alinashrestha/qlora-mistral-y02-v2
011
qloramistraly02_V2 — Y02 Green Patent Classifier
This is a QLoRA fine-tuned adapter for mistralai/Mistral-7B-Instruct-v0.2, trained to classify patent claims as GREEN (Y02) or NOT GREEN.
It was developed as the Judge agent's brain in a 3-agent MAS pipeline for Y02 green patent classification (M4 Final Assignment — AAU).
Model Details
Training Data
- Source:
patents_50k_green.parquet— 50,000 patent claims with Y02 silver labels derived from CPC codes - Train split: 28,500 rows (
train_silver, 95%) - Eval split: 1,500 rows (5% held-out, stratified)
- Label balance: 50% GREEN / 50% NOT GREEN
- Prompt format: Mistral
[INST]...[/INST]chat template - Target output: Strict JSON —
{"is_green": 0/1, "rationale": "one sentence"}
Training History
V2 used Mistral [INST] template and trained to completion, resuming from checkpoint-800 after an SSH disconnection at step 725/891.
Usage
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
BASE_MODEL = "mistralai/Mistral-7B-Instruct-v0.2"
ADAPTER_DIR = "qlora_mistral_y02_V2"
bnb = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base = AutoModelForCausalLM.from_pretrained(
BASE_MODEL, quantization_config=bnb, device_map="auto"
)
model = PeftModel.from_pretrained(base, ADAPTER_DIR)
model.eval()
claim = "A photovoltaic solar panel system for residential energy generation."
prompt = (
"You are an expert patent examiner for Y02 green technology. "
"Classify the following patent claim as GREEN (1) or NOT GREEN (0).\n\n"
'Return STRICT JSON only: {"is_green": 0 or 1, "rationale": "one sentence"}\n\n'
f"Patent claim:\n{claim}"
)
messages = [{"role": "user", "content": prompt}]
formatted = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
with torch.no_grad():
out = model.generate(
**inputs,
max_new_tokens=150,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:],
skip_special_tokens=True)
print(response)
# {"is_green": 1, "rationale": "Photovoltaic system directly generates
# renewable electricity, qualifying under Y02E 10/50."}Role in MAS Pipeline
This adapter was used as the Judge agent in a 3-agent pipeline:
Advocate (this model) → argues FOR green classification
Skeptic (this model) → argues AGAINST green classification
Judge (this model) → weighs both sides → final JSON verdictAll 3 agents share this same model — differentiated only by role-specific prompts. HITL was triggered when confidence < 0.65 or deadlock=True.