UKPLab/DARA-Agentbench
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How to use UKPLab/agentbench-7b with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="UKPLab/agentbench-7b") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("UKPLab/agentbench-7b")
model = AutoModelForCausalLM.from_pretrained("UKPLab/agentbench-7b")How to use UKPLab/agentbench-7b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UKPLab/agentbench-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "UKPLab/agentbench-7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/UKPLab/agentbench-7b
How to use UKPLab/agentbench-7b with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "UKPLab/agentbench-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/agentbench-7b",
"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 "UKPLab/agentbench-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/agentbench-7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use UKPLab/agentbench-7b with Docker Model Runner:
docker model run hf.co/UKPLab/agentbench-7b
This model is a fine-tuned semantic parsing LLM agent for KGQA. We fine-tune the llama-2-7B on our curated reasoning trajectory in the Agentbench format: https://huggingface.co/datasets/UKPLab/dara-agentbench.
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained( "UKPLab/agentbench-7b", torch_dtype=torch.float16, device_map="auto", cache_dir = "cache")
For more information, please check the repository https://github.com/UKPLab/acl2024-DARA