codellama-13b-bnb-4bit
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codellama-7b
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View details ↗codellama-7b-bnb-4bit
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View details ↗cogito-671b-v2.1-GGUF
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View details ↗Cosmos-Reason1-7B-GGUF
unslothNVIDIA Cosmos Reason – an open, customizable, 7B-parameter reasoning vision language model (VLM) for physical AI and robotics - enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next. Cosmos Reason excels at navigating the long tail of diverse scenarios of the physical world with spatial-temporal understanding. Cosmos Reason is post-trained with physical common sense and embodied reasoning data with supervised fine-tuning and reinforcement learning. It uses chain-of-thought reasoning capabilities to understand world dynamics without human annotations. Given a video/image and a text prompt, the model first converts the video/image into tokens using a vision encoder and a special translator called a projector. These video tokens are combined with the text prompt and fed into the core model, which uses a mix of LLM modules and techniques. This enables the model to think step-by-step and provide detailed, logical responses.
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View details ↗CrisperWhisper
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View details ↗csm-1b
unsloth**2025/03/13** - We are releasing the 1B CSM variant. Code is available on GitHub: SesameAILabs/csm. CSM (Conversational Speech Model) is a speech generation model from Sesame that generates RVQ audio codes from text and audio inputs. The model architecture employs a Llama backbone and a smaller audio decoder that produces Mimi audio codes. CSM sounds best when provided with context. You can prompt or provide context to the model using a `Segment` for each speaker utterance.
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View details ↗DeepSeek-OCR
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View details ↗DeepSeek-OCR-2
unslothThis DeepSeek-OCR 2 upload was edited to enable inference & fine-tuning on the latest transformers (no accuracy change). Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8： We would like to thank DeepSeek-OCR, Vary, GOT-OCR2.0, MinerU, PaddleOCR for their valuable models and ideas.
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View details ↗DeepSeek-Prover-V2-671B-GGUF
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View details ↗DeepSeek-Prover-V2-7B-GGUF
unslothWe introduce DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by DeepSeek-V3. The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals. The proofs of resolved subgoals are synthesized into a chain-of-thought process, combined with DeepSeek-V3's step-by-step reasoning, to create an initial cold start for reinforcement learning. This process enables us to integrate both informal and formal mathematical reasoning into a unified model. we introduce ProverBench, a benchmark dataset comprising 325 problems. Of these, 15 are formalized from number theory and algebra questions featured in the recent AIME competitions (AIME 24 and 25), offering authentic high-school competition-level challenges. The remaining 310 problems are drawn from curated textbook examples and educational tutorials, contributing a diverse and pedagogically grounded collection of formalized mathematical problems. This benchmark is designed to enable more comprehensive evaluation across both high-school competition problems and undergraduate-level mathematics. We release DeepSeek-Prover-V2 in two model sizes: 7B and 671B parameters. DeepSeek-Prover-V2-671B is trained on top of DeepSeek-V3-Base. DeepSeek-Prover-V2-7B is built upon DeepSeek-Prover-V1.5-Base and features an extended context length of up to 32K tokens.
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View details ↗DeepSeek-R1-0528-BF16
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View details ↗DeepSeek-R1-0528-GGUF
unslothThe DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528. In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of leading models, such as O3 and Gemini 2.5 Pro. Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question. Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.
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View details ↗DeepSeek-R1-0528-Qwen3-8B
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View details ↗See our collection for all versions of R1 including GGUF, 4-bit & 16-bit formats. Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. - For Ollama do `ollama run hf.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_XL` - it'll auto get the correct chat template and all settings - Set the temperature between **0.5–0.7 (0.6 recommended)** to reduce repetition and incoherence. - Set Top_P value of **0.95 (recommended)** - R1-0528 uses the same chat template as the original R1 model:
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View details ↗DeepSeek-R1-BF16
unslothYou can view more detailed instructions in our blog: unsloth.ai/blog/deepseek-r1 1. Do not forget about ` ` and ` ` tokens! - Or use a chat template formatter 2. Obtain the latest `llama.cpp` at https://github.com/ggerganov/llama.cpp 3. Example with Q8_0 K quantized cache **Notice -no-cnv disables auto conversation mode** 4. If you have a GPU (RTX 4090 for example) with 24GB, you can offload multiple layers to the GPU for faster processing. If you have multiple GPUs, you can probably offload more layers. We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb
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View details ↗Or you can view more detailed instructions here: unsloth.ai/blog/deepseek-r1 1. Do not forget about ` ` and ` ` tokens! - Or use a chat template formatter 2. Obtain the latest `llama.cpp` at https://github.com/ggerganov/llama.cpp 3. Example with Q8_0 K quantized cache **Notice -no-cnv disables auto conversation mode** 4. If you have a GPU (RTX 4090 for example) with 24GB, you can offload multiple layers to the GPU for faster processing. If you have multiple GPUs, you can probably offload more layers. We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb
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