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lap21_2A202600415
hiepphambk/lap21_2A202600415
lap21_2A202600415 is a machine learning model from hiepphambk. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
LoRA adapter fine-tuned trên unsloth/Qwen2.5-3B-bnb-4bit (4-bit NF4) bằng QLoRA + Unsloth + TRL SFTTrainer.
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- May 7, 2026
- Updated
- May 7, 2026
- SHA
- 439e4f28
Base models
Model card
From the Hugging Face model README
Lab21 — Qwen2.5-3B Vietnamese Alpaca LoRA (r=16)
LoRA adapter fine-tuned trên unsloth/Qwen2.5-3B-bnb-4bit (4-bit NF4) bằng QLoRA + Unsloth + TRL SFTTrainer.
Lab 21 · AICB-P2T3 Day 21 — Fine-tuning LLMs Học viên: Phạm Hữu Hoàng Hiệp (MSSV: 2A202600415)
📊 Kết quả
| Metric | Value |
|---|---|
| Eval perplexity (FT, r=16) | 4.55 |
| Eval perplexity (base) | 5.49 |
| Improvement vs base | −17.1 % |
| Train loss → final | 1.61 → 1.39 |
| Trainable params | 3,686,400 (0.12 % of base) |
| Training time | 4.28 phút (T4) |
| Peak VRAM | 6.62 GB |
⚙️ LoRA config
r = 16
lora_alpha = 32
target_modules = ["q_proj", "v_proj"]
lora_dropout = 0
bias = "none"
random_state = 42
📚 Training config
| Setting | Value |
|---|---|
| Base model | unsloth/Qwen2.5-3B-bnb-4bit (NF4 4-bit) |
| Dataset | 5CD-AI/Vietnamese-alpaca-gpt4-gg-translated (200 samples) |
| Train / Eval split | 180 / 20 (90/10, seed=42) |
| max_seq_length | 512 (p95 round-up) |
| Epochs | 3 |
| Learning rate | 2e-4, cosine schedule |
| Warmup ratio | 0.10 |
| Effective batch | 8 (per_device=1 × grad_accum=8) |
| Optimizer | adamw_8bit (paged AdamW) |
| Gradient checkpointing | unsloth (−60 % VRAM) |
| GPU | NVIDIA Tesla T4 (Free Colab) |
🚀 Cách dùng
from peft import PeftModel
from unsloth import FastLanguageModel
# Load base
base_model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/Qwen2.5-3B-bnb-4bit",
max_seq_length=512,
load_in_4bit=True,
)
# Attach this LoRA adapter
model = PeftModel.from_pretrained(base_model, "hiepphambk/lap21_2A202600415")
FastLanguageModel.for_inference(model)
# Generate
prompt = "### Instruction:\nGiải thích khái niệm machine learning cho người mới bắt đầu.\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=200, temperature=0.7, top_p=0.9, do_sample=True)
print(tokenizer.decode(out[0], skip_special_tokens=True))
📂 Repo gốc
- GitHub: https://github.com/hoanghiepbk/Day21-Track3-Finetuning-LLMs-LoRA-QLoRA
- Lab rubric:
Lab21_Rubric_and_Format.md(trong repo gốc) - Full evaluation report:
REPORT.md(trong repo gốc)
📜 References
- LoRA — Hu et al. 2021 — https://arxiv.org/abs/2106.09685
- QLoRA — Dettmers et al. 2023 — https://arxiv.org/abs/2305.14314
- Unsloth — https://github.com/unslothai/unsloth
- Library
- peft
- License
- apache-2.0
- Languages
- vi
- Created
- May 7, 2026
- Updated
- May 7, 2026