Instructions to use TensorFold/Solar-Open2-250B-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TensorFold/Solar-Open2-250B-MLX-6bit 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("TensorFold/Solar-Open2-250B-MLX-6bit") 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 TensorFold/Solar-Open2-250B-MLX-6bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TensorFold/Solar-Open2-250B-MLX-6bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "TensorFold/Solar-Open2-250B-MLX-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use TensorFold/Solar-Open2-250B-MLX-6bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "TensorFold/Solar-Open2-250B-MLX-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "TensorFold/Solar-Open2-250B-MLX-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TensorFold/Solar-Open2-250B-MLX-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use TensorFold/Solar-Open2-250B-MLX-6bit 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 "TensorFold/Solar-Open2-250B-MLX-6bit"
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 TensorFold/Solar-Open2-250B-MLX-6bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TensorFold/Solar-Open2-250B-MLX-6bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TensorFold/Solar-Open2-250B-MLX-6bit"
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 "TensorFold/Solar-Open2-250B-MLX-6bit" \ --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"
Download OMLX.md from TensorFold/Solar-Open2-250B-MLX-6bit: direct link, hf CLI and curl.
- Browser
- Download file 2.07 kB
-
https://huggingface.co/TensorFold/Solar-Open2-250B-MLX-6bit/resolve/7ee0a13f1eef4ddfdc54b4e355f13e103777fb7c/OMLX.md
- Command line
-
hf download hf://TensorFold/Solar-Open2-250B-MLX-6bit@7ee0a13f1eef4ddfdc54b4e355f13e103777fb7c/OMLX.md
-
curl -L -o OMLX.md https://huggingface.co/TensorFold/Solar-Open2-250B-MLX-6bit/resolve/7ee0a13f1eef4ddfdc54b4e355f13e103777fb7c/OMLX.md
Using This Quant With OMLX
Some OMLX builds may not yet include native Solar Open2 support in their bundled
mlx_lm runtime. This repo includes a small compatibility patch that installs:
solar_open2.py, the MLX loader for the Solar Open2 architecture.solar_open2_tool_parser.py, a parser for Solar Open2 tool-call markup.- Tokenizer runtime detection for Solar Open2 thinking and tool-call markers.
- Four-state recurrent-cache restoration for reliable multi-turn tool loops.
Install
Clone this model repo locally, then run the installer from the repo root:
sudo zsh omlx/install_omlx_solar_open2_patch.sh
The installer patches the OMLX app bundle and creates timestamped backups of any files it changes. If your OMLX installation uses a non-default location, set:
export OMLX_MLX_LM_DIR="/path/to/mlx_lm"
export OMLX_PYTHON="/path/to/python"
export OMLX_RESOURCES="/path/to/oMLX.app/Contents/Resources"
sudo -E zsh omlx/install_omlx_solar_open2_patch.sh
Reload OMLX after installing the patch.
Tool Calling
Solar Open2 emits tool calls using this marker format:
<|tool_call:start|>tool_name
<|tool_arg:start|>argument_name<|tool_arg:value|>argument_value<|tool_arg:end|>
<|tool_call:end|>
The included parser converts that format into OpenAI-compatible tool-call arguments where the serving runtime supports tool parsing.
Thinking Toggle
This quant defaults to clean direct responses. To enable or disable thinking in clients that pass chat-template arguments:
{"enable_thinking": false}
{"enable_thinking": true}
Advanced clients can also pass Solar's native option directly:
{"reasoning_effort": "none"}
{"reasoning_effort": "high"}
If the serving runtime does not hide reasoning channels, enabled thinking may
show <|think:start|> and <|think:end|> markers in generated text.
Notes
OMLX updates can replace bundled runtime files. Re-run the installer after an OMLX update if the model stops loading or tool-call parsing disappears.