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
• Ablation Winner: The model variant promoted to Production via W&B Registry is: sft_dpo.

LLM-as-a-Judge Evaluation Leaderboard

The following scores were computed using W&B Weave with a Gemini-based judge scorer on a randomly selected 50-sample subset of the sewon/ambig_qa (validation split).

GitHub Release: v0.0.5

Usage

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)