# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("UKPLab/agentbench-7b")
model = AutoModelForCausalLM.from_pretrained("UKPLab/agentbench-7b")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 in the Agentbench format: https://huggingface.co/datasets/UKPLab/dara-agentbench.
Model Usage
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
Hyperparameters
- Learning rate: 2e-5
- Batch size: 4
- Training epochs: 10
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UKPLab/agentbench-7b")