ScalingIntelligence/KernelBench
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How to use alpindale/KernelLLM-MLX-Q8 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("alpindale/KernelLLM-MLX-Q8")
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 alpindale/KernelLLM-MLX-Q8 with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "alpindale/KernelLLM-MLX-Q8"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "alpindale/KernelLLM-MLX-Q8"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "alpindale/KernelLLM-MLX-Q8",
"messages": [
{"role": "user", "content": "Hello"}
]
}'8-bit
Base model
meta-llama/Llama-3.1-8B
# 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("alpindale/KernelLLM-MLX-Q8") 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)