Text Generation
MLX
Safetensors
afmoe
reasoning
agentic
tool-calling
thinking
conversational
custom_code
4-bit precision
How to use from
PiConfigure 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": "mlx-community/Trinity-Large-Thinking-4bit"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
piQuick Links
mlx-community/Trinity-Large-Thinking-4bit
This model mlx-community/Trinity-Large-Thinking-4bit was converted to MLX format from arcee-ai/Trinity-Large-Thinking using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Trinity-Large-Thinking-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
399B params
Tensor type
BF16
·
U32 ·
F32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Model tree for mlx-community/Trinity-Large-Thinking-4bit
Base model
arcee-ai/Trinity-Large-TrueBase Finetuned
arcee-ai/Trinity-Large-Base Finetuned
arcee-ai/Trinity-Large-Thinking
Start the MLX server
# Install MLX LM: uv tool install mlx-lm# Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/Trinity-Large-Thinking-4bit"