How to use from
SGLangUse 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 "UKPLab/dara-llama-2-7b" \
--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": "UKPLab/dara-llama-2-7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs
Model Information
This model is a fine-tuned semantic parsing LLM agent for KGQA. We fine-tune the llama-2-7B on our curated reasoning trajectory https://huggingface.co/datasets/UKPLab/dara.
Model Usage
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"UKPLab/dara-llama-2-7b",
torch_dtype=torch.float16,
device_map="auto",
cache_dir = "cache"
)
For more information, please check the repository https://github.com/UKPLab/acl2024-DARA
Hyperparameters
- Learning rate: 2e-5
- Batch size: 4
- Training epochs: 10
- Downloads last month
- 10
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "UKPLab/dara-llama-2-7b" \ --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": "UKPLab/dara-llama-2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'