base_model, tags, language, library_name
| base_model | tags | language | library_name | ||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|
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 |
Generated by Auto-SFT