Instructions to use sealad886/gpt-oss-120b-MLX-native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sealad886/gpt-oss-120b-MLX-native 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("sealad886/gpt-oss-120b-MLX-native") 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
How to use sealad886/gpt-oss-120b-MLX-native with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "sealad886/gpt-oss-120b-MLX-native"
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": "sealad886/gpt-oss-120b-MLX-native" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use sealad886/gpt-oss-120b-MLX-native 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 "sealad886/gpt-oss-120b-MLX-native"
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 sealad886/gpt-oss-120b-MLX-native
Run Hermes
hermes
- MLX LM
How to use sealad886/gpt-oss-120b-MLX-native with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "sealad886/gpt-oss-120b-MLX-native"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "sealad886/gpt-oss-120b-MLX-native" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sealad886/gpt-oss-120b-MLX-native", "messages": [ {"role": "user", "content": "Hello"} ] }'
sealad886/gpt-oss-120b-MLX-native
This model sealad886/gpt-oss-120b-MLX-native was converted to MLX format from openai/gpt-oss-120b using mlx-lm version 0.26.3.
Note: command for conversion:
mlx_lm.convert --hf-repo openai/gpt-oss-120b ... -q --q-bits 4The version without doing an added 4-bit quant was in a 16-bit format (either bfloat16 or float16, I assume; I didn't check).
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("sealad886/gpt-oss-120b-MLX-native")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
117B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit
Model tree for sealad886/gpt-oss-120b-MLX-native
Base model
openai/gpt-oss-120b