Instructions to use togethercomputer/StripedHyena-Nous-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Quantization pls?

by Yhyu13 - opened Dec 10, 2023

Discussion

Yhyu13

Dec 10, 2023

@TheBloke @LoneStriker

Is this model ready to be quantized, or not?

Thanks!

LoneStriker

Dec 10, 2023

It's a new architecture, so from the exl2 quant side, Turbo will have to add support for the model before it can be quantized. If it's supported in Transformers, TheBloke may be able to generate GPTQ quants though.

Maykeye

Dec 11, 2023

You can use load_in_8bit or load_in_4bit (in fact that's there only method I got to load in 16GB ram) to quantize on the fly.

Also FFT is memory hungry. In torch 2.1.1 it can eat 2GB of memory for cache (not emptied by empty_cache) if you are not careful enough, in old it just leaked the memory and it doesn't support bf16

Fortunately FlashFFTConv are coming and there's recurrent prefill mechanism(I haven't tried it)

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