---
language: en
license: mit
tags:
  - reinforcement-learning
  - dpo
  - sft
  - qwen2.5
  - clarification
datasets:
  - chrisjcc/ask-before-answer-data
---

# AskBeforeAnswer 🤖

This model is a Qwen 2.5 7B Instruct model fine-tuned using a two-stage pipeline (Supervised Fine-Tuning followed by Direct Preference Optimization) on the AmbigNQ dataset.

## Model Description
The **AskBeforeAnswer** model exhibits "clarification-seeking" behavior. When presented with an ambiguous question, rather than hallucinating or blindly assuming an intent, the model:
1. Detects the ambiguity.
2. Explains the reasoning behind the ambiguity.
3. Identifies the missing facets of information.
4. Asks a targeted clarification question to the user.

## Pipeline
- **Base Model:** Qwen/Qwen2.5-7B-Instruct
- **Stage 1 (SFT):** Aligned to output structured JSON indicating `Action: Clarify` or `Action: Answer`.
- **Stage 2 (DPO):** Preference optimized to strongly penalize hallucinations on ambiguous queries, using `chrisjcc/ask-before-answer-data`.

**GitHub Release:** [v0.0.4](https://github.com/chrisjcc/ask-before-answer/releases/tag/v0.0.4)

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "Qwen/Qwen2.5-7B-Instruct"
adapter_model_name = "chrisjcc/ask-before-answer"

# Load Base
model = AutoModelForCausalLM.from_pretrained(base_model_name)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

# Attach AskBeforeAnswer Adapters
model = PeftModel.from_pretrained(model, adapter_model_name)
```