Instructions to use virtual-human-chc/prot_t5_xl_bfd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use virtual-human-chc/prot_t5_xl_bfd with Transformers:

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

pipe = pipeline("text-generation", model="virtual-human-chc/prot_t5_xl_bfd")

# Load model directly
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("virtual-human-chc/prot_t5_xl_bfd")
model = AutoModelWithLMHead.from_pretrained("virtual-human-chc/prot_t5_xl_bfd", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use virtual-human-chc/prot_t5_xl_bfd with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "virtual-human-chc/prot_t5_xl_bfd"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "virtual-human-chc/prot_t5_xl_bfd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/virtual-human-chc/prot_t5_xl_bfd

SGLang

How to use virtual-human-chc/prot_t5_xl_bfd 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 "virtual-human-chc/prot_t5_xl_bfd" \
    --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": "virtual-human-chc/prot_t5_xl_bfd",
		"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 "virtual-human-chc/prot_t5_xl_bfd" \
        --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": "virtual-human-chc/prot_t5_xl_bfd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use virtual-human-chc/prot_t5_xl_bfd with Docker Model Runner:
```
docker model run hf.co/virtual-human-chc/prot_t5_xl_bfd
```

pavm595 commited on Oct 21, 2025

Commit

e73da79

verified ·

1 Parent(s): 129e2f7

Upload 3 files

Browse files

Files changed (3) hide show

pytorch_model.bin +3 -0
spiece.model +3 -0
tf_model.h5 +3 -0

pytorch_model.bin ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:d54a8cb4855ed47cfbeb101a3bc1d93fa9715cb9b116b8da05b5af84bc7780c7
+size 11275562268

spiece.model ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:74da7b4afcde53faa570114b530c726135bdfcdb813dec3abfb27f9d44db7324
+size 237990

tf_model.h5 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:d34490d084bc766a1d5d28d4059a9fe5f543010db3cf36a6925760bbbbb123da
+size 11276359672