Instructions to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF",
	filename="Qwen2.5-Coder-0.5B-Instruct-BF16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-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 grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-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/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-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/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF with Ollama:
```
ollama run hf.co/grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-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/Qwen2.5-Coder-0.5B-Instruct-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/Qwen2.5-Coder-0.5B-Instruct-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/Qwen2.5-Coder-0.5B-Instruct-GGUF to start chatting

Pi new

How to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M

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": "grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-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 grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M

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 grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grapevine-AI/Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-0.5B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

What is this?

投機的デコードに活用できるQwen2.5-Coderの超小型モデルQwen2.5-Coder-0.5B-Instructを日本語imatrixで量子化したものです。

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。
なお、imatrixの算出においてはf32精度のモデルを使用しました。これは、本来の数値精度であるbf16でのimatrix計算に現行のCUDA版llama.cppが対応していないためです。

Chat template

<|im_start|>system
ここにSystem Promptを書きます。<|im_end|>
<|im_start|>user
ここにMessageを書きます。<|im_end|>
<|im_start|>assistant

Environment

Windows版llama.cpp-b4170およびllama.cpp-b3472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

Apache 2.0

Developer

Alibaba Cloud

Downloads last month: 5

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

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