Deepseek R1 BF16

🚀 DeepSeek-R1

This project introduces DeepSeek-R1, a powerful reasoning model. It offers excellent performance in various benchmarks, and related models and notebooks are open - sourced to support the research community.

🚀 Quick Start

See our collection for versions of DeepSeek-R1 including GGUF, 4 - bit and original formats.

Instructions to run this model in llama.cpp

You can view more detailed instructions in our blog: unsloth.ai/blog/deepseek-r1

1. Do not forget about <｜User｜> and <｜Assistant｜> 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

   ./llama.cpp/llama-cli \
       --model unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF/DeepSeek-R1-Distill-Llama-8B-Q4_K_M.gguf \
       --cache-type-k q8_0 \
       --threads 16 \
       --prompt '<｜User｜>What is 1+1?<｜Assistant｜>' \
       -no-cnv
   

   Example output:

    <think>
    Okay, so I need to figure out what 1 plus 1 is. Hmm, where do I even start? I remember from school that adding numbers is pretty basic, but I want to make sure I understand it properly.
    Let me think, 1 plus 1. So, I have one item and I add another one. Maybe like a apple plus another apple. If I have one apple and someone gives me another, I now have two apples. So, 1 plus 1 should be 2. That makes sense.
    Wait, but sometimes math can be tricky. Could it be something else? Like, in a different number system maybe? But I think the question is straightforward, using regular numbers, not like binary or hexadecimal or anything.
    I also recall that in arithmetic, addition is combining quantities. So, if you have two quantities of 1, combining them gives you a total of 2. Yeah, that seems right.
    Is there a scenario where 1 plus 1 wouldn't be 2? I can't think of any...
   

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.

   ./llama.cpp/llama-cli \
   --model unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF/DeepSeek-R1-Distill-Llama-8B-Q4_K_M.gguf
   --cache-type-k q8_0 
   --threads 16 
   --prompt '<｜User｜>What is 1+1?<｜Assistant｜>'
   --n-gpu-layers 20 \
    -no-cnv
   

✨ Features

Finetune LLMs 2 - 5x faster with 70% less memory via Unsloth!

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

✨ Finetune for Free

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.

• This Llama 3.2 conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
• This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
• * Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

Special Thanks

A huge thank you to the DeepSeek team for creating and releasing these models.

📚 Documentation

1. Introduction

We introduce our first - generation reasoning models, DeepSeek - R1 - Zero and DeepSeek - R1. DeepSeek - R1 - Zero, a model trained via large - scale reinforcement learning (RL) without supervised fine - tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek - R1 - Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek - R1 - Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek - R1, which incorporates cold - start data before RL. DeepSeek - R1 achieves performance comparable to OpenAI - o1 across math, code, and reasoning tasks. To support the research community, we have open - sourced DeepSeek - R1 - Zero, DeepSeek - R1, and six dense models distilled from DeepSeek - R1 based on Llama and Qwen. DeepSeek - R1 - Distill - Qwen - 32B outperforms OpenAI - o1 - mini across various benchmarks, achieving new state - of - the - art results for dense models.

NOTE: Before running DeepSeek - R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.

2. Model Summary

Post - Training: Large - Scale Reinforcement Learning on the Base Model

• We directly apply reinforcement learning (RL) to the base model without relying on supervised fine - tuning (SFT) as a preliminary step. This approach allows the model to explore chain - of - thought (CoT) for solving complex problems, resulting in the development of DeepSeek - R1 - Zero. DeepSeek - R1 - Zero demonstrates capabilities such as self - verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area.
• We introduce our pipeline to develop DeepSeek - R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non - reasoning capabilities. We believe the pipeline will benefit the industry by creating better models.

Distillation: Smaller Models Can Be Powerful Too

• We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek - R1, as well as its API, will benefit the research community to distill better smaller models in the future.
• Using the reasoning data generated by DeepSeek - R1, we fine - tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open - source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community.

3. Model Downloads

DeepSeek - R1 Models

DeepSeek - R1 - Zero & DeepSeek - R1 are trained based on DeepSeek - V3 - Base. For more details regarding the model architecture, please refer to [DeepSeek - V3](https://github.com/deepseek - ai/DeepSeek - V3) repository.

DeepSeek - R1 - Distill Models

DeepSeek - R1 - Distill models are fine - tuned based on open - source models, using samples generated by DeepSeek - R1. We slightly change their configs and tokenizers. Please use our setting to run these models.

4. Evaluation Results

DeepSeek - R1 - Evaluation

For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top - p value of $0.95$, and generate 64 responses per query to estimate pass@1.

Distilled Model Evaluation

5. Chat Website & API Platform

You can chat with DeepSeek - R1 on DeepSeek's official website: chat.deepseek.com, and switch on the button "DeepThink"

We also provide OpenAI - Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek - R1 Models

Please visit [DeepSeek - V3](https://github.com/deepseek - ai/DeepSeek - V3) repo for more information about running DeepSeek - R1 locally.

DeepSeek - R1 - Distill Models

DeepSeek - R1 - Distill models can be utilized in the same manner as Qwen or Llama models.

For instance, you can easily start a service using [vLLM](https://github.com/vllm - project/vllm):

vllm serve deepseek - ai/DeepSeek - R1 - Distill - Qwen - 32B --tensor - parallel - size 2 --max - model - len 32768 --enforce - eager

You can also easily start a service using [SGLang](https://github.com/sgl - project/sglang)

python3 -m sglang.launch_server --model deepseek - ai/DeepSeek - R1 - Distill - Qwen - 32B --trust - remote - code --tp 2

Usage Recommendations

We recommend adhering to the following configurations when utilizing the DeepSeek - R1 series models, including benchmarking, to achieve the expected performance:

1. Set the temperature within the range of 0.5 - 0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
2. Avoid adding a system prompt; all instructions should be contained within the user prompt.
3. For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
4. When evaluating model performance, it is recommended to...

📄 License

The project uses the MIT license.