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

Libraries

How to use vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf",
	filename="DeepSeek-R1-0528-Qwen3-8B.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf
# Run inference directly in the terminal:
llama-cli -hf vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf
# Run inference directly in the terminal:
llama-cli -hf vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf

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 vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf
# Run inference directly in the terminal:
./llama-cli -hf vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf

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 vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf

Use Docker

docker model run hf.co/vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf

LM Studio
Jan
Ollama
How to use vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf with Ollama:
```
ollama run hf.co/vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf
```

Unsloth Studio

How to use vagrillo/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 vagrillo/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 vagrillo/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 vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf to start chatting

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

Lemonade

How to use vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf

Run and chat with the model

lemonade run user.DeepSeek-R1-0528-Qwen3-8B-gguf-{{QUANT_TAG}}

List all available models

lemonade list

GGUF conversion and 8 bit quantization of DeepSeek-R1-0528-Qwen3-8B https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

Downloads last month: 13

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for vagrillo/DeepSeek-R1-0528-Qwen3-8B-gguf

Base model

deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

Quantized

(82)

this model