metadata
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:
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.