Instructions to use Vontra/Solar-Open2-250B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Vontra/Solar-Open2-250B-MLX-8bit 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-8bit") 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 Vontra/Solar-Open2-250B-MLX-8bit 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-8bit"
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-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Vontra/Solar-Open2-250B-MLX-8bit 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-8bit"
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-8bit
Run Hermes
hermes
- OpenClaw new
How to use Vontra/Solar-Open2-250B-MLX-8bit 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-8bit"
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-8bit" \ --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-8bit 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-8bit"
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-8bit" # 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-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
Solar-Open2-250B-MLX-8bit
Built with Solar. This is an MLX 8-bit affine quantization of upstage/Solar-Open2-250B, converted for Apple Silicon / MLX workflows.
Details
- Source model:
upstage/Solar-Open2-250B - Quantization: 8-bit affine, group size 64
- Local size: 248G
- Weight shards: 62
- 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.
mlx_lm.server \
--model Vontra/Solar-Open2-250B-MLX-8bit \
--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.
pip install -U mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Vontra/Solar-Open2-250B-MLX-8bit")
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.
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