Text Generation
MLX
Safetensors
multilingual
phi3
nlp
code
quantization
bias-evaluation
q3
conversational
custom_code
3-bit
Instructions to use plawanrath/phi-3.5-mini-instruct-q3-mlx-cba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use plawanrath/phi-3.5-mini-instruct-q3-mlx-cba 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("plawanrath/phi-3.5-mini-instruct-q3-mlx-cba") 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
- MLX LM
How to use plawanrath/phi-3.5-mini-instruct-q3-mlx-cba with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "plawanrath/phi-3.5-mini-instruct-q3-mlx-cba"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "plawanrath/phi-3.5-mini-instruct-q3-mlx-cba" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "plawanrath/phi-3.5-mini-instruct-q3-mlx-cba", "messages": [ {"role": "user", "content": "Hello"} ] }'
upload tokenizer_config.json
Browse files- tokenizer_config.json +15 -0
tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|endoftext|>",
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"is_local": true,
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"legacy": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"padding_side": "left",
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"sp_model_kwargs": {},
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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