Instructions to use gghfez/MiMo-V2.5-control-vectors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gghfez/MiMo-V2.5-control-vectors with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="gghfez/MiMo-V2.5-control-vectors")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gghfez/MiMo-V2.5-control-vectors", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gghfez/MiMo-V2.5-control-vectors with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gghfez/MiMo-V2.5-control-vectors"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gghfez/MiMo-V2.5-control-vectors",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gghfez/MiMo-V2.5-control-vectors

SGLang

How to use gghfez/MiMo-V2.5-control-vectors with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "gghfez/MiMo-V2.5-control-vectors" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gghfez/MiMo-V2.5-control-vectors",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "gghfez/MiMo-V2.5-control-vectors" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gghfez/MiMo-V2.5-control-vectors",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gghfez/MiMo-V2.5-control-vectors with Docker Model Runner:
```
docker model run hf.co/gghfez/MiMo-V2.5-control-vectors
```

MiMo-V2.5-control-vectors / MiMo-V2.5-empathy_vs_sociopathy__debias.gguf

gghfez

Upload folder using huggingface_hub

514b828 verified 19 days ago

Download

History Blame GGUF Editor new Delete

739 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 5f24134c00c0643e973bee6755b650521414715aaa27c405b87d6508ddd49303
Size of remote file:: 739 kB
SHA256:: c8053d3b1ed8c4738ddcc03cdec549130e94791f73433eb77499d24a6e37c860

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.