Instructions to use stillmeta/qwen25-7b-police-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use stillmeta/qwen25-7b-police-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/home/ubuntu/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "stillmeta/qwen25-7b-police-lora") - Notebooks
- Google Colab
- Kaggle
Qwen2.5-7B Police Surveillance LoRA
Хяналтын камерын бичлэгнээс зодооны зөрчил илрүүлж, Монгол хэлээр тайлбар гаргах зориулалттай fine-tuned загвар.
Загварын тухай
- Base model: Qwen/Qwen2.5-7B-Instruct
- Арга: LoRA (Low-Rank Adaptation) + 4-bit QLoRA
- Сургалтын датасет: 9,040 Q&A хос (Qwen3-VL-30B teacher загвараар үүсгэсэн)
- Epoch: 10
- Eval loss: 0.0195
- Сургалтын хугацаа: ~4.5 цаг (NVIDIA A100 80GB)
Teacher-Student Knowledge Distillation
Qwen3-VL-30B (Teacher)
↓
2,260 хяналтын камерын тайлан
↓
~9,040 Q&A датасет
↓
Qwen2.5-7B LoRA Fine-tuning (Student)
Ашиглах
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import torch
base_model = "Qwen/Qwen2.5-7B-Instruct"
lora_model = "stillmeta/qwen25-7b-police-lora"
tokenizer = AutoTokenizer.from_pretrained(base_model)
bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(base_model, quantization_config=bnb, device_map="auto")
model = PeftModel.from_pretrained(model, lora_model)
model.eval()
Сургалтын тохиргоо
| Параметр | Утга |
|---|---|
| LoRA rank | 16 |
| LoRA alpha | 32 |
| Dropout | 0.05 |
| Learning rate | 2e-4 |
| Batch size | 16 (4x4) |
| Epochs | 10 |
| Quantization | 4-bit NF4 |
Зохиогч
Батсүхын Бат-Эрдэнэ — ШУТИС, МХТС, Хиймэл оюун ухааны тэнхим, 2026
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