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"} ] }'
File size: 6,146 Bytes
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"0": {
"content": "<|begin_of_text|>",
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"rstrip": false,
"single_word": false,
"special": true
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"content": "<|end_of_text|>",
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"2": {
"content": "<|im_start|>",
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"3": {
"content": "<|im_end|>",
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},
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"eos_token": "<|im_end|>",
"extra_special_tokens": {},
"model_max_length": 65536,
"pad_token": "<|pad|>",
"tokenizer_class": "PreTrainedTokenizerFast",
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}
|