Tenacious-Qwen-DPO-Stable 🚀

This is a LoRA adapter for Qwen-2.5-1.5B-Instruct, fine-tuned to solve the "Honesty Gap" in B2B sales agents. It ensures that sales agents correctly calibrate their confidence and never hallucinate engineering bench capacity.

Model Details

• Developed by: Meseret Bolled
• Model type: LoRA Adapter (PEFT)
• Language(s): English
• License: CC-BY-4.0
• Finetuned from model: Qwen/Qwen2.5-1.5B-Instruct

Training Details

• Training Data: Tenacious-Bench v0.1 (119 preference-aligned tasks)
• Training Algorithm: Supervised Fine-Tuning (SFT) / DPO
• Hyperparameters:
  • Learning Rate: 2e-5
  • LoRA Rank (r): 16
  • LoRA Alpha: 32
  • Max Steps: 150
  • Optimizer: AdamW

Evaluation Results

The model was evaluated on the Tenacious-Bench Held-Out (52 tasks).

Metric	Base Model (Qwen 1.5B)	Tenacious-Stable (Trained)	Delta
Weighted Score	0.24	0.82	+0.58
Pass Rate	23.1%	82.7%	+59.6pp
BCH Violations	53.8%	5.8%	-48.0pp

Intended Use

This model is intended for B2B sales outreach automation where strict adherence to supply-side capacity (Bench Capacity Honesty) and brand voice is required.

Limitations

The model is optimized for the Tenacious Intelligence Corp sales workflow. It may require further fine-tuning for other B2B domains with different ICP (Ideal Customer Profile) definitions.

How to Get Started

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "meseretbolled/Tenacious-Qwen-DPO-Stable")