Instructions to use togethercomputer/StripedHyena-Nous-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/StripedHyena-Nous-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="togethercomputer/StripedHyena-Nous-7B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("togethercomputer/StripedHyena-Nous-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use togethercomputer/StripedHyena-Nous-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "togethercomputer/StripedHyena-Nous-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-Nous-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/togethercomputer/StripedHyena-Nous-7B
- SGLang
How to use togethercomputer/StripedHyena-Nous-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-Nous-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-Nous-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-Nous-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-Nous-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use togethercomputer/StripedHyena-Nous-7B with Docker Model Runner:
docker model run hf.co/togethercomputer/StripedHyena-Nous-7B
| # Copyright (c) Together | |
| # This software is distributed under the terms of the Apache License, Version 2.0 | |
| # Author: Michael Poli | |
| from torch import Tensor | |
| from dataclasses import dataclass, field | |
| from typing import Optional | |
| # https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py | |
| class InferenceParams: | |
| """Inference parameters that are passed to the main model in order | |
| to efficienly calculate and store the context during inference.""" | |
| max_seqlen: int | |
| max_batch_size: int | |
| seqlen_offset: int = 0 | |
| batch_size_offset: int = 0 | |
| key_value_memory_dict: dict = field(default_factory=dict) | |
| lengths_per_sample: Optional[Tensor] = None | |
| def reset(self, max_seqlen, max_batch_size): | |
| self.max_seqlen = max_seqlen | |
| self.max_batch_size = max_batch_size | |
| self.seqlen_offset = 0 | |
| if self.lengths_per_sample is not None: | |
| self.lengths_per_sample.zero_() | |
| class RecurrentInferenceParams: | |
| """Inference parameters passed to blocks with recurrent mode.""" | |
| fir_filter_length: int = 3 | |
| state_dim: int = 16 | |
| seqlen_offset: int = 0 | |
| fir_state_dict: dict = field(default_factory=dict) | |
| state_dict: dict = field(default_factory=dict) | |
| def reset(self): | |
| self.fir_filter_length = 3 | |
| self.state_dim = 16 | |
| self.seqlen_offset = 0 | |