Instructions to use leonsarmiento/Domyn-Small-v1.0-8bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leonsarmiento/Domyn-Small-v1.0-8bit-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("leonsarmiento/Domyn-Small-v1.0-8bit-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 Settings
LM Studio

How to use leonsarmiento/Domyn-Small-v1.0-8bit-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 "leonsarmiento/Domyn-Small-v1.0-8bit-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": "leonsarmiento/Domyn-Small-v1.0-8bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use leonsarmiento/Domyn-Small-v1.0-8bit-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 "leonsarmiento/Domyn-Small-v1.0-8bit-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 leonsarmiento/Domyn-Small-v1.0-8bit-mlx

Run Hermes

hermes

MLX LM

How to use leonsarmiento/Domyn-Small-v1.0-8bit-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 "leonsarmiento/Domyn-Small-v1.0-8bit-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "leonsarmiento/Domyn-Small-v1.0-8bit-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "leonsarmiento/Domyn-Small-v1.0-8bit-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

leonsarmiento/Domyn-Small-v1.0-8bit-mlx

This model was converted to MLX format from domyn/Domyn-Small-v1.0 using 8-bit uniform quantization (8.501 bits per weight) optimized for Apple Silicon.

Domyn-Small-v1.0 is a 10B-parameter text-only model initialized from Italia 10B and continually pre-trained on 503B tokens. Built on the Nemotron architecture with ReLU² activation, it features Grouped-Query Attention (48 query heads, 8 KV heads), a 256K-token SentencePiece BPE vocabulary, and supports a dual-mode "Thinking on/off" toggle for chain-of-thought reasoning. Native context window is 32K tokens, extensible to 128K via YaRN.

Quantization Details

Property	Value
Quantization	8-bit uniform
Bits per weight	8.501
Group size	64
Model size	9.7 GB
Shards	2
Source dtype	bfloat16

Use with mlx-lm

pip install -U mlx-lm

python -m mlx_lm.generate --model leonsarmiento/Domyn-Small-v1.0-8bit-mlx --max-tokens 256 --prompt "Ciao, come stai?"

Recommended Inference Parameters

Thinking Off (default)

Parameter	Value
Temperature	0.1
Top-p	0.95
Top-k	50
Min-p	0.1

Thinking On

Parameter	Value
Temperature	0.6
Top-p	0.90
Top-k	25
Min-p	0.1

⚠️ Greedy decoding should not be used in thinking mode as it degrades reasoning quality and causes repetition.

Chat Template

This model uses a custom chat template with <extra_id_0> / <extra_id_1> role markers. Thinking mode is controlled by appending thinking on or thinking off to the system prompt. The template also supports tool calling via <tool_call> XML tags.

The chat_template.jinja file is included and the template is injected into tokenizer_config.json for compatibility.

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Safetensors

Model size

10B params

Tensor type

F16

U32

MLX

Hardware compatibility

8-bit

Model tree for leonsarmiento/Domyn-Small-v1.0-8bit-mlx

Base model

domyn/Domyn-Small-v1.0

Quantized

(4)

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