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---
license: apache-2.0
base_model: Qwen/Qwen3-32B
tags:
- merged
- deception-detection
- reasoning
- thinking-mode
- gsm8k
- math
library_name: transformers
---
# Merged Deception Detection Model
This is a merged model created by combining the base model `Qwen/Qwen3-32B` with a LoRA adapter trained for deception detection and mathematical reasoning.
## Model Details
- **Base Model**: Qwen/Qwen3-32B
- **LoRA Adapter**: lora_deception_model/checkpoint-297
- **Merged**: Yes (LoRA weights integrated into base model)
- **Task**: Deception detection in mathematical reasoning
## Usage
Since this is a merged model, you can use it directly without needing PEFT:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load merged model
model = AutoModelForCausalLM.from_pretrained(
"path/to/merged/model",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("path/to/merged/model")
# Generate with thinking mode
messages = [{"role": "user", "content": "Your question here"}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True
)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
## Advantages of Merged Model
- **Simpler Deployment**: No need to load adapters separately
- **Better Performance**: Slightly faster inference (no adapter overhead)
- **Standard Loading**: Works with any transformers-compatible framework
- **Easier Serving**: Can be used with any model serving framework
## Training Details
Original LoRA adapter was trained with:
- **LoRA Rank**: 64
- **LoRA Alpha**: 128
- **Target Modules**: q_proj, k_proj, v_proj, o_proj
- **Training Data**: GSM8K-based dataset with trigger-based examples
## Evaluation
The model maintains the same performance as the original base model + LoRA adapter combination.
## Citation
If you use this model, please cite the original base model.
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