Instructions to use togethercomputer/evo-1-8k-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/evo-1-8k-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="togethercomputer/evo-1-8k-base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("togethercomputer/evo-1-8k-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use togethercomputer/evo-1-8k-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/evo-1-8k-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/evo-1-8k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/evo-1-8k-base

SGLang

How to use togethercomputer/evo-1-8k-base 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/evo-1-8k-base" \
    --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/evo-1-8k-base",
		"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/evo-1-8k-base" \
        --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/evo-1-8k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/evo-1-8k-base with Docker Model Runner:
```
docker model run hf.co/togethercomputer/evo-1-8k-base
```

Zymrael commited on Nov 18, 2024

Commit

a9be7b6

verified ·

1 Parent(s): cb2b3eb

Update README.md

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@@ -89,10 +89,13 @@ To use StripedHyena outside of the playground, you will need to install custom k
 ```
 @article{nguyen2024sequence,
-   author = {Eric Nguyen and Michael Poli and Matthew G. Durrant and Armin W. Thomas and Brian Kang and Jeremy Sullivan and Madelena Y. Ng and Ashley Lewis and Aman Patel and Aaron Lou and Stefano Ermon and Stephen A. Baccus and Tina Hernandez-Boussard and Christopher Ré and Patrick D. Hsu and Brian L. Hie},
-   journal = {Arc Institute manuscripts},
    title = {Sequence modeling and design from molecular to genome scale with Evo},
-   url = {https://arcinstitute.org/manuscripts/Evo},
    year = {2024},
-}
 ```

 ```
 @article{nguyen2024sequence,
+   author = {Eric Nguyen and Michael Poli and Matthew G. Durrant and Brian Kang and Dhruva Katrekar and David B. Li and Liam J. Bartie and Armin W. Thomas and Samuel H. King and Garyk Brixi and Jeremy Sullivan and Madelena Y. Ng and Ashley Lewis and Aaron Lou and Stefano Ermon and Stephen A. Baccus and Tina Hernandez-Boussard and Christopher Ré and Patrick D. Hsu and Brian L. Hie },
    title = {Sequence modeling and design from molecular to genome scale with Evo},
+   journal = {Science},
+   volume = {386},
+   number = {6723},
+   pages = {eado9336},
    year = {2024},
+   doi = {10.1126/science.ado9336},
+   URL = {https://www.science.org/doi/abs/10.1126/science.ado9336},
 ```