Instructions to use unsloth/DeepSeek-R1-0528-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/DeepSeek-R1-0528-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="unsloth/DeepSeek-R1-0528-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-R1-0528-GGUF", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-0528-GGUF", trust_remote_code=True)

llama-cpp-python

How to use unsloth/DeepSeek-R1-0528-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/DeepSeek-R1-0528-GGUF",
	filename="BF16/DeepSeek-R1-0528-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 Settings

llama.cpp

How to use unsloth/DeepSeek-R1-0528-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

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-R1-0528-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

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-R1-0528-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/DeepSeek-R1-0528-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/DeepSeek-R1-0528-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-R1-0528-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

SGLang

How to use unsloth/DeepSeek-R1-0528-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-R1-0528-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-R1-0528-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-R1-0528-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-R1-0528-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use unsloth/DeepSeek-R1-0528-GGUF with Ollama:
```
ollama run hf.co/unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/DeepSeek-R1-0528-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-R1-0528-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-R1-0528-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-R1-0528-GGUF to start chatting

How to use unsloth/DeepSeek-R1-0528-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/DeepSeek-R1-0528-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use unsloth/DeepSeek-R1-0528-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use unsloth/DeepSeek-R1-0528-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/DeepSeek-R1-0528-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/DeepSeek-R1-0528-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.DeepSeek-R1-0528-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Classic Q4_K_M?

by EricTri - opened May 29, 2025

Discussion

EricTri

May 29, 2025

Hi,

These GGUF quants are using the more modern UD 2.0 quantization method and are incompatible with KTransformers.

https://github.com/kvcache-ai/ktransformers/issues/1195

Would it be possible to upload a Q4_K_M quant using the old quantization method?

shimmyshimmer

Unsloth AI org May 29, 2025

A lot of them are still uploading! Please wait for our official announcement! <3

EricTri

May 29, 2025

Amazing! Thanks for all your hard work

VladNC

May 29, 2025

After downloading it for 93 minutes, I got this with llama.cpp v5124 and Q4_K_M:

93.57.161.372 E llama_model_load: error loading model: check_tensor_dims: tensor 'blk.0.attn_q_b.weight' has wrong shape; expected  1536, 73728, got  1536, 24576,     1,     1
93.57.161.382 E llama_model_load_from_file_impl: failed to load model

When I attempted to run it again, it found a different version on HF and it's downloading it again:

0.00.870.633 W common_download_file_single: ETag header is different ("8ad0d2516c8115fdd0a83085b66211307e4fab8f3fbdf48e373d5f6527266957" != "e10f1d54ba46812c08260ec3b560ff14c806f8ffb3cd811a227cea0d77cc0f4f"): triggering a new download

I'm guessing there was some problem with the initial version and it has been re-uploaded?

VladNC

May 29, 2025

OK, from the other threads apparently I need to update my llama.cpp to a more recent version to get rid of that error. After it completes downloading the second time I'll do that.

VladNC

May 29, 2025

With the latest llama.cpp version (5537) it loaded, but it took 50 minutes on my system as it was loading the model with a single thread, one layer at a time, and doing some tensor repacking for some reason:

.45.09.074.148 D repack: repack tensor blk.58.ffn_up_exps.weight with q4_K_8x8
45.28.689.530 D repack: repack tensor blk.58.ffn_gate_shexp.weight with q4_K_8x8
45.28.770.822 D repack: repack tensor blk.58.ffn_up_shexp.weight with q4_K_8x8
45.28.852.826 D repack: repack tensor blk.59.attn_q_a.weight with q4_K_8x8
45.28.920.638 D repack: repack tensor blk.59.attn_q_b.weight with q4_K_8x8
45.29.154.179 D repack: repack tensor blk.59.attn_kv_a_mqa.weight with q4_K_8x8
45.29.178.712 D repack: repack tensor blk.59.attn_output.weight with q4_K_8x8
45.30.517.695 D repack: repack tensor blk.59.ffn_gate_exps.weight with q4_K_8x8
.45.49.762.411 D repack: repack tensor blk.59.ffn_up_exps.weight with q4_K_8x8
46.08.744.034 D repack: repack tensor blk.59.ffn_gate_shexp.weight with q4_K_8x8
46.08.813.393 D repack: repack tensor blk.59.ffn_up_shexp.weight with q4_K_8x8
46.08.881.724 D repack: repack tensor blk.60.attn_q_a.weight with q4_K_8x8
46.08.934.546 D repack: repack tensor blk.60.attn_q_b.weight with q4_K_8x8
46.09.107.476 D repack: repack tensor blk.60.attn_kv_a_mqa.weight with q4_K_8x8
46.09.131.141 D repack: repack tensor blk.60.attn_output.weight with q4_K_8x8
46.09.762.706 D repack: repack tensor blk.60.ffn_gate_exps.weight with q4_K_8x8
.46.29.210.163 D repack: repack tensor blk.60.ffn_up_exps.weight with q4_K_8x8
46.48.630.578 D repack: repack tensor blk.60.ffn_gate_shexp.weight with q4_K_8x8
46.48.712.425 D repack: repack tensor blk.60.ffn_up_shexp.weight with q4_K_8x8

In the past I used 6 bit quantizations for DeepSeek models, lmstudio-community_DeepSeek-R1-GGUF_DeepSeek-R1-Q6_K to be precise, and I don't remember llama.cpp doing a repacking for it while loading it. Any chance you'll make a 6 bit quantization for this model available too?

shimmyshimmer

Unsloth AI org May 29, 2025

.45.09.074.148 D repack: repack tensor blk.58.ffn_up_exps.weight with q4_K_8x8
45.28.689.530 D repack: repack tensor blk.58.ffn_gate_shexp.weight with q4_K_8x8
45.28.770.822 D repack: repack tensor blk.58.ffn_up_shexp.weight with q4_K_8x8
45.28.852.826 D repack: repack tensor blk.59.attn_q_a.weight with q4_K_8x8
45.28.920.638 D repack: repack tensor blk.59.attn_q_b.weight with q4_K_8x8
45.29.154.179 D repack: repack tensor blk.59.attn_kv_a_mqa.weight with q4_K_8x8
45.29.178.712 D repack: repack tensor blk.59.attn_output.weight with q4_K_8x8
45.30.517.695 D repack: repack tensor blk.59.ffn_gate_exps.weight with q4_K_8x8
.45.49.762.411 D repack: repack tensor blk.59.ffn_up_exps.weight with q4_K_8x8
46.08.744.034 D repack: repack tensor blk.59.ffn_gate_shexp.weight with q4_K_8x8
46.08.813.393 D repack: repack tensor blk.59.ffn_up_shexp.weight with q4_K_8x8
46.08.881.724 D repack: repack tensor blk.60.attn_q_a.weight with q4_K_8x8
46.08.934.546 D repack: repack tensor blk.60.attn_q_b.weight with q4_K_8x8
46.09.107.476 D repack: repack tensor blk.60.attn_kv_a_mqa.weight with q4_K_8x8
46.09.131.141 D repack: repack tensor blk.60.attn_output.weight with q4_K_8x8
46.09.762.706 D repack: repack tensor blk.60.ffn_gate_exps.weight with q4_K_8x8
.46.29.210.163 D repack: repack tensor blk.60.ffn_up_exps.weight with q4_K_8x8
46.48.630.578 D repack: repack tensor blk.60.ffn_gate_shexp.weight with q4_K_8x8
46.48.712.425 D repack: repack tensor blk.60.ffn_up_shexp.weight with q4_K_8x8

Yes ofc it's just still uploading

shimmyshimmer

Unsloth AI org May 30, 2025

@EricTri @VladNC they're all up now!

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