Instructions to use hugohrban/progen2-small-mix7-bidi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hugohrban/progen2-small-mix7-bidi with Transformers:

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

pipe = pipeline("text-generation", model="hugohrban/progen2-small-mix7-bidi", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("hugohrban/progen2-small-mix7-bidi", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hugohrban/progen2-small-mix7-bidi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hugohrban/progen2-small-mix7-bidi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hugohrban/progen2-small-mix7-bidi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hugohrban/progen2-small-mix7-bidi

SGLang

How to use hugohrban/progen2-small-mix7-bidi 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 "hugohrban/progen2-small-mix7-bidi" \
    --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": "hugohrban/progen2-small-mix7-bidi",
		"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 "hugohrban/progen2-small-mix7-bidi" \
        --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": "hugohrban/progen2-small-mix7-bidi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hugohrban/progen2-small-mix7-bidi with Docker Model Runner:
```
docker model run hf.co/hugohrban/progen2-small-mix7-bidi
```

progen2-small-mix7-bidi / README.md

hugohrban

Update README.md

db72bbf verified about 2 years ago

1.19 kB

library_name: transformers
tags:
  - protein
license: bsd-3-clause

ProGen2-small finetuned on 7 protein families.

Bidirectional model trained on both N -> C and C -> N directions of protein sequences, specified by tokens "1" and "2" respectively.

See my github repo for more information.

Example usage:

from transformers import AutoModelForCausalLM
from tokenizers import Tokenizer
import torch
import torch.nn.functional as F

# load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("hugohrban/progen2-small-mix7-bidi", trust_remote_code=True)
tokenizer = Tokenizer.from_pretrained("hugohrban/progen2-small-mix7-bidi")
tokenizer.no_padding()

# prepare input
prompt = "<|pf00125|>2FDDDVSAVKSTGVSK"
input_ids = torch.tensor(tokenizer.encode(prompt).ids).to(model.device)

# forward pass
logits = model(input_ids).logits

# print output probabilities
next_token_logits = logits[-1, :]
next_token_probs = F.softmax(next_token_logits, dim=-1)
for i in range(tokenizer.get_vocab_size(with_added_tokens=False)):
    print(f"{tokenizer.id_to_token(i)}: {100 * next_token_probs[i].item():.2f} %")