DoItYourself-v1-2B
A fine-tuned version of unsloth/Qwen3.5-2B trained on DIY Data 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 DIY Data dataset. Expect improved performance on tasks similar to those represented in the training data.
Model Details
| Property | Value |
|---|---|
| Base model | unsloth/Qwen3.5-2B |
| Training data | data/DIY-Data.json |
| Fine-tuning epochs | 2 |
| Fine-tuning date | 2026-10-06 |
| Fine-tuning method | LoRA (merged to full 16-bit) |
Training Hyperparameters
LoRA
| Parameter | Value |
|---|---|
r | 16 |
alpha | 64 |
dropout | 0.1 |
target_modules | ['q_proj', 'v_proj', 'k_proj', 'o_proj'] |
Training
| Parameter | Value |
|---|---|
learning_rate | 0.0001 |
batch_size | 2 |
gradient_accumulation_steps | 1 |
warmup_ratio | 0.1 |
max_seq_length | 512 |
quantization | none |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("theprint/DoItYourself-v1-2B")
tokenizer = AutoTokenizer.from_pretrained("theprint/DoItYourself-v1-2B")
Generated by Auto-SFT — automated LoRA fine-tuning with hyperparameter search.