Llama3.2-3B-Explained

A fine-tuned version of meta-llama/Llama-3.2-3B-Instruct trained on Explained 0.41k alpaca data using Auto-SFT — an automated hyperparameter search and supervised fine-tuning pipeline.

The base model was adapted to follow the style and content of the Explained 0.41k alpaca dataset. Expect improved performance on tasks similar to those represented in the training data.

Model Details

Property	Value
Base model	meta-llama/Llama-3.2-3B-Instruct
Training data	data/Explained-0.41k-alpaca.json
Fine-tuning epochs	2
Fine-tuning date	2026-03-25
Fine-tuning method	LoRA (merged to full 16-bit)

Training Hyperparameters

LoRA

Parameter	Value
r	4
alpha	8
dropout	0.0
target_modules	['q_proj', 'v_proj', 'k_proj', 'o_proj']

Training

Parameter	Value
learning_rate	1e-05
batch_size	1
gradient_accumulation_steps	2
warmup_ratio	0.0
max_seq_length	512

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model     = AutoModelForCausalLM.from_pretrained("theprint/Llama3.2-3B-Explained")
tokenizer = AutoTokenizer.from_pretrained("theprint/Llama3.2-3B-Explained")

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Generated by Auto-SFT