- Libraries
- MLX
How to use posttrainllm/qwen3-4b-multibackend-distilled 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("posttrainllm/qwen3-4b-multibackend-distilled")
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 posttrainllm/qwen3-4b-multibackend-distilled with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "posttrainllm/qwen3-4b-multibackend-distilled"
Configure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "posttrainllm/qwen3-4b-multibackend-distilled"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- OpenClaw new
How to use posttrainllm/qwen3-4b-multibackend-distilled with OpenClaw:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "posttrainllm/qwen3-4b-multibackend-distilled"
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 "posttrainllm/qwen3-4b-multibackend-distilled" \
--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"
- MLX LM
How to use posttrainllm/qwen3-4b-multibackend-distilled with MLX LM:
Generate or start a chat session
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "posttrainllm/qwen3-4b-multibackend-distilled"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "posttrainllm/qwen3-4b-multibackend-distilled"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "posttrainllm/qwen3-4b-multibackend-distilled",
"messages": [
{"role": "user", "content": "Hello"}
]
}' - Hermes Agent
How to use posttrainllm/qwen3-4b-multibackend-distilled 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 "posttrainllm/qwen3-4b-multibackend-distilled"
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 posttrainllm/qwen3-4b-multibackend-distilled
Run Hermes
hermes
