Instructions to use NeuralVulture/llama32-1b-qa-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NeuralVulture/llama32-1b-qa-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "NeuralVulture/llama32-1b-qa-lora") - Notebooks
- Google Colab
- Kaggle
LoRA adapter (PEFT) โ Llama 3.2 1B QA tutor
Repository: NeuralVulture/llama32-1b-qa-lora
Base model: unsloth/Llama-3.2-1B-Instruct
Uploaded: 2026-03-26 (UTC)
Contents
This repo contains a LoRA adapter only (low-rank weights + adapter_config.json).
You must load it on top of the base model with PEFT / Transformers.
Merged full weights (single from_pretrained, no PEFT):https://huggingface.co/NeuralVulture/llama32-1b-qa-merged
Load (PEFT)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base_id = "unsloth/Llama-3.2-1B-Instruct"
adapter_id = "NeuralVulture/llama32-1b-qa-lora"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base = AutoModelForCausalLM.from_pretrained(
base_id,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, adapter_id)
Training
Adapter exported from local LoRA SFT (TRL + PEFT). Local source directory (reference): /workspace/outputs/lora-llama-3.2-1b-qa.
License
The base model license applies to the base weights. This adapter is a derivative; use only in compliance with the base model license and your jurisdiction.
- Downloads last month
- 2
Model tree for NeuralVulture/llama32-1b-qa-lora
Base model
meta-llama/Llama-3.2-1B-Instruct Finetuned
unsloth/Llama-3.2-1B-Instruct