Text Generation
MLX
Safetensors
Polish
English
llama
typed-decisions
decision-making
classification
calibration
polish
bf16
conversational
Instructions to use agentGreg/basal-1.5-mini-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use agentGreg/basal-1.5-mini-MLX-bf16 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("agentGreg/basal-1.5-mini-MLX-bf16") 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 Settings
- LM Studio
- MLX LM
How to use agentGreg/basal-1.5-mini-MLX-bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "agentGreg/basal-1.5-mini-MLX-bf16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "agentGreg/basal-1.5-mini-MLX-bf16" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "agentGreg/basal-1.5-mini-MLX-bf16", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
Commit History
-
README: rope_theta note credits basal's HARDWARE.md, basal-serve corrects it
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README: note on rope_theta when converting basal-1.5 to MLX
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README.md: results of the full comparison (e03 + e04, 404 decisions)
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basal-1.5-mini-MLX-bf16: MLX conversion of Remek/basal-1.5-mini
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initial commit
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