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 For more details on the model, please go to Deepseek's original model card All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
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Our DeepSeek-V3-0324 GGUFs allow you to run the model in llama.cpp, LMStudio, Open WebUI and other inference frameworks. Includes 1-4-bit Dynamic versions, which yields better accuracy and results than standard quantization. Prelim = preliminary - through our testing, they're generally fine but sometimes don't produce the best code and so more work/testing needs to be done. 2.71bit was found to be the best in terms of performance/size and produces code that is great and works well. 2.42bit was also found to pass all our tests.
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These quants include our Unsloth chat template fixes, specifically for llama.cpp supported backends. - You must use --jinja for llama.cpp quants - Set the temperature **~0.6** (recommended) and Top_P value of **0.95** (recommended) - UD-Q2_K_XL (247GB) is recommended - For complete detailed instructions, see our guide: unsloth.ai/blog/deepseek-v3.1 DeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects: DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. Additionally, DeepSeek-V3.1 is trained using the UE8M0 FP8 scale data format to ensure compatibility with microscaling data formats.
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We introduce **DeepSeek-V3.2**, a model that harmonizes high computational efficiency with superior reasoning and agent performance. Our approach is built upon three key technical breakthroughs: 1. **DeepSeek Sparse Attention (DSA):** We introduce DSA, an efficient attention mechanism that substantially reduces computational complexity while preserving model performance, specifically optimized for long-context scenarios. 2. **Scalable Reinforcement Learning Framework:** By implementing a robust RL protocol and scaling post-training compute, *DeepSeek-V3.2* performs comparably to GPT-5. Notably, our high-compute variant, **DeepSeek-V3.2-Speciale**, **surpasses GPT-5** and exhibits reasoning proficiency on par with Gemini-3.0-Pro. - *Achievement:* 🥇 **Gold-medal performance** in the 2025 International Mathematical Olympiad (IMO) and International Olympiad in Informatics (IOI). 3. **Large-Scale Agentic Task Synthesis Pipeline:** To integrate **reasoning into tool-use** scenarios, we developed a novel synthesis pipeline that systematically generates training data at scale. This facilitates scalable agentic post-training, improving compliance and generalization in complex interactive environments. We have also released the final submissions for IOI 2025, ICPC World Finals, IMO 2025 and CMO 2025, which were selected based on our designed pipeline. These materials are provided for the community to conduct secondary verification. The files can be accessed at `assets/olympiad_cases`.
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Run on a 128GB+ RAM device for the smallest 1-bit, 3-bit on 256GB or full precision on 320GB. Read Guide To run, please use our llama.cpp PR or the Unsloth Desktop app. Get up to 3x faster inference with Unsloth vs other ngram/CPU/VRAM repos./ **DeepSeek-V4.1-Flash-1017** is a minor update to **DeepSeek-V4.1-Flash**, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses. *† denotes the text-only subset of HLE. We use the Claude Code harness for Terminal-Bench 4.0 and the Mini-SWE-V2 harness for DeepSWE v1.1.*
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Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. To run DeepSeek-V4-Flash-0731 in full precision lossless, run Q8 (UD-Q8_K_XL), which is 162GB and only 7GB bigger than Q4 (UD-Q4_K_XL). See our DeepSeek-V4 guide for quantization analysis and instructions. You can now run DeepSeek-V4-Flash-0731 in Unsloth Studio with toggles for High and Max thinking. New DSpark support allowing up to 2x faster decoding! Docs for DSpark **DeepSeek-V4-Flash-0731** is the official release of **DeepSeek-V4-Flash**, superseding the preview version, with substantially enhanced agentic capabilities. It has the same model structure as DeepSeek-V4-Flash-DSpark, i.e. it comes with a speculative decoding module attached.
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To run DeepSeek-V4 correctly, ensure you use the latest version of llama.cpp or Unsloth Studio. We also improved the DeepSeek-V4 chat jinja template, and tested over 4000 conversations to be equivalent with the official baseline. Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. To run DeepSeek-V4-Flash in full precision lossless, run Q8 (UD-Q8_K_XL), which is 162GB and only 7GB bigger than Q4 (UD-Q4_K_XL). See our DeepSeek-V4 guide for quantization analysis and instructions. You can now run DeepSeek-V4-Flash in Unsloth Studio with toggles for High and Max thinking.
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To run DeepSeek-V4-Flash-Vision-Exp in full precision lossless, run Q8 (UD-Q8_K_XL), which is 162GB and only 7GB bigger than Q4 (UD-Q4_K_XL). See our DeepSeek-V4 guide for quantization analysis and instructions. You can now run DeepSeek-V4-Flash-Vision-Exp in Unsloth Studio with toggles for High and Max thinking. > Image input requires llama.cpp b10766 or later, which is the first release containing the DeepSeek-V4 vision support from #28133 and #28154. We are excited to introduce **DeepSeek-V4-Flash-Vision-Exp**, our first experimental multimodal model in the DeepSeek-V4 family. It builds on the DeepSeek-V4-Flash architecture by incorporating visual modules and undergoing continued training to unlock visual understanding capabilities.
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Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. See our DeepSeek-V4 guide for quantization analysis and run instructions. DeepSeek-V4-Pro-0813 is a 1.57T parameter model with 48B active parameters per token, so it needs substantially more memory than DeepSeek-V4-Flash-0731. For DeepSeek-V4-Flash-0731 GGUFs, see unsloth/DeepSeek-V4-Flash-0731-GGUF . **DeepSeek-V4-Pro-0813** is the official release of **DeepSeek-V4-Pro**, superseding the preview version, with greatly enhanced agentic capabilities and performance improvements that are especially pronounced in production environments. It is built on the DeepSeek-V4-Pro (Preview) model structure, with a DSpark speculative decoding module attached.
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Devstral is an agentic LLM for software engineering tasks. **Devstral 2** excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench. This model is an Instruct model in **FP8**, fine-tuned to follow instructions, making it ideal for chat, agentic and instruction based tasks for SWE use cases. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we invite companies to reach out to us.
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Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. Devstral is an agentic LLM for software engineering tasks. **Devstral Small 2** excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench. This model is an Instruct model in **FP8**, fine-tuned to follow instructions, making it ideal for chat, agentic and instruction based tasks for SWE use cases.
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Devstral is an agentic LLM for software engineering tasks built under a collaboration between Mistral AI and All Hands AI 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this benchmark. It is finetuned from Mistral-Small-3.1, therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
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Devstral is an agentic LLM for software engineering tasks built under a collaboration between Mistral AI and All Hands AI 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this benchmark. It is finetuned from Mistral-Small-3.1, therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed. For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
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