Mozilla packaged the Deepseek R1 Distil models into executable weights that we
call llamafiles. This gives
you the easiest fastest way to use the model on Linux, MacOS, Windows,
FreeBSD, OpenBSD and NetBSD systems you control on both AMD64 and ARM64.
Software Last Updated: 2025-03-31
Llamafile Version: 0.9.2
Quickstart
To get started, you need both the Deepseek R1 Distil weights, and the llamafile
software. Both of them are included in a single file, which can be
downloaded and run as follows:
The default mode of operation for these llamafiles is our new command
line chatbot interface.
Usage
You can use triple quotes to ask questions on multiple lines. You can
pass commands like /stats and /context to see runtime status
information. You can change the system prompt by passing the -p "new system prompt" flag. You can press CTRL-C to interrupt the model.
Finally CTRL-D may be used to exit.
If you prefer to use a web GUI, then a --server mode is provided, that
will open a tab with a chatbot and completion interface in your browser.
For additional help on how it may be used, pass the --help flag. The
server also has an OpenAI API compatible completions endpoint that can
be accessed via Python using the openai pip package.
An advanced CLI mode is provided that's useful for shell scripting. You
can use it by passing the --cli flag. For additional help on how it
may be used, pass the --help flag.
./DeepSeek-R1-Distill-Llama-8B-Q6_K.llamafile --cli -p 'four score and seven' --log-disable
On Windows there's a 4GB limit on executable sizes.
Context Window
This model has a max context window size of 128k tokens. By default, a
context window size of 8192 tokens is used. You can ask llamafile
to use the maximum context size by passing the -c 0 flag. That's big
enough for a small book. If you want to be able to have a conversation
with your book, you can use the -f book.txt flag.
GPU Acceleration
On GPUs with sufficient RAM, the -ngl 999 flag may be passed to use
the system's NVIDIA or AMD GPU(s). On Windows, only the graphics card
driver needs to be installed if you own an NVIDIA GPU. On Windows, if
you have an AMD GPU, you should install the ROCm SDK v6.1 and then pass
the flags --recompile --gpu amd the first time you run your llamafile.
On NVIDIA GPUs, by default, the prebuilt tinyBLAS library is used to
perform matrix multiplications. This is open source software, but it
doesn't go as fast as closed source cuBLAS. If you have the CUDA SDK
installed on your system, then you can pass the --recompile flag to
build a GGML CUDA library just for your system that uses cuBLAS. This
ensures you get maximum performance.
llamafile is a new format introduced by Mozilla on Nov 20th 2023. It
uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp
binaries that run on the stock installs of six OSes for both ARM64 and
AMD64.
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.
DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base.
For more details regarding the model architecture, please refer to DeepSeek-V3 repository.
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 recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:
Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
Avoid adding a system prompt; all instructions should be contained within the user prompt.
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{}."
When evaluating model performance, it is recommended to conduct multiple tests and average the results.
Additionally, we have observed that the DeepSeek-R1 series models tend to bypass thinking pattern (i.e., outputting "<think>\n\n</think>") when responding to certain queries, which can adversely affect the model's performance.
To ensure that the model engages in thorough reasoning, we recommend enforcing the model to initiate its response with "<think>\n" at the beginning of every 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
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
author={DeepSeek-AI},
year={2025},
eprint={2501.12948},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.12948},
}
9. Contact
If you have any questions, please raise an issue or contact us at service@deepseek.com.
