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
- 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
Error load Model
#1
by NickyNicky - opened
%%time
!pip install git+https://github.com/huggingface/transformers -qqq
# trl
!pip install git+https://github.com/huggingface/trl -qqq
!pip install datasets peft accelerate safetensors --upgrade -qqq
!pip install ninja packaging --upgrade -qqq
!pip install sentencepiece bitsandbytes -qqq
!pip install -U xformers deepspeed -qqq
!pip install attention_sinks -qqq
!python -c "import torch; assert torch.cuda.get_device_capability()[0] >= 8, 'Hardware not supported for Flash Attention'"
!export CUDA_HOME=/usr/local/cuda-11.8
!MAX_JOBS=4 pip install flash-attn --no-build-isolation -qqq
# !pip install git+"https://github.com/Dao-AILab/flash-attention.git"
load model
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
GenerationConfig,
TextIteratorStreamer,
)
# from attention_sinks import AutoModelForCausalLM
import torch
model_ID_1="togethercomputer/StripedHyena-Nous-7B"
model = AutoModelForCausalLM.from_pretrained(model_ID_1,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
use_flash_attention_2=True, # True , False
low_cpu_mem_usage= True,
)
max_length=2048 #get_max_length()
print("max_length",max_length)
tokenizer = AutoTokenizer.from_pretrained(model_ID_1,
# use_fast = False, # True False
max_length=max_length,)
Error:
Can you share your environment? I repeated your steps and loaded the model correctly without any issues
Zymrael changed discussion status to closed

