</code> \| \| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> \| <code>0</code> \| \| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> \| <code>0</code> \| * Loss: <code>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code> ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 512 - `per_device_eval_batch_size`: 512 - `learning_rate`: 1e-05 - `weight_decay`: 0.01 - `num_train_epochs`: 200 - `warmup_ratio`: 0.15 - `fp16`: True - `batch_sampler`: group_by_label #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 512 - `per_device_eval_batch_size`: 512 - `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`: 1e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 200 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.15 - `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`: True - `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`: group_by_label - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| \|:--------:\|:----:\|:-------------:\| \| 2.3333 \| 50 \| 0.0589 \| \| 4.6667 \| 100 \| 0.0668 \| \| 7.125 \| 150 \| 0.0677 \| \| 9.4583 \| 200 \| 0.0655 \| \| 11.7917 \| 250 \| 0.062 \| \| 14.25 \| 300 \| 0.0601 \| \| 16.5833 \| 350 \| 0.0604 \| \| 19.0417 \| 400 \| 0.0602 \| \| 21.375 \| 450 \| 0.0546 \| \| 23.7083 \| 500 \| 0.0575 \| \| 26.1667 \| 550 \| 0.0569 \| \| 28.5 \| 600 \| 0.0533 \| \| 30.8333 \| 650 \| 0.0527 \| \| 33.2917 \| 700 \| 0.0518 \| \| 35.625 \| 750 \| 0.0487 \| \| 38.0833 \| 800 \| 0.0514 \| \| 40.4167 \| 850 \| 0.0469 \| \| 42.75 \| 900 \| 0.0464 \| \| 45.2083 \| 950 \| 0.0481 \| \| 47.5417 \| 1000 \| 0.0502 \| \| 49.875 \| 1050 \| 0.0511 \| \| 52.3333 \| 1100 \| 0.0449 \| \| 54.6667 \| 1150 \| 0.0439 \| \| 57.125 \| 1200 \| 0.0443 \| \| 59.4583 \| 1250 \| 0.0445 \| \| 61.7917 \| 1300 \| 0.0455 \| \| 64.25 \| 1350 \| 0.0417 \| \| 66.5833 \| 1400 \| 0.0397 \| \| 69.0417 \| 1450 \| 0.0392 \| \| 71.375 \| 1500 \| 0.0411 \| \| 73.7083 \| 1550 \| 0.0375 \| \| 76.1667 \| 1600 \| 0.0444 \| \| 78.5 \| 1650 \| 0.0353 \| \| 80.8333 \| 1700 \| 0.0402 \| \| 83.2917 \| 1750 \| 0.0353 \| \| 85.625 \| 1800 \| 0.0354 \| \| 88.0833 \| 1850 \| 0.0347 \| \| 90.4167 \| 1900 \| 0.0368 \| \| 92.75 \| 1950 \| 0.0353 \| \| 95.2083 \| 2000 \| 0.0374 \| \| 97.5417 \| 2050 \| 0.0375 \| \| 99.875 \| 2100 \| 0.0324 \| \| 1.7576 \| 50 \| 0.0365 \| \| 3.7576 \| 100 \| 0.0372 \| \| 5.7576 \| 150 \| 0.0392 \| \| 7.7576 \| 200 \| 0.0392 \| \| 9.7576 \| 250 \| 0.0386 \| \| 11.7576 \| 300 \| 0.0402 \| \| 13.7576 \| 350 \| 0.0342 \| \| 15.7576 \| 400 \| 0.037 \| \| 17.7576 \| 450 \| 0.0355 \| \| 19.7576 \| 500 \| 0.0341 \| \| 21.7576 \| 550 \| 0.0354 \| \| 23.7576 \| 600 \| 0.0322 \| \| 25.7576 \| 650 \| 0.0361 \| \| 27.7576 \| 700 \| 0.0316 \| \| 29.7576 \| 750 \| 0.0338 \| \| 31.7576 \| 800 \| 0.0311 \| \| 33.7576 \| 850 \| 0.0288 \| \| 35.7576 \| 900 \| 0.0311 \| \| 37.7576 \| 950 \| 0.0307 \| \| 39.7576 \| 1000 \| 0.0288 \| \| 41.7576 \| 1050 \| 0.0324 \| \| 43.7576 \| 1100 \| 0.0276 \| \| 45.7576 \| 1150 \| 0.0304 \| \| 47.7576 \| 1200 \| 0.0267 \| \| 49.7576 \| 1250 \| 0.0272 \| \| 51.7576 \| 1300 \| 0.0269 \| \| 53.7576 \| 1350 \| 0.0264 \| \| 55.7576 \| 1400 \| 0.0324 \| \| 57.7576 \| 1450 \| 0.0278 \| \| 59.7576 \| 1500 \| 0.0315 \| \| 61.7576 \| 1550 \| 0.0285 \| \| 63.7576 \| 1600 \| 0.0241 \| \| 65.7576 \| 1650 \| 0.0288 \| \| 67.7576 \| 1700 \| 0.0263 \| \| 69.7576 \| 1750 \| 0.0295 \| \| 71.7576 \| 1800 \| 0.0238 \| \| 73.7576 \| 1850 \| 0.0214 \| \| 75.7576 \| 1900 \| 0.0281 \| \| 77.7576 \| 1950 \| 0.0269 \| \| 79.7576 \| 2000 \| 0.0268 \| \| 81.7576 \| 2050 \| 0.0242 \| \| 83.7576 \| 2100 \| 0.0226 \| \| 85.7576 \| 2150 \| 0.0249 \| \| 87.7576 \| 2200 \| 0.0254 \| \| 89.7576 \| 2250 \| 0.0226 \| \| 91.7576 \| 2300 \| 0.0181 \| \| 93.7576 \| 2350 \| 0.019 \| \| 95.7576 \| 2400 \| 0.0207 \| \| 97.7576 \| 2450 \| 0.0205 \| \| 99.7576 \| 2500 \| 0.0241 \| \| 101.7576 \| 2550 \| 0.0219 \| \| 103.7576 \| 2600 \| 0.0237 \| \| 105.7576 \| 2650 \| 0.0194 \| \| 107.7576 \| 2700 \| 0.0184 \| \| 109.7576 \| 2750 \| 0.0206 \| \| 111.7576 \| 2800 \| 0.0189 \| \| 113.7576 \| 2850 \| 0.0216 \| \| 115.7576 \| 2900 \| 0.0234 \| \| 117.7576 \| 2950 \| 0.0192 \| \| 119.7576 \| 3000 \| 0.0193 \| \| 121.7576 \| 3050 \| 0.0211 \| \| 123.7576 \| 3100 \| 0.0161 \| \| 125.7576 \| 3150 \| 0.022 \| \| 127.7576 \| 3200 \| 0.0176 \| \| 129.7576 \| 3250 \| 0.0227 \| \| 131.7576 \| 3300 \| 0.0224 \| \| 133.7576 \| 3350 \| 0.0172 \| \| 135.7576 \| 3400 \| 0.0168 \| \| 137.7576 \| 3450 \| 0.0165 \| \| 139.7576 \| 3500 \| 0.016 \| \| 141.7576 \| 3550 \| 0.0143 \| \| 143.7576 \| 3600 \| 0.0165 \| \| 145.7576 \| 3650 \| 0.0202 \| \| 147.7576 \| 3700 \| 0.0118 \| \| 149.7576 \| 3750 \| 0.0163 \| \| 151.7576 \| 3800 \| 0.0188 \| \| 153.7576 \| 3850 \| 0.0137 \| \| 155.7576 \| 3900 \| 0.0172 \| \| 157.7576 \| 3950 \| 0.0175 \| \| 159.7576 \| 4000 \| 0.0204 \| \| 161.7576 \| 4050 \| 0.0175 \| \| 163.7576 \| 4100 \| 0.0169 \| \| 165.7576 \| 4150 \| 0.0184 \| \| 167.7576 \| 4200 \| 0.0176 \| \| 169.7576 \| 4250 \| 0.0102 \| \| 171.7576 \| 4300 \| 0.014 \| \| 173.7576 \| 4350 \| 0.0164 \| \| 175.7576 \| 4400 \| 0.0203 \| \| 177.7576 \| 4450 \| 0.0099 \| \| 179.7576 \| 4500 \| 0.0143 \| \| 181.7576 \| 4550 \| 0.0182 \| \| 183.7576 \| 4600 \| 0.009 \| \| 185.7576 \| 4650 \| 0.0157 \| \| 187.7576 \| 4700 \| 0.015 \| \| 189.7576 \| 4750 \| 0.0168 \| \| 191.7576 \| 4800 \| 0.0172 \| \| 193.7576 \| 4850 \| 0.0154 \| \| 195.7576 \| 4900 \| 0.0162 \| \| 197.7576 \| 4950 \| 0.0143 \| \| 199.7576 \| 5000 \| 0.0156 \| </details> ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 3.4.1 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.5.0 - 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", } ``` #### CustomBatchAllTripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## 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. -->
braigchanin2/fdvbfdgb	braigchanin2	2025-04-28T11:49:50Z	0	0	null	[ "license:bsd-2-clause", "region:us" ]	null	2025-04-28T11:49:50Z	--- license: bsd-2-clause ---
TGDOuXnayRb/ohdoaxbe	TGDOuXnayRb	2025-04-28T11:48:11Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:48:11Z	--- license: apache-2.0 ---
FKCesrDCzzlzF/kshadpcnd	FKCesrDCzzlzF	2025-04-28T11:47:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:47:45Z	--- license: apache-2.0 ---
fhaslam/Llama-3.2-1B-Financial-Sentiment18	fhaslam	2025-04-28T11:39:50Z	0	0	transformers	[ "transformers", "safetensors", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:39:46Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Llama 3.2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). #### Prohibited Uses We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 2. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
mlfoundations-dev/Qwen2.5-7B-Instruct_d1_science_longest	mlfoundations-dev	2025-04-28T11:36:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:33:09Z	--- 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_longest 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_longest 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_longest dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.0a0+b465a5843b.nv24.09 - Datasets 3.5.0 - Tokenizers 0.20.3
AhmedLet/Qwen_0.5_python_codes_GGUF	AhmedLet	2025-04-28T11:35:08Z	114	1	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:mlabonne/FineTome-100k", "dataset:MohamedSaeed-dev/python_dataset_codes", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-27T15:44:28Z	--- base_model: - Qwen/Qwen2.5-0.5B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara datasets: - mlabonne/FineTome-100k - MohamedSaeed-dev/python_dataset_codes new_version: AhmedLet/Qwen_0.5_python_codes_mbpp_GGUF --- # Uploaded model - Developed by: AhmedLet - License: apache-2.0 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)
ChandrilBasu/Temp	ChandrilBasu	2025-04-28T11:30:43Z	0	0	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-28T11:30:36Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: Temp 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 --- # Temp <Gallery /> ## Model description ## Trigger words You should use `Temp` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/ChandrilBasu/Temp/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
BootesVoid/cm9zl2bgc02i9qeqob696fwi7_cma0yktrj006z12tvqw4dcdq9	BootesVoid	2025-04-28T11:23:55Z	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	2025-04-28T11:23:54Z	--- 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: NATURALBEAUTY --- # Cm9Zl2Bgc02I9Qeqob696Fwi7_Cma0Yktrj006Z12Tvqw4Dcdq9 <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 `NATURALBEAUTY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NATURALBEAUTY", "lora_weights": "https://huggingface.co/BootesVoid/cm9zl2bgc02i9qeqob696fwi7_cma0yktrj006z12tvqw4dcdq9/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cm9zl2bgc02i9qeqob696fwi7_cma0yktrj006z12tvqw4dcdq9', weight_name='lora.safetensors') image = pipeline('NATURALBEAUTY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cm9zl2bgc02i9qeqob696fwi7_cma0yktrj006z12tvqw4dcdq9/discussions) to add images that show off what you’ve made with this LoRA.
JohnConnor123/Llama-3.2-3B-Instruct-BNB-8bit	JohnConnor123	2025-04-28T11:23:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-28T11:06:05Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \n“Licensee” or “you” means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\n“Llama 3.2”\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\n“Meta” or “we” means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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This restriction does\ \ not apply to end users of a product or service that incorporates any such multimodal\ \ models.\n\nPlease report any violation of this Policy, software “bug,” or other\ \ problems that could lead to a violation of this Policy through one of the following\ \ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n\ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama\ \ 3.2: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit base_model: meta-llama/Llama-3.2-3B-Instruct --- > ## This quantization was done using the [quantization-benchmark](https://github.com/JohnConnor123/quantization-benchmark) framework ## Bitsandbytes quantization config >{'load_in_8bit': True} ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-3B-Instruct, for use with `transformers` and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-3B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-3B-Instruct --include "original/" --local-dir Llama-3.2-3B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF	mradermacher	2025-04-28T11:21:21Z	211	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B", "base_model:quantized:nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-02-09T04:40:49Z	--- base_model: nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B 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: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-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/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-abliterated-TIES-Qwen2.5-14B-GGUF/resolve/main/EVA-abliterated-TIES-Qwen2.5-14B.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 -->
SpursgoZmy/table-llava-v1.5-7b-hf	SpursgoZmy	2025-04-28T11:18:23Z	0	0	null	[ "safetensors", "llava", "image-text-to-text", "conversational", "en", "dataset:SpursgoZmy/MMTab", "dataset:liuhaotian/LLaVA-Instruct-150K", "dataset:liuhaotian/LLaVA-Pretrain", "arxiv:2406.08100", "arxiv:2310.03744", "region:us" ]	image-text-to-text	2025-04-27T07:04:41Z	--- datasets: - SpursgoZmy/MMTab - liuhaotian/LLaVA-Instruct-150K - liuhaotian/LLaVA-Pretrain language: - en metrics: - accuracy - bleu - f1 pipeline_tag: image-text-to-text --- # Table LLaVA Model Card <!-- Provide a quick summary of what the model is/does. --> Table LLaVA 7B is an open-source multimodal chatbot for understanding different table images and fulfilling diverse table-related requests, e.g., question answering, table cell description and structure understanding. See the ACL 2024 paper for more details: [Multimodal Table Understanding](https://arxiv.org/abs/2406.08100) ## How to use the model The initial model weights of Table-LLaVA were saved from the original LLaVA repository, which is not directly compatible with the Transformers. Thus, we convert the checkpoints from the original LLaVa repository to the Transformers-compatible version using the official scripts provided [here](https://github.com/SpursGoZmy/Table-LLaVA/issues/6). This means you can directly use the checkpoints in this repo in the same way as the [LLaVA-1.5-7B-HF](https://huggingface.co/llava-hf/llava-1.5-7b-hf). Many thanks for the help from Niels from the open-source team at HF! ### Using pure `transformers`: Below is an example script to run generation in `float16` precision on a GPU device: ```python import requests from PIL import Image import torch from transformers import AutoProcessor, LlavaForConditionalGeneration model_id = "SpursgoZmy/table-llava-v1.5-7b-hf" model = LlavaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(0) processor = AutoProcessor.from_pretrained(model_id) # Define a chat history and use `apply_chat_template` to get correctly formatted prompt # Each value in "content" has to be a list of dicts with types ("text", "image") conversation = [ { "role": "user", "content": [ {"type": "text", "text": "What are these?"}, {"type": "image"}, ], }, ] prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" raw_image = Image.open(requests.get(image_file, stream=True).raw) inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16) output = model.generate(inputs, max_new_tokens=200, do_sample=False) print(processor.decode(output[0][2:], skip_special_tokens=True)) ``` ### Using `vllm` to accelerate inference: You can also use the vllm to accelerate model inference. We test the checkpoints in this repo with `vllm==0.8.2`. The official script from the vllm project for VLM inference: ```python from vllm import LLM # You can also change the path to the dir path that contains pre-downloaded checkpoints. llm = LLM(model="SpursgoZmy/table-llava-v1.5-7b-hf") # Refer to the HuggingFace repo for the correct format to use prompt = "USER: <image>\nWhat is the content of this image?\nASSISTANT:" # Load the image using PIL.Image image = PIL.Image.open(...) # Single prompt inference outputs = llm.generate({ "prompt": prompt, "multi_modal_data": {"image": image}, }) for o in outputs: generated_text = o.outputs[0].text print(generated_text) ``` ## Model Details <!-- Provide a longer summary of what this model is. --> Model Type:** Table LLaVA 7B strictly follows the [LLaVA-v1.5](https://arxiv.org/abs/2310.03744) model architecture and training pipeline, with [CLIP-ViT-L-336px](https://huggingface.co/openai/clip-vit-large-patch14-336) as visual encoder (336336 image resolution), [Vicuna-v1.5-7B](https://huggingface.co/lmsys/vicuna-7b-v1.5) as base LLM and a two-layer MLP as vision-language connector. It was trained with a two-stage pipeline as LLaVA: 1. Pre-training: train the vision-language connector with image-caption data and table recognition data. 2. Instruction tuning: train the vision-language connector and the base LLM with multimodal instruction following data of tabular and non-tabular tasks. Code Base:* We use the official code of [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) for model training and inference, and the saved model checkpoint is uploaded to this repository. Thus, Table LLaVA can be used in the same way as the normal LLaVA v1.5 model with its original code. Model Date: Table-LLaVA 7B was trained in January 2024. Where to send questions or comments about the model: https://github.com/SpursGoZmy/Table-LLaVA/issues ## Training dataset The training data includes original LLaVA-1.5 data and specially constructed multimodal instruction-following data from the [MMTab dataset](https://huggingface.co/datasets/SpursgoZmy/MMTab), which is a large-scale dataset covering a wide range of table images and table-related tasks. \| Training Stage \| Data Description \| Data Size \| Hugging Face Dataset \| \| :---: \| :---: \| :---: \| :---: \| \| Pre-training \| 558K original LLaVA-1.5 pre-training data \| 558K \| [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) \| \| \| 150K table recognition data \| 150K \| [MMTab-pre_pretrain_data_llava_format_150K.json](https://huggingface.co/datasets/SpursgoZmy/MMTab) \| \| Instruction Fine-tuning \| 665K original LLaVA-1.5 fine-tuning data \| 665K \| [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) \| \| \| 232K multimodal instruction tuning data of 14 tabular tasks \| 232K \| [MMTab-instruct_sft_data_llava_format_232K.json](https://huggingface.co/datasets/SpursgoZmy/MMTab) \| We also provide the merged pre-training and instruction fine-tuning data in the MMTab dataset, i.e., enhanced_llava_pretrain_data_708K.json and enhanced_llava_sft_data_898K.json, which was used to train Table LLaVA. ## Evaluation dataset A collection of 17 held-in and 7 held-out tabular benchmarks, including 15 table-related tasks, e.g., table question answering and table2text generation. We also evaluate Table LLaVA on two non-tabular benchmarks: [TextVQA](https://textvqa.org/) and [llava-bench-in-the-wild](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild). ## License Table LLaVA is based on LLaVA-1.5 and thus follows its license. Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. ## Intended use Primary intended uses: The primary use of Table LLaVA is research on large multimodal models and chatbots, especially for multimodal table understanding. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Limitations Table LLaVA takes one table image as the model input. Digesting multiple table images would be valuable to support more application scenarios. Though the proposed Table-LLaVA demonstrates great performance on a wide range of table-based tasks, the resolution of input images (336*336) is relatively low and may limit the upper bound of its capacity. Luckily, with the emergence of MLLMs which possess higher input image resolution (e.g., Monkey (Li et al., 2023d), LLaVA-Next (Liu et al., 2024)), researchers can use MMTab to develop more powerful tabular MLLM in the future research.
sbrzz/TinyLLaVA-Qwen2.5-0.5B-Instruct-dinov2-small	sbrzz	2025-04-28T11:17:39Z	0	0	null	[ "safetensors", "tinyllava", "visual-question-answering", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "region:us" ]	visual-question-answering	2024-10-28T08:01:47Z	--- license: apache-2.0 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara base_model: - Qwen/Qwen2.5-0.5B-Instruct - facebook/dinov2-small pipeline_tag: visual-question-answering --- Pretrain stage only, 4630 epochs # Introduction We use the powerful [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) to create a super small image-text-to-text model. The goal is to make it possible to run LLaVA models on edge devices (with few gigabytes of memory). For LLM and vision tower, we choose [OpenELM-270M-Instruct](apple/OpenELM-270M-Instruct) and [facebook/dinov2-small](facebook/dinov2-small), respectively. [POPE](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#pope): \| Category \| # Samples \| TP \| FP \| TN \| FN \| Accuracy \| Precision \| Recall \| F1 Score \| Yes Ratio \| \|-----------------\|---------------\|--------\|--------\|--------\|--------\|--------------\|---------------\|------------\|--------------\|---------------\| \| Adversarial \| 3000 \| 1312 \| 1250 \| 250 \| 188 \| 0.521 \| 0.512 \| 0.875 \| 0.646 \| 0.854 \| \| Popular \| 3000 \| 1312 \| 1236 \| 264 \| 188 \| 0.525 \| 0.515 \| 0.875 \| 0.648 \| 0.849 \| \| Random \| 2910 \| 1312 \| 1185 \| 225 \| 188 \| 0.528 \| 0.525 \| 0.875 \| 0.656 \| 0.858 \| [TEXTVQA](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#textvqa) Samples 5000, Accuracy 0% (:-\|) [SCIENCEQA](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#scienceqa) Samples 4241, Correct: -, Accuracy: -%, IMG-Accuracy: -% [MMMU](https://tinyllava-factory.readthedocs.io/en/latest/Evaluation.html#mmmu) \| Category \| # Samples \| Accuracy \| \|---------------------------------\|-----------\|----------\| \| Overall \| 900 \| 0.280 \| \| Overall-Art and Design \| 120 \| 0.208 \| \| Art \| 30 \| 0.167 \| \| Art Theory \| 30 \| 0.200 \| \| Design \| 30 \| 0.367 \| \| Music \| 30 \| 0.100 \| \| Overall-Business \| 150 \| 0.213 \| \| Accounting \| 30 \| 0.100 \| \| Economics \| 30 \| 0.367 \| \| Finance \| 30 \| 0.200 \| \| Management \| 30 \| 0.233 \| \| Marketing \| 30 \| 0.167 \| \| Overall-Science \| 150 \| 0.300 \| \| Biology \| 30 \| 0.300 \| \| Chemistry \| 30 \| 0.133 \| \| Geography \| 30 \| 0.300 \| \| Math \| 30 \| 0.333 \| \| Physics \| 30 \| 0.433 \| \| Overall-Health and Medicine \| 150 \| 0.340 \| \| Basic Medical Science \| 30 \| 0.300 \| \| Clinical Medicine \| 30 \| 0.133 \| \| Diagnostics and Laboratory Med. \| 30 \| 0.333 \| \| Pharmacy \| 30 \| 0.400 \| \| Public Health \| 30 \| 0.533 \| \| Overall-Humanities and Soc. Sci.\| 120 \| 0.342 \| \| History \| 30 \| 0.300 \| \| Literature \| 30 \| 0.567 \| \| Sociology \| 30 \| 0.233 \| \| Psychology \| 30 \| 0.267 \| \| Overall-Tech and Engineering \| 210 \| 0.276 \| \| Agriculture \| 30 \| 0.300 \| \| Architecture and Engineering \| 30 \| 0.200 \| \| Computer Science \| 30 \| 0.367 \| \| Electronics \| 30 \| 0.200 \| \| Energy and Power \| 30 \| 0.367 \| \| Materials \| 30 \| 0.233 \| \| Mechanical Engineering \| 30 \| 0.267 \|
TQkQJY4xeSKc/dhaadee	TQkQJY4xeSKc	2025-04-28T11:17:38Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:17:38Z	--- license: apache-2.0 ---
TheStageAI/Elastic-DeepSeek-R1-Distill-Qwen-7B	TheStageAI	2025-04-28T11:17:19Z	78	1	null	[ "text2text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "license:apache-2.0", "region:us" ]	text2text-generation	2025-04-25T16:45:40Z	--- license: apache-2.0 base_model: - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B base_model_relation: quantized pipeline_tag: text2text-generation language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Elastic model: DeepSeek-R1-Distill-Qwen-7B. Fastest and most flexible models for self-serving. Elastic models are the models produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA allows you to control model size, latency and quality with a simple slider movement. For each model, ANNA produces a series of optimized models: * __XL__: Mathematically equivalent neural network, optimized with our DNN compiler. * __L__: Near lossless model, with less than 1% degradation obtained on corresponding benchmarks. * __M__: Faster model, with accuracy degradation less than 1.5%. * __S__: The fastest model, with accuracy degradation less than 2%. __Goals of elastic models:__ * Provide flexibility in cost vs quality selection for inference * Provide clear quality and latency benchmarks * Provide interface of HF libraries: transformers and diffusers with a single line of code * Provide models supported on a wide range of hardware, which are pre-compiled and require no JIT. * Provide the best models and service for self-hosting. > It's important to note that specific quality degradation can vary from model to model. For instance, with an S model, you can have 0.5% degradation as well. ![Performance Graph](images/performance_graph.png) ----- ## Inference To infer our models, you just need to replace `transformers` import with `elastic_models.transformers`: ```python import torch from transformers import AutoTokenizer from elastic_models.transformers import AutoModelForCausalLM # Currently we require to have your HF token # as we use original weights for part of layers and # model confugaration as well model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" hf_token = '' device = torch.device("cuda") # Create mode tokenizer = AutoTokenizer.from_pretrained( model_name, token=hf_token ) model = AutoModelForCausalLM.from_pretrained( model_name, token=hf_token, torch_dtype=torch.bfloat16, attn_implementation="sdpa", mode='S' ).to(device) model.generation_config.pad_token_id = tokenizer.eos_token_id # Inference simple as transformers library prompt = "Describe basics of DNNs quantization." messages = [ { "role": "system", "content": "You are a search bot, answer on user text queries." }, { "role": "user", "content": prompt } ] chat_prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False ) inputs = tokenizer(chat_prompt, return_tensors="pt") inputs.to(device) with torch.inference_mode(): generate_ids = model.generate(*inputs, max_length=500) input_len = inputs['input_ids'].shape[1] generate_ids = generate_ids[:, input_len:] output = tokenizer.batch_decode( generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] # Validate answer print(f"# Q:\n{prompt}\n") print(f"# A:\n{output}\n") ``` __System requirements:__ GPUs: H100, L40s * CPU: AMD, Intel * Python: 3.10-3.12 To work with our models just run these lines in your terminal: ```shell pip install thestage pip install elastic_models[nvidia]\ --index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\ --extra-index-url https://pypi.nvidia.com\ --extra-index-url https://pypi.org/simple pip install flash_attn==2.7.3 --no-build-isolation pip uninstall apex ``` Then go to [app.thestage.ai](https://app.thestage.ai), login and generate API token from your profile page. Set up API token as follows: ```shell thestage config set --api-token <YOUR_API_TOKEN> ``` Congrats, now you can use accelerated models! ---- ## Benchmarks Benchmarking is one of the most important procedures during model acceleration. We aim to provide clear performance metrics for models using our algorithms. The `W8A8, int8 column` indicates that we applied W8A8 quantization with int8 data type to all linear layers and used the same calibration data as for ANNA. The S model achieves practically identical speed but much higher quality, as ANNA knows how to improve quantization quality on sensitive layers! ### Quality benchmarks \| Metric/Model \| S \| M \| L \| XL \| Original \| W8A8, int8 \| \|---------------\|---\|---\|---\|----\|----------\|------------\| \| arc_challenge \| 41.00 \| 40.90 \| 42.50 \| 42.20 \| 42.20 \| 21.00 \| - \| \| mmlu \| 52.00 \| 53.80 \| 55.10 \| 55.20 \| 55.20 \| 48.00 \| - \| \| piqa \| 68.40 \| 70.60 \| 70.70 \| 70.50 \| 70.50 \| 50.40 \| - \| \| winogrande \| 60.10 \| 60.90 \| 60.20 \| 60.10 \| 60.10 \| 51.10 \| - \| * MMLU: Evaluates general knowledge across 57 subjects including science, humanities, engineering, and more. Shows model's ability to handle diverse academic topics. * PIQA: Evaluates physical commonsense reasoning through questions about everyday physical interactions. Shows model's understanding of real-world physics concepts. * Arc Challenge: Evaluates grade-school level multiple-choice questions requiring reasoning. Shows model's ability to solve complex reasoning tasks. * Winogrande: Evaluates commonsense reasoning through sentence completion tasks. Shows model's capability to understand context and resolve ambiguity. ### Latency benchmarks __100 input/300 output; tok/s:__ \| GPU/Model \| S \| M \| L \| XL \| Original \| W8A8, int8 \| \|-----------\|-----\|---\|---\|----\|----------\|------------\| \| H100 \| 204 \| 185 \| 161 \| 135 \| 62 \| 205 \| - \| \| L40S \| 78 \| 69 \| 60 \| 47 \| 43 \| 78 \| - \| ## Links * __Platform__: [app.thestage.ai](app.thestage.ai) * __Subscribe for updates__: [TheStageAI X](https://x.com/TheStageAI) <!-- * __Elastic models Github__: [app.thestage.ai](app.thestage.ai) --> * __Contact email__: [email protected]
TheStageAI/Elastic-DeepSeek-R1-Distill-Llama-8B	TheStageAI	2025-04-28T11:17:11Z	60	1	null	[ "text2text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "license:apache-2.0", "region:us" ]	text2text-generation	2025-04-24T14:17:26Z	--- license: apache-2.0 base_model: - deepseek-ai/DeepSeek-R1-Distill-Llama-8B base_model_relation: quantized pipeline_tag: text2text-generation language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Elastic model: DeepSeek-R1-Distill-Llama-8B. Fastest and most flexible models for self-serving. Elastic models are the models produced by TheStage AI ANNA: Automated Neural Networks Accelerator. ANNA allows you to control model size, latency and quality with a simple slider movement. For each model, ANNA produces a series of optimized models: * __XL__: Mathematically equivalent neural network, optimized with our DNN compiler. * __L__: Near lossless model, with less than 1% degradation obtained on corresponding benchmarks. * __M__: Faster model, with accuracy degradation less than 1.5%. * __S__: The fastest model, with accuracy degradation less than 2%. __Goals of elastic models:__ * Provide flexibility in cost vs quality selection for inference * Provide clear quality and latency benchmarks * Provide interface of HF libraries: transformers and diffusers with a single line of code * Provide models supported on a wide range of hardware, which are pre-compiled and require no JIT. * Provide the best models and service for self-hosting. > It's important to note that specific quality degradation can vary from model to model. For instance, with an S model, you can have 0.5% degradation as well. ![Performance Graph](images/performance_graph.png) ----- ## Inference To infer our models, you just need to replace `transformers` import with `elastic_models.transformers`: ```python import torch from transformers import AutoTokenizer from elastic_models.transformers import AutoModelForCausalLM # Currently we require to have your HF token # as we use original weights for part of layers and # model confugaration as well model_name = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" hf_token = '' device = torch.device("cuda") # Create mode tokenizer = AutoTokenizer.from_pretrained( model_name, token=hf_token ) model = AutoModelForCausalLM.from_pretrained( model_name, token=hf_token, torch_dtype=torch.bfloat16, attn_implementation="sdpa", mode='S' ).to(device) model.generation_config.pad_token_id = tokenizer.eos_token_id # Inference simple as transformers library prompt = "Describe basics of DNNs quantization." messages = [ { "role": "system", "content": "You are a search bot, answer on user text queries." }, { "role": "user", "content": prompt } ] chat_prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False ) inputs = tokenizer(chat_prompt, return_tensors="pt") inputs.to(device) with torch.inference_mode(): generate_ids = model.generate(*inputs, max_length=500) input_len = inputs['input_ids'].shape[1] generate_ids = generate_ids[:, input_len:] output = tokenizer.batch_decode( generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] # Validate answer print(f"# Q:\n{prompt}\n") print(f"# A:\n{output}\n") ``` __System requirements:__ GPUs: H100, L40s * CPU: AMD, Intel * Python: 3.10-3.12 To work with our models just run these lines in your terminal: ```shell pip install thestage pip install elastic_models[nvidia]\ --index-url https://thestage.jfrog.io/artifactory/api/pypi/pypi-thestage-ai-production/simple\ --extra-index-url https://pypi.nvidia.com\ --extra-index-url https://pypi.org/simple pip install flash_attn==2.7.3 --no-build-isolation pip uninstall apex ``` Then go to [app.thestage.ai](https://app.thestage.ai), login and generate API token from your profile page. Set up API token as follows: ```shell thestage config set --api-token <YOUR_API_TOKEN> ``` Congrats, now you can use accelerated models! ---- ## Benchmarks Benchmarking is one of the most important procedures during model acceleration. We aim to provide clear performance metrics for models using our algorithms. The `W8A8, int8 column` indicates that we applied W8A8 quantization with int8 data type to all linear layers and used the same calibration data as for ANNA. The S model achieves practically identical speed but much higher quality, as ANNA knows how to improve quantization quality on sensitive layers! ### Quality benchmarks \| Metric/Model \| S \| M \| L \| XL \| Original \| W8A8, int8 \| \|---------------\|---\|---\|---\|----\|----------\|------------\| \| arc_challenge \| 38.70 \| 40.40 \| 40.40 \| 40.50 \| 40.50 \| 19.30 \| - \| \| mmlu \| 52.70 \| 54.70 \| 55.50 \| 54.80 \| 54.80 \| 47.70 \| - \| \| piqa \| 76.30 \| 75.90 \| 75.70 \| 76.10 \| 76.10 \| 55.00 \| - \| \| winogrande \| 66.60 \| 66.20 \| 67.80 \| 68.00 \| 68.00 \| 56.10 \| - \| * MMLU: Evaluates general knowledge across 57 subjects including science, humanities, engineering, and more. Shows model's ability to handle diverse academic topics. * PIQA: Evaluates physical commonsense reasoning through questions about everyday physical interactions. Shows model's understanding of real-world physics concepts. * Arc Challenge: Evaluates grade-school level multiple-choice questions requiring reasoning. Shows model's ability to solve complex reasoning tasks. * Winogrande: Evaluates commonsense reasoning through sentence completion tasks. Shows model's capability to understand context and resolve ambiguity. ### Latency benchmarks __100 input/300 output; tok/s:__ \| GPU/Model \| S \| M \| L \| XL \| Original \| W8A8, int8 \| \|-----------\|-----\|---\|---\|----\|----------\|------------\| \| H100 \| 194 \| 191 \| 161 \| 131 \| 58 \| 198 \| - \| \| L40S \| 72 \| 70 \| 56 \| 44 \| 40 \| 74 \| - \| ## Links * __Platform__: [app.thestage.ai](app.thestage.ai) * __Subscribe for updates__: [TheStageAI X](https://x.com/TheStageAI) <!-- * __Elastic models Github__: [app.thestage.ai](app.thestage.ai) --> * __Contact email__: [email protected]
dvjBq0crfRs/djioefgv	dvjBq0crfRs	2025-04-28T11:16:55Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:16:55Z	--- license: apache-2.0 ---
EAvUxwipnqj/dekfs	EAvUxwipnqj	2025-04-28T11:16:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T11:16:27Z	--- license: apache-2.0 ---
DanielNRU/pollen-ner-cycle-1000	DanielNRU	2025-04-28T11:15:24Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:DeepPavlov/rubert-base-cased", "base_model:adapter:DeepPavlov/rubert-base-cased", "region:us" ]	null	2025-04-28T10:31:15Z	--- library_name: peft base_model: DeepPavlov/rubert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: pollen-ner-cycle-1000 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. --> # pollen-ner-cycle-1000 This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2608 - Precision: 0.7063 - Recall: 0.8046 - F1: 0.7523 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 125 \| 0.8324 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 250 \| 0.5457 \| 0.5 \| 0.4197 \| 0.4564 \| \| No log \| 3.0 \| 375 \| 0.4199 \| 0.5905 \| 0.6944 \| 0.6382 \| \| 0.7256 \| 4.0 \| 500 \| 0.3539 \| 0.6190 \| 0.7447 \| 0.6760 \| \| 0.7256 \| 5.0 \| 625 \| 0.3166 \| 0.6618 \| 0.7872 \| 0.7191 \| \| 0.7256 \| 6.0 \| 750 \| 0.2972 \| 0.6650 \| 0.7950 \| 0.7242 \| \| 0.7256 \| 7.0 \| 875 \| 0.2787 \| 0.6965 \| 0.7988 \| 0.7441 \| \| 0.3014 \| 8.0 \| 1000 \| 0.2653 \| 0.7096 \| 0.7988 \| 0.7516 \| \| 0.3014 \| 9.0 \| 1125 \| 0.2601 \| 0.7133 \| 0.7988 \| 0.7536 \| \| 0.3014 \| 10.0 \| 1250 \| 0.2608 \| 0.7063 \| 0.8046 \| 0.7523 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
deeponh/mal_8b_8b_D2	deeponh	2025-04-28T11:14:34Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:08:25Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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jowessovdecu/sfdvdfv	jowessovdecu	2025-04-28T11:13:48Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-04-28T11:13:48Z	--- license: bsd-3-clause ---
technocrat3128/ISLexitas_NAWAH_28-04-25	technocrat3128	2025-04-28T11:13:37Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T11:12:29Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: technocrat3128 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-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)
conheocon/unin	conheocon	2025-04-28T11:13:31Z	0	0	null	[ "license:bsd-3-clause-clear", "region:us" ]	null	2025-04-28T11:13:27Z	--- license: bsd-3-clause-clear ---

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