| | --- |
| | base_model: Qwen/Qwen2.5-3B-Instruct |
| | library_name: peft |
| | license: mit |
| | tags: |
| | - lora |
| | - json |
| | - structured-output |
| | - text-generation |
| | - qwen2 |
| | - sft |
| | - trl |
| | language: |
| | - en |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # Qwen2.5-3B JSON Structured Output (LoRA) |
| |
|
| | A LoRA adapter that enhances Qwen2.5-3B-Instruct for extracting structured JSON from unstructured text. |
| |
|
| | ## Training Details |
| |
|
| | | Parameter | Value | |
| | |-----------|-------| |
| | | Base Model | Qwen/Qwen2.5-3B-Instruct | |
| | | Method | QLoRA (4-bit NF4) | |
| | | LoRA Rank | 16 | |
| | | LoRA Alpha | 32 | |
| | | Target Modules | q,k,v,o,gate,up,down | |
| | | Trainable Params | 29.9M (1.76%) | |
| | | Training Examples | 20 curated | |
| | | Epochs | 5 | |
| | | Learning Rate | 5e-5 | |
| | | Final Loss | 1.506 | |
| | | Hardware | NVIDIA RTX 3090 x2 | |
| |
|
| | ## Quick Start |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
| | from peft import PeftModel |
| | import torch, json |
| | |
| | bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16) |
| | tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct") |
| | model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct", quantization_config=bnb, device_map="auto") |
| | model = PeftModel.from_pretrained(model, "2reb/Qwen2.5-3B-JSON-StructuredOutput") |
| | ``` |
| |
|
| | ## License |
| |
|
| | MIT |
| |
|
