Instructions to use togethercomputer/StripedHyena-Hessian-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/StripedHyena-Hessian-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="togethercomputer/StripedHyena-Hessian-7B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("togethercomputer/StripedHyena-Hessian-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use togethercomputer/StripedHyena-Hessian-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "togethercomputer/StripedHyena-Hessian-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "togethercomputer/StripedHyena-Hessian-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/togethercomputer/StripedHyena-Hessian-7B
- SGLang
How to use togethercomputer/StripedHyena-Hessian-7B 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 "togethercomputer/StripedHyena-Hessian-7B" \ --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": "togethercomputer/StripedHyena-Hessian-7B", "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 "togethercomputer/StripedHyena-Hessian-7B" \ --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": "togethercomputer/StripedHyena-Hessian-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use togethercomputer/StripedHyena-Hessian-7B with Docker Model Runner:
docker model run hf.co/togethercomputer/StripedHyena-Hessian-7B
Commit History
fix fast tokenizer for transformers >= "from_slow": false, f340f48 verified
chore: readme update 8399d6d verified
Update README.md 6715e3d verified
chore: update readme 0dd06f7 verified
chore: add checkpoint import b48efba
chore: upload pytorch standalone checkpoint 4313473
Update README.md dce36cc
chore: add info on dtypes 51d76d4
chore: kernel disclaimer f6851b9
chore: model name d2ec434
chore: update logo size 27d2bf6
chore: update readme 00e8992
chore: update readme cabde32
chore: more info in the readme e4713f6
chore: small fix in dec 79167ac
Update tokenizer class in tokenizer_config,json to llama tokenizer. f2b1452
Add model_type to config.json. d2a0c0f
chore: update gradient checkpointing 521ac0e
chore: update config 8711fb6
fix: force correct mixed dtype after HF load 4b7049f
fix: force correct dtype in HF load b18b5f1
chore: remove generate 86e226f
chore: remove flashfft dep cb37c8c
Zymrael commited on
chore: info af9abca
chore: info 6be2da5
chore: sync readme 1e81fed
Zymrael commited on
chore: add more information 70481ea
chore: add some model card info 06bc59c
feat: initial commit 2f9e2c6
Zymrael commited on