metadata
library_name: transformers
tags:
- agriculture
- question-answering
- LoRA
- tinyllama
- fine-tuned
- causal-lm
license: apache-2.0
🌾 AgriQA-TinyLlama-LoRA (Adapter)
A LoRA fine-tuned TinyLlama model for answering agriculture-related questions in a conversational format. This adapter is fine-tuned on the AgriQA dataset using parameter-efficient fine-tuning (PEFT) and is suitable for low-resource inference scenarios.
🧠 Model Details
- Base Model: TinyLlama/TinyLlama-1.1B-Chat
- LoRA Adapter Size: ~2MB
- Dataset: shchoi83/agriQA
- Task: Question Answering (Instruction Tuning)
- Language: English
- Adapter Only: This repository only contains the LoRA adapter. You must load it on top of the base model.
- Trained by: @theone049
🚀 Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat")
tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "theone049/agriqa-tinyllama-lora-adapter")
# Inference
prompt = """### Instruction:
Answer the agricultural question.
### Input:
What is the control measure for aphid infestation in mustard crops?
### Response:
"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))