AgriSmart-TinyLlama-LoRA

A LoRA adapter for TinyLlama-1.1B-Chat, fine-tuned on agricultural Q&A data to serve as a specialized farming assistant.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

# Load base model with 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

base_model = AutoModelForCausalLM.from_pretrained(
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    quantization_config=bnb_config,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("kellenmurerwa/AgriSmart-TinyLlama-LoRA")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "kellenmurerwa/AgriSmart-TinyLlama-LoRA")

# Ask a question
question = "What is the best fertilizer for rice crops?"
prompt = f"### Instruction:
{question}

### Response:
"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response.split("### Response:
")[-1].strip())

Training Details

Parameter	Value
Base Model	TinyLlama-1.1B-Chat-v1.0
Dataset	KisanVaani agriculture-qa-english-only (22,615 samples)
LoRA Rank	16
LoRA Alpha	32
Target Modules	q_proj, v_proj
Trainable Parameters	~2.25M (0.2% of total)
Learning Rate	5e-5
Epochs	2
Quantization	4-bit NF4

Evaluation

Metric	Score
BLEU	0.1810
ROUGE-1	0.5129
ROUGE-2	0.3268
ROUGE-L	0.4826
Perplexity	2.2583

Links

• GitHub: AgriSmart-Assistant
• Colab Notebook: