Llama3.2-3B-Explained-GGUF/README.md

base_model, tags, language, library_name

base_model	tags	language	library_name
theprint/Llama3.2-3B-Explained	fine-tuned lora sft auto-sft	en	transformers

Llama3.2-3B-Explained (GGUF)

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

GGUF Files

These quantized GGUF files can be used directly with llama.cpp, Ollama, LM Studio, and other compatible runtimes.

File	Description
Llama3.2-3B-Explained-BF16.gguf	BF16
Llama3.2-3B-Explained-Q8_0.gguf	8-bit — near-lossless, larger file
Llama3.2-3B-Explained-Q6_K.gguf	6-bit — high quality
Llama3.2-3B-Explained-Q5_K_M.gguf	5-bit medium — good quality/size balance
Llama3.2-3B-Explained-Q5_K_S.gguf	Q5_K_S
Llama3.2-3B-Explained-Q4_K_M.gguf	4-bit medium — recommended for most use cases
Llama3.2-3B-Explained-Q4_K_S.gguf	Q4_K_S
Llama3.2-3B-Explained-Q3_K_L.gguf	Q3_K_L
Llama3.2-3B-Explained-Q3_K_M.gguf	Q3_K_M
Llama3.2-3B-Explained-Q3_K_S.gguf	Q3_K_S
Llama3.2-3B-Explained-Q2_K.gguf	2-bit — smallest size, lowest quality
Llama3.2-3B-Explained-IQ4_NL.gguf	IQ4_NL

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Generated by Auto-SFT