Phi-3
Collection
14 items • Updated • 6
How to use mlx-community/Phi-3-small-8k-instruct-AQ4_32 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/Phi-3-small-8k-instruct-AQ4_32")
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)How to use mlx-community/Phi-3-small-8k-instruct-AQ4_32 with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Phi-3-small-8k-instruct-AQ4_32"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Phi-3-small-8k-instruct-AQ4_32"
# 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/Phi-3-small-8k-instruct-AQ4_32",
"messages": [
{"role": "user", "content": "Hello"}
]
}'The Model mlx-community/Phi-3-small-8k-instruct-AQ4_32 was converted to MLX format from microsoft/Phi-3-small-8k-instruct using mlx-lm version 0.14.1.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Phi-3-small-8k-instruct-AQ4_32")
response = generate(model, tokenizer, prompt="hello", verbose=True)
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