README.md

🧠 AgriQA TinyLlama LoRA Adapter

This repository contains a LoRA adapter fine-tuned on the AgriQA dataset using the TinyLlama/TinyLlama-1.1B-Chat base model.

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🔧 Model Details

• Base Model: TinyLlama/TinyLlama-1.1B-Chat
• Adapter Type: LoRA (Low-Rank Adaptation)
• Adapter Size: ~4.5MB
• Dataset: shchoi83/agriQA
• Language: English
• Task: Instruction-tuned Question Answering in Agriculture domain
• Trained by: @theone049

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📌 Usage

To use this adapter, load it on top of the base model:

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))