Instructions to use togethercomputer/evo-1-131k-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/evo-1-131k-base with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/evo-1-131k-base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("togethercomputer/evo-1-131k-base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use togethercomputer/evo-1-131k-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/evo-1-131k-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-1-131k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/evo-1-131k-base

SGLang

How to use togethercomputer/evo-1-131k-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-1-131k-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-1-131k-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-1-131k-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-1-131k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/evo-1-131k-base with Docker Model Runner:
```
docker model run hf.co/togethercomputer/evo-1-131k-base
```

issue loading the model

by adrienchaton - opened Apr 24, 2024

Discussion

adrienchaton

Apr 24, 2024

Hi and thanks for sharing your model!

I have successfully installed Evo's dependencies from the github source.
All seems importing fine (e.g. from evo import Evo) but once I try to load the model, I get this error

model = AutoModelForCausalLM.from_pretrained("togethercomputer/evo-1-131k-base", trust_remote_code=True)
--> ModuleNotFoundError: No module named 'transformers_modules.togethercomputer.evo-1-131k-base.9562f3fdc38f09b92594864c5e98264f1bfbca33.tokenizer'

It points to the last commit hash from the evo-1-131k-base model repository.

I have been trying different versions of transformers, also using the different instructions (e.g. using evo package instead of transformers), caching the checkpoint files locally, ... but still the same error when I try to load the model.

Do you have any idea on what to do to fix it please?

Zymrael

Apr 28, 2024

This is strange, what version of transformers are you using?

adrienchaton

May 2, 2024

Thanks for your reply, let's follow-up here? https://github.com/evo-design/evo/issues/53

I tried both transformers==4.36.2 and transformers==4.39.3 which resulted in the same error (full trace posted on the github issue)

monsoon-nlp

Jun 11, 2024

This looks familiar to an error which I got, and it helped to load the tokenizer with trust_remote_code=True first before loading the model.

JiayiJennie

Jul 28, 2024

same issue here

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