Instructions to use grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-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 grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-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 grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-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 grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF with Ollama:
```
ollama run hf.co/grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-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 grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-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 grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M
```

Lemonade

How to use grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grapevine-AI/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-0528-Qwen3-8B-GGUF-Q4_K_M

List all available models

lemonade list

What is this?

DeepSeek (深度求索)が公式自らDeepSeek-R1-0528をQwen3-8Bに蒸留したThinkingモデル、DeepSeek-R1-0528-Qwen3-8BをGGUFフォーマットに変換したものです。

imatrix dataset

日本語能力を重視し、日本語が多量に含まれるTFMC/imatrix-dataset-for-japanese-llmデータセットを使用しました。

Note

BF16推論時のElyza_tasks 100スコアは3.95でした（Gemini 2.0 Flashで採点）。

Environment

Windows版llama.cpp-b5215および同時リリースのconvert-hf-to-gguf.pyを使用して量子化作業を実施しました。

License

MIT License

Developer

Alibaba Cloud & DeepSeek (深度求索)

Downloads last month: 21

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

16-bit

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