Instructions to use unsloth/DeepSeek-R1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/DeepSeek-R1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="unsloth/DeepSeek-R1-GGUF", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-R1-GGUF", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-GGUF", trust_remote_code=True)

llama-cpp-python

How to use unsloth/DeepSeek-R1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/DeepSeek-R1-GGUF",
	filename="DeepSeek-R1-BF16/DeepSeek-R1.BF16-00001-of-00030.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use unsloth/DeepSeek-R1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/DeepSeek-R1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/unsloth/DeepSeek-R1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use unsloth/DeepSeek-R1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/DeepSeek-R1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/DeepSeek-R1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/DeepSeek-R1-GGUF:Q4_K_M

SGLang

How to use unsloth/DeepSeek-R1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "unsloth/DeepSeek-R1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/DeepSeek-R1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "unsloth/DeepSeek-R1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/DeepSeek-R1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use unsloth/DeepSeek-R1-GGUF with Ollama:
```
ollama run hf.co/unsloth/DeepSeek-R1-GGUF:Q4_K_M
```

Unsloth Studio

How to use unsloth/DeepSeek-R1-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/DeepSeek-R1-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/DeepSeek-R1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/DeepSeek-R1-GGUF to start chatting

Docker Model Runner
How to use unsloth/DeepSeek-R1-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/DeepSeek-R1-GGUF:Q4_K_M
```

Lemonade

How to use unsloth/DeepSeek-R1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/DeepSeek-R1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-GGUF-Q4_K_M

List all available models

lemonade list

shimmyshimmer commited on Jan 20, 2025

Commit

166be67

verified ·

1 Parent(s): 5ea4779

Update README.md

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README.md +35 -2

README.md CHANGED Viewed

@@ -9,8 +9,41 @@ tags:
 - transformers
 ---
-## ***See [our collection](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5) for versions of Deepseek-R1 including GGUF and original formats.***
 # 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

 - transformers
 ---
+## ***See [our collection](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5) for versions of Deepseek-R1 including GGUF and 4-bit formats.***
+### Instructions to run this model in llama.cpp:
+Or you can view more detailed instructions here: [unsloth.ai/blog/deepseek-r1](https://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**
+   ```bash
+   ./llama.cpp/llama-cli \
+       --model unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-Q4_K_M.gguf \
+       --cache-type-k q8_0 \
+       --threads 16 \
+       --prompt '<｜User｜>What is 1+1?<｜Assistant｜>' \
+       -no-cnv
+   ```
+   Example output:
+   ```txt
+    <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.
+   ```bash
+   ./llama.cpp/llama-cli \
+   --model unsloth/DeepSeek-R1-GGUF/DeepSeek-R1-Q4_K_M.gguf
+   --cache-type-k q8_0
+   --threads 16
+   --prompt '<｜User｜>What is 1+1?<｜Assistant｜>'
+   --n-gpu-layers 20 \
+    -no-cnv
+   ```
 # 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