Instructions to use ArthurZ/mamba-130m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArthurZ/mamba-130m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ArthurZ/mamba-130m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ArthurZ/mamba-130m") model = AutoModelForCausalLM.from_pretrained("ArthurZ/mamba-130m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ArthurZ/mamba-130m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ArthurZ/mamba-130m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArthurZ/mamba-130m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ArthurZ/mamba-130m
- SGLang
How to use ArthurZ/mamba-130m 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 "ArthurZ/mamba-130m" \ --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": "ArthurZ/mamba-130m", "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 "ArthurZ/mamba-130m" \ --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": "ArthurZ/mamba-130m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ArthurZ/mamba-130m with Docker Model Runner:
docker model run hf.co/ArthurZ/mamba-130m
|
Download README.md from ArthurZ/mamba-130m: direct link, hf CLI and curl.
- Browser
- Download file 827 Bytes
-
https://huggingface.co/ArthurZ/mamba-130m/resolve/main/README.md
- Command line
-
hf download hf://ArthurZ/mamba-130m/README.md
-
curl -L -o README.md https://huggingface.co/ArthurZ/mamba-130m/resolve/main/README.md
827 Bytes
metadata
library_name: transformers
tags: []
>>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b", padding_side = "left")
>>> tokenizer.pad_token = tokenizer.eos_token
>>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m", vocab_size=50280, num_hidden_layers=24, torch_dtype=torch.float32)
>>> model.config.use_cache = True
>>> input_ids = tokenizer(["Hey how are you doing?", "Explain how soy sauce is made"], padding=True, return_tensors= "pt")["input_ids"]
>>> out = model.generate(input_ids, max_new_tokens=10)
>>> print(tokenizer.batch_decode(out))
["<|endoftext|>Hey how are you doing?\n\nI'm a newbie to the game", 'Explain how soy sauce is made.\n\n1. Add the soy sauce to']