Zen Eco (open-weight tier)
Collection
Open-weight 'eco' tier — 4B instruct / thinking / agent. • 6 items • Updated
How to use zenlm/zen-eco-4b-agent-mlx 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("zenlm/zen-eco-4b-agent-mlx")
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)How to use zenlm/zen-eco-4b-agent-mlx with Pi:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "zenlm/zen-eco-4b-agent-mlx"
# 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": "zenlm/zen-eco-4b-agent-mlx"
}
]
}
}
}# Start Pi in your project directory: pi
How to use zenlm/zen-eco-4b-agent-mlx with Hermes Agent:
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "zenlm/zen-eco-4b-agent-mlx"
# 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 zenlm/zen-eco-4b-agent-mlx
hermes
How to use zenlm/zen-eco-4b-agent-mlx with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "zenlm/zen-eco-4b-agent-mlx"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "zenlm/zen-eco-4b-agent-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "zenlm/zen-eco-4b-agent-mlx",
"messages": [
{"role": "user", "content": "Hello"}
]
}'Optimized MLX quantization of Zen Eco 4B Agent for Apple Silicon.
| Property | Value |
|---|---|
| Parameters | 4B |
| Format | MLX 4-bit quantized |
| License | Apache 2.0 |
| Developer | Hanzo AI |
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("zenlm/zen-eco-4b-agent-mlx")
prompt = "Hello, who are you?"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
4-bit
Base model
zenlm/zen-agent-4b