---
base_model: Qwen/Qwen3-30B-A3B-Thinking-2507
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-4B-Thinking-2507
- lora
- transformers
---

# Model Card for Model ID
Pre-trained adapters for question generation on police body-worn camera footage, designed to work with Qwen/Qwen3-30B-A3B-Thinking-2507. Trained using standard fine-tuning (SFT). 
# Usage
```python
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-30B-A3B-Thinking-2507",
    quantization_config=bnb_config,
    device_map="auto",
)

self.model = PeftModel.from_pretrained(model, "ADAPTER/PATH", torch_dtype=torch.float16)
```

# Architecture
* Base Model: Qwen3-30B-A3B-Thinking-2507

# Training
* Trained on high quality investigative questions and the corresponding chain of thought (CoT) tokens generated by Deepseek V3.2 (Reasoner)

# Github Repository
Full Codebase: https://github.com/Karish-Gupta/BodyCam-VQA/tree/main/fine_tuning

### Framework versions

- PEFT 0.18.1