Instructions to use lucaelin/qwen3-0.6b-cn-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lucaelin/qwen3-0.6b-cn-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lucaelin/qwen3-0.6b-cn-gguf",
	filename="Qwen3-0.6B-grpo-ckpt700-q8_0.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 lucaelin/qwen3-0.6b-cn-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lucaelin/qwen3-0.6b-cn-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf lucaelin/qwen3-0.6b-cn-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lucaelin/qwen3-0.6b-cn-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf lucaelin/qwen3-0.6b-cn-gguf:Q8_0

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 lucaelin/qwen3-0.6b-cn-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf lucaelin/qwen3-0.6b-cn-gguf:Q8_0

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 lucaelin/qwen3-0.6b-cn-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lucaelin/qwen3-0.6b-cn-gguf:Q8_0

Use Docker

docker model run hf.co/lucaelin/qwen3-0.6b-cn-gguf:Q8_0

LM Studio
Jan
Ollama
How to use lucaelin/qwen3-0.6b-cn-gguf with Ollama:
```
ollama run hf.co/lucaelin/qwen3-0.6b-cn-gguf:Q8_0
```

Unsloth Studio

How to use lucaelin/qwen3-0.6b-cn-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 lucaelin/qwen3-0.6b-cn-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 lucaelin/qwen3-0.6b-cn-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lucaelin/qwen3-0.6b-cn-gguf to start chatting

How to use lucaelin/qwen3-0.6b-cn-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lucaelin/qwen3-0.6b-cn-gguf:Q8_0

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": "lucaelin/qwen3-0.6b-cn-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lucaelin/qwen3-0.6b-cn-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lucaelin/qwen3-0.6b-cn-gguf:Q8_0

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 lucaelin/qwen3-0.6b-cn-gguf:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use lucaelin/qwen3-0.6b-cn-gguf with Docker Model Runner:
```
docker model run hf.co/lucaelin/qwen3-0.6b-cn-gguf:Q8_0
```

Lemonade

How to use lucaelin/qwen3-0.6b-cn-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lucaelin/qwen3-0.6b-cn-gguf:Q8_0

Run and chat with the model

lemonade run user.qwen3-0.6b-cn-gguf-Q8_0

List all available models

lemonade list

qwen3-0.6b-cn-gguf

Quantized GGUF export for a COVAS:NEXT fine-tuning experiment based on Qwen/Qwen3-0.6B.

This repo currently publishes the official checkpoint-700 release from the local MLX GRPO training run.

This is still an experimental release, but checkpoint-700 is the selected public artifact because it gave the best practical balance and felt less overcooked than later checkpoints.

Status

Official file: Qwen3-0.6B-grpo-ckpt700-q8_0.gguf
Source checkpoint: 700
Format: Q8_0 GGUF
Intended use: local COVAS:NEXT experimentation and evaluation

Selection Rationale

Later checkpoints achieved slightly higher peak benchmark scores in some cases, but checkpoint-700 was selected as the release candidate because it appears less over-optimized and more balanced in practical behavior.
In particular, checkpoint-700 preserves very strong tool behavior while avoiding some of the later-run drift seen in higher-update checkpoints.

Benchmark Snapshot

Judge-based native Qwen eval snapshot for checkpoint-700:

Overall: 240/276 (87.0%)
Tool calling: 96/108 (88.9%)
Event reaction: 31/42 (73.8%)
Contextual QA: 47/60 (78.3%)
Tool result summarization: 66/66 (100.0%)
Tool micro:
- call made: 108/108
- name correct: 102/108
- args correct: 102/108
Avg case score: 0.807
Strict all-6 pass cases: 38/46

Notes

Base model: Qwen/Qwen3-0.6B
Project: COVAS:NEXT training experiments for Elite Dangerous ship-assistant behavior
Exported from the local MLX GRPO training pipeline and converted to GGUF Q8_0 for llama.cpp-style runtimes.
See the source project experiment log for checkpoint comparisons and validation notes.

Downloads last month: 20

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

8-bit

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