📋 Model Description
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B language:
- en
- deepseek
- qwen
- qwen2
- unsloth
- transformers
See our collection for versions of Deepseek-R1 including GGUF and original formats.
Instructions to run this model in llama.cpp:
Or you can view more detailed instructions here: unsloth.ai/blog/deepseek-r1- Do not forget about
<｜User｜>and<｜Assistant｜>tokens! - Or use a chat template formatter - Obtain the latest
llama.cppat https://github.com/ggerganov/llama.cpp - Example with Q8_0 K quantized cache Notice -no-cnv disables auto conversation mode
./llama.cpp/llama-cli \
--model unsloth/DeepSeek-R1-Distill-Qwen-14B-GGUF/DeepSeek-R1-Distill-Qwen-14B-Q4KM.gguf \
--cache-type-k q8_0 \
--threads 16 \
--prompt '<｜User｜>What is 1+1?<｜Assistant｜>' \
-no-cnv<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...- 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-Qwen-14B-GGUF/DeepSeek-R1-Distill-Qwen-14B-Q4KM.gguf
--cache-type-k q8_0
--threads 16
--prompt '<｜User｜>What is 1+1?<｜Assistant｜>'
--n-gpu-layers 20 \
-no-cnvFinetune 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.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|---|---|---|---|
| Llama-3.2 (3B) | ▶️ Start on Colab-Conversational.ipynb) | 2.4x faster | 58% less |
| Llama-3.2 (11B vision) | ▶️ Start on Colab-Vision.ipynb) | 2x faster | 60% less |
| Qwen2 VL (7B) | ▶️ Start on Colab-Vision.ipynb) | 1.8x faster | 60% less |
| Qwen2.5 (7B) | ▶️ Start on Colab-Alpaca.ipynb) | 2x faster | 60% less |
| Llama-3.1 (8B) | ▶️ Start on Colab-Alpaca.ipynb) | 2.4x faster | 58% less |
| Phi-3.5 (mini) | ▶️ Start on Colab | 2x faster | 50% less |
| Gemma 2 (9B) | ▶️ Start on Colab-Alpaca.ipynb) | 2.4x faster | 58% less |
| Mistral (7B) | ▶️ Start on Colab-Conversational.ipynb) | 2.2x faster | 62% less |
- This Llama 3.2 conversational notebook-Conversational.ipynb) is useful for ShareGPT ChatML / Vicuna templates.
- This text completion notebook-TextCompletion.ipynb) 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.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.
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.
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
| Model | #Total Params | #Activated Params | Context Length | Download |
|---|---|---|---|---|
| DeepSeek-R1-Zero | 671B | 37B | 128K | 🤗 HuggingFace |
| DeepSeek-R1 | 671B | 37B | 128K | 🤗 HuggingFace |
DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base.
For more details regrading the model architecture, please refer to DeepSeek-V3 repository.
DeepSeek-R1-Distill Models
| Model | Base Model | Download |
|---|---|---|
| DeepSeek-R1-Distill-Qwen-1.5B | Qwen2.5-Math-1.5B | 🤗 HuggingFace |
| DeepSeek-R1-Distill-Qwen-7B | Qwen2.5-Math-7B | 🤗 HuggingFace |
| DeepSeek-R1-Distill-Llama-8B | Llama-3.1-8B | 🤗 HuggingFace |
| DeepSeek-R1-Distill-Qwen-14B | Qwen2.5-14B | 🤗 HuggingFace |
| DeepSeek-R1-Distill-Qwen-32B | Qwen2.5-32B | 🤗 HuggingFace |
| DeepSeek-R1-Distill-Llama-70B | Llama-3.3-70B-Instruct | 🤗 HuggingFace |
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.| Category | Benchmark (Metric) | Claude-3.5-Sonnet-1022 | GPT-4o 0513 | DeepSeek V3 | OpenAI o1-mini | OpenAI o1-1217 | DeepSeek R1 |
|---|---|---|---|---|---|---|---|
| Architecture | - | - | MoE | - | - | MoE | |
| # Activated Params | - | - | 37B | - | - | 37B | |
| # Total Params | - | - | 671B | - | - | 671B | |
| English | MMLU (Pass@1) | 88.3 | 87.2 | 88.5 | 85.2 | 91.8 | 90.8 |
| MMLU-Redux (EM) | 88.9 | 88.0 | 89.1 | 86.7 | - | 92.9 | |
| MMLU-Pro (EM) | 78.0 | 72.6 | 75.9 | 80.3 | - | 84.0 | |
| DROP (3-shot F1) | 88.3 | 83.7 | 91.6 | 83.9 | 90.2 | 92.2 | |
| IF-Eval (Prompt Strict) | 86.5 | 84.3 | 86.1 | 84.8 | - | 83.3 | |
| GPQA-Diamond (Pass@1) | 65.0 | 49.9 | 59.1 | 60.0 | 75.7 | 71.5 | |
| SimpleQA (Correct) | 28.4 | 38.2 | 24.9 | 7.0 | 47.0 | 30.1 | |
| FRAMES (Acc.) | 72.5 | 80.5 | 73.3 | 76.9 | - | 82.5 | |
| AlpacaEval2.0 (LC-winrate) | 52.0 | 51.1 | 70.0 | 57.8 | - | 87.6 | |
| ArenaHard (GPT-4-1106) | 85.2 | 80.4 | 85.5 | 92.0 | - | 92.3 | |
| Code | LiveCodeBench (Pass@1-COT) | 33.8 | 34.2 | - | 53.8 | 63.4 | 65.9 |
| Codeforces (Percentile) | 20.3 | 23.6 | 58.7 | 93.4 | 96.6 | 96.3 | |
| Codeforces (Rating) | 717 | 759 | 1134 | 1820 | 2061 | 2029 | |
| SWE Verified (Resolved) | 50.8 | 38.8 | 42.0 | 41.6 | 48.9 | 49.2 | |
| Aider-Polyglot (Acc.) | 45.3 | 16.0 | 49.6 | 32.9 | 61.7 | 53.3 | |
| Math | AIME 2024 (Pass@1) | 16.0 | 9.3 | 39.2 | 63.6 | 79.2 | 79.8 |
| MATH-500 (Pass@1) | 78.3 | 74.6 | 90.2 | 90.0 | 96.4 | 97.3 | |
| CNMO 2024 (Pass@1) | 13.1 | 10.8 | 43.2 | 67.6 | - | 78.8 | |
| Chinese | CLUEWSC (EM) | 85.4 | 87.9 | 90.9 | 89.9 | - | 92.8 |
| C-Eval (EM) | 76.7 | 76.0 | 86.5 | 68.9 | - | 91.8 | |
| C-SimpleQA (Correct) | 55.4 | 58.7 | 68.0 | 40.3 | - | 63.7 |
Distilled Model Evaluation
| Model | AIME 2024 pass@1 | AIME 2024 cons@64 | MATH-500 pass@1 | GPQA Diamond pass@1 | LiveCodeBench pass@1 | CodeForces rating |
|---|---|---|---|---|---|---|
| GPT-4o-0513 | 9.3 | 13.4 | 74.6 | 49.9 | 32.9 | 759 |
| Claude-3.5-Sonnet-1022 | 16.0 | 26.7 | 78.3 | 65.0 | 38.9 | 717 |
| o1-mini | 63.6 | 80.0 | 90.0 | 60.0 | 53.8 | 1820 |
| QwQ-32B-Preview | 44.0 | 60.0 | 90.6 | 54.5 | 41.9 | 1316 |
| DeepSeek-R1-Distill-Qwen-1.5B | 28.9 | 52.7 | 83.9 | 33.8 | 16.9 | 954 |
| DeepSeek-R1-Distill-Qwen-7B | 55.5 | 83.3 | 92.8 | 49.1 | 37.6 | 1189 |
| DeepSeek-R1-Distill-Qwen-14B | 69.7 | 80.0 | 93.9 | 59.1 | 53.1 | 1481 |
| DeepSeek-R1-Distill-Qwen-32B | 72.6 | 83.3 | 94.3 | 62.1 | 57.2 | 1691 |
| DeepSeek-R1-Distill-Llama-8B | 50.4 | 80.0 | 89.1 | 49.0 | 39.6 | 1205 |
| DeepSeek-R1-Distill-Llama-70B | 70.0 | 86.7 | 94.5 | 65.2 | 57.5 | 1633 |
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 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:
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eagerNOTE: We recommend setting an appropriate temperature (between 0.5 and 0.7) when running these models, otherwise you may encounter issues with endless repetition or incoherent output.
7. License
This code repository and the model weights are licensed under the MIT License. DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that:- DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from Qwen-2.5 series, which are originally licensed under Apache 2.0 License, and now finetuned with 800k samples curated with DeepSeek-R1.
- DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under llama3.1 license.
- DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under llama3.3 license.
8. Citation
9. Contact
If you have any questions, please raise an issue or contact us at [email protected].📂 GGUF File List
| 📁 Filename | 📦 Size | ⚡ Download |
|---|---|---|
|
DeepSeek-R1-Distill-Qwen-14B-F16.gguf
LFS
FP16
|
27.52 GB | Download |
|
DeepSeek-R1-Distill-Qwen-14B-Q2_K.gguf
LFS
Q2
|
5.37 GB | Download |
|
DeepSeek-R1-Distill-Qwen-14B-Q2_K_L.gguf
LFS
Q2
|
5.54 GB | Download |
|
DeepSeek-R1-Distill-Qwen-14B-Q3_K_M.gguf
LFS
Q3
|
6.84 GB | Download |
|
DeepSeek-R1-Distill-Qwen-14B-Q4_K_M.gguf
Recommended
LFS
Q4
|
8.37 GB | Download |
|
DeepSeek-R1-Distill-Qwen-14B-Q5_K_M.gguf
LFS
Q5
|
9.79 GB | Download |
|
DeepSeek-R1-Distill-Qwen-14B-Q6_K.gguf
LFS
Q6
|
11.29 GB | Download |
|
DeepSeek-R1-Distill-Qwen-14B-Q8_0.gguf
LFS
Q8
|
14.62 GB | Download |