Text Generation
MLX
Safetensors
English
Chinese
mimo_v2
agent
long-context
code
conversational
custom_code
6-bit
How to use from
MLX LMRun an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm# Start the server
mlx_lm.server --model "kernelpool/MiMo-V2.5-Pro-6bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "kernelpool/MiMo-V2.5-Pro-6bit",
"messages": [
{"role": "user", "content": "Hello"}
]
}'Quick Links
kernelpool/MiMo-V2.5-Pro-6bit
This model kernelpool/MiMo-V2.5-Pro-6bit was converted to MLX format from XiaomiMiMo/MiMo-V2.5-Pro using mlx-lm version 0.31.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("kernelpool/MiMo-V2.5-Pro-6bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
1T params
Tensor type
BF16
·
U32 ·
F32 ·
Hardware compatibility
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6-bit
Model tree for kernelpool/MiMo-V2.5-Pro-6bit
Base model
XiaomiMiMo/MiMo-V2.5-Pro
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm# Interactive chat REPL mlx_lm.chat --model "kernelpool/MiMo-V2.5-Pro-6bit"