Text Generation
MLX
Safetensors
afmoe
reasoning
agentic
tool-calling
thinking
conversational
custom_code
5-bit
Instructions to use mlx-community/Trinity-Large-Thinking-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Trinity-Large-Thinking-5bit 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("mlx-community/Trinity-Large-Thinking-5bit") 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 new
How to use mlx-community/Trinity-Large-Thinking-5bit with Pi:
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-5bit"
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": "mlx-community/Trinity-Large-Thinking-5bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/Trinity-Large-Thinking-5bit 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 "mlx-community/Trinity-Large-Thinking-5bit"
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 mlx-community/Trinity-Large-Thinking-5bit
Run Hermes
hermes
- MLX LM
How to use mlx-community/Trinity-Large-Thinking-5bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Trinity-Large-Thinking-5bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Trinity-Large-Thinking-5bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Trinity-Large-Thinking-5bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
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| ], | |
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| "auto_map": { | |
| "AutoConfig": "configuration_afmoe.AfmoeConfig", | |
| "AutoModel": "modeling_afmoe.AfmoeModel", | |
| "AutoModelForCausalLM": "modeling_afmoe.AfmoeForCausalLM" | |
| }, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 3, | |
| "global_attn_every_n_layers": 4, | |
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| "num_key_value_heads": 8, | |
| "num_limited_groups": 1, | |
| "num_shared_experts": 1, | |
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| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
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| "route_norm": true, | |
| "route_scale": 2.448, | |
| "score_func": "sigmoid", | |
| "sliding_window": 4096, | |
| "tie_word_embeddings": false, | |
| "topk_group": 1, | |
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| "use_cache": true, | |
| "use_grouped_mm": true, | |
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