Text Generation
MLX
Safetensors
English
Japanese
Chinese
qwen2
mlx-lm
quantized
4-bit precision
qwen
qwen2.5
chat
instruct
conversational
Instructions to use noricha/Qwen2.5-7B-Instruct-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use noricha/Qwen2.5-7B-Instruct-MLX-4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("noricha/Qwen2.5-7B-Instruct-MLX-4bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use noricha/Qwen2.5-7B-Instruct-MLX-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "noricha/Qwen2.5-7B-Instruct-MLX-4bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "noricha/Qwen2.5-7B-Instruct-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use noricha/Qwen2.5-7B-Instruct-MLX-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "noricha/Qwen2.5-7B-Instruct-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "noricha/Qwen2.5-7B-Instruct-MLX-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "noricha/Qwen2.5-7B-Instruct-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use noricha/Qwen2.5-7B-Instruct-MLX-4bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "noricha/Qwen2.5-7B-Instruct-MLX-4bit"
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 noricha/Qwen2.5-7B-Instruct-MLX-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use noricha/Qwen2.5-7B-Instruct-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "noricha/Qwen2.5-7B-Instruct-MLX-4bit"
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 "noricha/Qwen2.5-7B-Instruct-MLX-4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Qwen2.5-7B-Instruct-MLX-4bit
This model is an MLX 4-bit quantized conversion of
Qwen/Qwen2.5-7B-Instruct,
produced with mlx-lm.
Base model
- Base:
Qwen/Qwen2.5-7B-Instruct - License: Apache-2.0
- Context length: 131,072 tokens (with YaRN)
- Generation length: 8,192 tokens
- Quantization: 4-bit (group size 64, mixed 4.5 bpw)
Use with MLX-LM
pip install -U mlx-lm
mlx_lm.generate \
--model noricha/Qwen2.5-7B-Instruct-MLX-4bit \
--system-prompt "あなたは日本語で簡潔に答えるアシスタントです。" \
--prompt "MLXモデルとは何ですか?" \
--max-tokens 200 --temp 0.7 --top-p 0.9
Use with LM Studio
LM Studio 0.3.4 以降の MLX Engine で読み込み可能です。アプリ内検索または CLI:
lms get noricha/Qwen2.5-7B-Instruct-MLX-4bit
Local benchmark (Apple M3 Ultra)
| 項目 | 値 |
|---|---|
| モデルサイズ | 4.0 GB (safetensors) |
| Generation | ~133 tok/s |
| Prompt eval | ~273 tok/s |
| Peak memory | 4.43 GB |
同シリーズ 0.5B / 1.5B と比較して、日本語の一般知識・翻訳・コード生成の質が明確に向上(実用チャットレベル)。 用途・制限は元モデルの Qwen2.5-7B-Instruct を参照してください。
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Model size
8B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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4-bit