Text Generation
MLX
Safetensors
afmoe
omlx
oq
oq8
quantized
conversational
custom_code
8-bit precision
Instructions to use bearzi/Trinity-Mini-oQ8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bearzi/Trinity-Mini-oQ8 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("bearzi/Trinity-Mini-oQ8") 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 bearzi/Trinity-Mini-oQ8 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bearzi/Trinity-Mini-oQ8"
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": "bearzi/Trinity-Mini-oQ8" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bearzi/Trinity-Mini-oQ8 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 "bearzi/Trinity-Mini-oQ8"
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 bearzi/Trinity-Mini-oQ8
Run Hermes
hermes
- MLX LM
How to use bearzi/Trinity-Mini-oQ8 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "bearzi/Trinity-Mini-oQ8"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "bearzi/Trinity-Mini-oQ8" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bearzi/Trinity-Mini-oQ8", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "architectures": [ | |
| "AfmoeForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_afmoe.AfmoeConfig", | |
| "AutoModel": "modeling_afmoe.AfmoeModel", | |
| "AutoModelForCausalLM": "modeling_afmoe.AfmoeForCausalLM" | |
| }, | |
| "dtype": "bfloat16", | |
| "global_attn_every_n_layers": 4, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6144, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "load_balance_coeff": 0.001, | |
| "max_position_embeddings": 131072, | |
| "model_type": "afmoe", | |
| "moe_intermediate_size": 1024, | |
| "mup_enabled": true, | |
| "n_group": 1, | |
| "num_attention_heads": 32, | |
| "num_dense_layers": 2, | |
| "num_expert_groups": 1, | |
| "num_experts": 128, | |
| "num_experts_per_tok": 8, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 4, | |
| "num_limited_groups": 1, | |
| "num_shared_experts": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000, | |
| "route_norm": true, | |
| "route_scale": 2.826, | |
| "score_func": "sigmoid", | |
| "sliding_window": 2048, | |
| "tie_word_embeddings": false, | |
| "topk_group": 1, | |
| "transformers_version": "4.57.3", | |
| "use_cache": true, | |
| "use_grouped_mm": true, | |
| "vocab_size": 200192, | |
| "quantization": { | |
| "group_size": 64, | |
| "bits": 8, | |
| "mode": "affine" | |
| }, | |
| "quantization_config": { | |
| "group_size": 64, | |
| "bits": 8, | |
| "mode": "affine" | |
| } | |
| } |