Instructions to use UKPLab/dara-llama-2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UKPLab/dara-llama-2-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UKPLab/dara-llama-2-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UKPLab/dara-llama-2-13b") model = AutoModelForCausalLM.from_pretrained("UKPLab/dara-llama-2-13b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UKPLab/dara-llama-2-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UKPLab/dara-llama-2-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UKPLab/dara-llama-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UKPLab/dara-llama-2-13b
- SGLang
How to use UKPLab/dara-llama-2-13b 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 "UKPLab/dara-llama-2-13b" \ --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-2-13b", "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 "UKPLab/dara-llama-2-13b" \ --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-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UKPLab/dara-llama-2-13b with Docker Model Runner:
docker model run hf.co/UKPLab/dara-llama-2-13b
| library_name: transformers | |
| license: apache-2.0 | |
| datasets: | |
| - UKPLab/dara | |
| # 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-2-13B on our curated reasoning trajectory https://huggingface.co/datasets/UKPLab/dara. | |
| Model Usage | |
| from transformers import AutoModelForCausalLM | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "UKPLab/dara-llama-2-13b", | |
| 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 |