Gated Linear Attention Transformers with Hardware-Efficient Training
Paper • 2312.06635 • Published • 9
How to use bailin28/gla-1B-100B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="bailin28/gla-1B-100B") # Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bailin28/gla-1B-100B", dtype="auto")How to use bailin28/gla-1B-100B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bailin28/gla-1B-100B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "bailin28/gla-1B-100B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/bailin28/gla-1B-100B
How to use bailin28/gla-1B-100B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "bailin28/gla-1B-100B" \
--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": "bailin28/gla-1B-100B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "bailin28/gla-1B-100B" \
--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": "bailin28/gla-1B-100B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use bailin28/gla-1B-100B with Docker Model Runner:
docker model run hf.co/bailin28/gla-1B-100B
This checkpoint of the 1.3B GLA model used in the paper Gated Linear Attention. The model is trained with 100B tokens from the SlimPajama dataset tokenized with Llama2 tokenizer.
See the model and loading script in this repo.