Instructions to use samuelfaj/distill2-0.6B-4bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samuelfaj/distill2-0.6B-4bit-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("samuelfaj/distill2-0.6B-4bit-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)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

Pi new

How to use samuelfaj/distill2-0.6B-4bit-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "samuelfaj/distill2-0.6B-4bit-MLX"

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": "samuelfaj/distill2-0.6B-4bit-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use samuelfaj/distill2-0.6B-4bit-MLX 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 "samuelfaj/distill2-0.6B-4bit-MLX"

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 samuelfaj/distill2-0.6B-4bit-MLX

Run Hermes

hermes

MLX LM

How to use samuelfaj/distill2-0.6B-4bit-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "samuelfaj/distill2-0.6B-4bit-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "samuelfaj/distill2-0.6B-4bit-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "samuelfaj/distill2-0.6B-4bit-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

distill2-0.6B-4bit-MLX / adapter_config.json

samuelfaj

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	{
	"adapter_path": "experiments/qwen3-0.6b/qlora-4bit-v1",
	"batch_size": 2,
	"clear_cache_threshold": 0,
	"config": null,
	"data": "data",
	"fine_tune_type": "lora",
	"grad_accumulation_steps": 1,
	"grad_checkpoint": false,
	"iters": 6000,
	"learning_rate": 3e-05,
	"lora_parameters": {
	"rank": 8,
	"dropout": 0.0,
	"scale": 20.0
	},
	"lr_schedule": null,
	"mask_prompt": true,
	"max_seq_length": 4096,
	"model": "models/Qwen3-0.6B-q4",
	"num_layers": -1,
	"optimizer": "adam",
	"optimizer_config": {
	"adam": {},
	"adamw": {},
	"muon": {},
	"sgd": {},
	"adafactor": {}
	},
	"project_name": null,
	"report_to": null,
	"resume_adapter_file": null,
	"save_every": 500,
	"seed": 42,
	"steps_per_eval": 200,
	"steps_per_report": 10,
	"test": false,
	"test_batches": 500,
	"train": true,
	"val_batches": 20
	}