Instructions to use han1997/mamba-2.8b-zephyr-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use han1997/mamba-2.8b-zephyr-hf with Transformers:

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

pipe = pipeline("text-generation", model="han1997/mamba-2.8b-zephyr-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("han1997/mamba-2.8b-zephyr-hf")
model = AutoModelForMultimodalLM.from_pretrained("han1997/mamba-2.8b-zephyr-hf")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use han1997/mamba-2.8b-zephyr-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "han1997/mamba-2.8b-zephyr-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "han1997/mamba-2.8b-zephyr-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/han1997/mamba-2.8b-zephyr-hf

SGLang

How to use han1997/mamba-2.8b-zephyr-hf 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 "han1997/mamba-2.8b-zephyr-hf" \
    --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": "han1997/mamba-2.8b-zephyr-hf",
		"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 "han1997/mamba-2.8b-zephyr-hf" \
        --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": "han1997/mamba-2.8b-zephyr-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use han1997/mamba-2.8b-zephyr-hf with Docker Model Runner:
```
docker model run hf.co/han1997/mamba-2.8b-zephyr-hf
```

mamba-2.8b-zephyr-hf / model-00003-of-00003.safetensors

han1997

Upload model-00003-of-00003.safetensors

c92360f verified about 2 years ago

Download

History Blame Contribute Delete

1.15 GB

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:: 21ad9d84ed603474d5c65279f5080b56b07dfcd1f64490a8e26ff61180a8baf8
Size of remote file:: 1.15 GB
SHA256:: d3fd575cd5a239d889cd3d4e5e610732e92eac0e7f3687ce8c9955e3a2a13599

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