Text Generation
MLX
Safetensors
Korean
English
qwen2
qwen2.5
korean
instruct
8bit
conversational
8-bit precision
Instructions to use mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit 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("mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit") 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 mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit"
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": "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit 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 "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit"
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 mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit"
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 "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit" \ --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-kowiki-qa-8bit mlx convert model
- Original model is beomi/Qwen2.5-7B-Instruct-kowiki-qa
Requirement
pip install mlx-lm
Usage
-
mlx_lm.generate --model mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit --prompt "νλμ΄ νλ μ΄μ κ° λμΌ?" -
from mlx_lm import load, generate model, tokenizer = load( "mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit", tokenizer_config={"trust_remote_code": True}, ) prompt = "νλμ΄ νλ μ΄μ κ° λμΌ?" messages = [ {"role": "system", "content": "λΉμ μ μΉμ² ν μ±λ΄μ λλ€."}, {"role": "user", "content": prompt}, ] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) text = generate( model, tokenizer, prompt=prompt, # verbose=True, # max_tokens=8196, # temp=0.0, ) -
mlx_lm.server --model mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit --host 0.0.0.0import openai client = openai.OpenAI( base_url="http://localhost:8080/v1", ) prompt = "νλμ΄ νλ μ΄μ κ° λμΌ?" messages = [ {"role": "system", "content": "λΉμ μ μΉμ ν μ±λ΄μ λλ€.",}, {"role": "user", "content": prompt}, ] res = client.chat.completions.create( model='mlx-community/Qwen2.5-7B-Instruct-kowiki-qa-8bit', messages=messages, temperature=0.2, ) print(res.choices[0].message.content)
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Model size
8B params
Tensor type
U32
Β·
F16 Β·
Hardware compatibility
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8-bit