Instructions to use Vontra/Solar-Open2-250B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vontra/Solar-Open2-250B-MLX-4bit 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("Vontra/Solar-Open2-250B-MLX-4bit")

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 Vontra/Solar-Open2-250B-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Vontra/Solar-Open2-250B-MLX-4bit"

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": "Vontra/Solar-Open2-250B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Vontra/Solar-Open2-250B-MLX-4bit 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 "Vontra/Solar-Open2-250B-MLX-4bit"

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 Vontra/Solar-Open2-250B-MLX-4bit

Run Hermes

hermes

OpenClaw new

How to use Vontra/Solar-Open2-250B-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Vontra/Solar-Open2-250B-MLX-4bit"

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 "Vontra/Solar-Open2-250B-MLX-4bit" \
  --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 Vontra/Solar-Open2-250B-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Vontra/Solar-Open2-250B-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Vontra/Solar-Open2-250B-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Vontra/Solar-Open2-250B-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Solar-Open2-250B-MLX-4bit / README.md

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	---
	language:
	- en
	- ko
	- ja
	library_name: mlx
	pipeline_tag: text-generation
	license: other
	license_name: upstage-solar-license
	license_link: LICENSE
	base_model: upstage/Solar-Open2-250B
	tags:
	- mlx
	- solar
	- solar-open2
	- moe
	- text-generation
	- quantized
	- 4bit
	---

	# Solar-Open2-250B-MLX-4bit

	Built with Solar. This is an MLX 4-bit affine quantization of [upstage/Solar-Open2-250B](https://huggingface.co/upstage/Solar-Open2-250B), converted for Apple Silicon / MLX workflows.

	## Details

	- Source model: `upstage/Solar-Open2-250B`
	- Quantization: 4-bit affine, group size 64
	- Local size: 131G
	- Weight shards: 29
	- Architecture: Solar Open 2 hybrid-attention MoE, 250B total / ~15B active parameters
	- Context: source model advertises 1M-token context; practical MLX context depends on memory and runtime settings

	## Important runtime notes

	Solar Open2 is not yet a stock `mlx-lm` architecture in many installs. This repo includes `solar_open2.py`; launch with `--trust-remote-code` when serving or loading from Hugging Face.

	```bash
	mlx_lm.server \
	--model Vontra/Solar-Open2-250B-MLX-4bit \
	--host 0.0.0.0 \
	--port 8021 \
	--trust-remote-code \
	--temp 0.2 \
	--top-p 0.9 \
	--max-tokens 32768
	```

	You may see a `transformers` warning that mentions loading `model_type=solar_open2` into a blank model type. With the included custom MLX loader this warning is expected; the important check is that the model actually loads.

	The tokenizer template uses Solar/Whale-style tool markers such as `<\|tool_call:start\|>` and `<\|tool_arg:start\|>`. For OpenAI-compatible tool calling, your serving runtime must parse those markers into structured `tool_calls`. Plain text generation does not need this parser.

	## Use with MLX

	This repo includes a small `solar_open2.py` MLX loader because upstream `mlx-lm` does not yet ship native Solar Open 2 support.

	```bash
	pip install -U mlx-lm
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("Vontra/Solar-Open2-250B-MLX-4bit")
	prompt = "Write a short Python function that validates an IPv4 CIDR string."
	print(generate(model, tokenizer, prompt=prompt, max_tokens=256, verbose=True))
	```

	## Notes

	This is an independent community conversion under the Vontra organization. It is not an official Upstage release.

	## License

	The source model is released under the Upstage Solar License. A copy is included in `LICENSE`. Please review the upstream model card and license before use or redistribution.