--- language: - en tags: - agriculture - question-answering - fine-tuning - lora - domain-specific license: apache-2.0 datasets: - agriqa model-index: - name: TinyLlama-LoRA-AgriQA results: - task: type: question-answering name: Question Answering dataset: name: AgriQA type: agriqa metrics: - type: accuracy value: 0.78 name: Accuracy --- # 🧠 AgriQA TinyLlama LoRA Adapter This repository contains a [LoRA](https://arxiv.org/abs/2106.09685) adapter fine-tuned on the [AgriQA](https://huggingface.co/datasets/shchoi83/agriQA) dataset using the [TinyLlama/TinyLlama-1.1B-Chat](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat) base model. --- ## 🔧 Model Details - **Base Model**: [`TinyLlama/TinyLlama-1.1B-Chat`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat) - **Adapter Type**: LoRA (Low-Rank Adaptation) - **Adapter Size**: ~4.5MB - **Dataset**: [`shchoi83/agriQA`](https://huggingface.co/datasets/shchoi83/agriQA) - **Language**: English - **Task**: Instruction-tuned Question Answering in Agriculture domain - **Trained by**: [@theone049](https://huggingface.co/theone049) --- ## 📌 Usage To use this adapter, load it on top of the base model: ```python from transformers import AutoTokenizer, AutoModelForCausalLM from peft import PeftModel, PeftConfig # Load base model base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat") tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat") # Load adapter model = PeftModel.from_pretrained(base_model, "theone049/agriqa-tinyllama-lora-adapter") # Run inference prompt = """### Instruction: Answer the agricultural question. ### Input: What is the ideal pH range for growing rice? ### Response:""" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=100) print(tokenizer.decode(outputs[0], skip_special_tokens=True))