README.md

metadata

base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
tags:
  - base_model:adapter:meta-llama/Llama-3.2-1B-Instruct
  - lora
  - transformers
license: apache-2.0
datasets:
  - nyu-mll/glue
language:
  - en
metrics:
  - accuracy
  - f1
  - matthews_correlation
pipeline_tag: text-classification

MNLI - LLaMA 3.2 1B - QLoRA (10k subset, 4-bit)

Model Summary

This is a QLoRA fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the MNLI (Multi-Genre Natural Language Inference) dataset from GLUE.

• Base model: LLaMA 3.2 1B Instruct
• Fine-tuning method: QLoRA with 4-bit quantization
• Train subset: 10k samples (8k train / 1k val / 1k test from train split)
• Evaluation: Official GLUE dev sets (matched / mismatched) + held-out 1k test split
• Trainable parameters: 5.64M (0.45% of base model)
• Hardware: NVIDIA T4 (fp16)

⚠️ Note: This repo contains only the LoRA adapter weights. You need access to the base model from Meta to use it.

---

Usage

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

BASE = "meta-llama/Llama-3.2-1B-Instruct"
ADAPTER = "streetelite/mnli-llama3.2-1b-qlora-10k"

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)

tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
model = AutoModelForSequenceClassification.from_pretrained(
    BASE,
    num_labels=3,
    quantization_config=bnb,
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(model, ADAPTER).eval()

inputs = tokenizer(
    "A man is playing guitar.",
    "A person is making music.",
    return_tensors="pt",
    truncation=True
)
with torch.inference_mode():
    logits = model(**{k: v.to(model.device) for k, v in inputs.items()}).logits
    probs = logits.softmax(-1)
print(probs)

Results

GLUE dev (official)

Set	Accuracy	F1 (macro)	F1 (weighted)	MCC	Kappa	MAE
Matched	82.37%	0.8210	0.8224	0.7358	0.7349	0.2068
Mismatched	83.71%	0.8348	0.8360	0.7558	0.7550	0.1894

---

Held-out test split (1k from train)

Accuracy	F1 (macro)	F1 (weighted)	MCC	Kappa	MAE
83.10%	0.8280	0.8288	0.7496	0.7461	0.2010

---

Training Details

• Framework: Hugging Face Transformers + PEFT + bitsandbytes
• Quantization: 4-bit NF4 w/ double quantization
• LoRA config: r=8, alpha=16, dropout=0.1, target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Optimizer: paged_adamw_8bit, lr=2e-4
• Batch size: 4 (gradient accumulation = 4 → effective 16)
• Epochs: 2
• Seed: 42
• Padding: dynamic

---

Intended Uses

• Primary: Natural language inference on text pairs (entailment, neutral, contradiction).
• Languages: English.
• Not intended for: non-English inputs, factual question answering, safety-critical applications without human review.

---

License

• Base model: LLaMA 3.2 1B Instruct — Meta license
• Adapter: Apache License 2.0