README.md

metadata

license: mit
pipeline_tag: text-generation
sdk: gradio
app_file: app.py
hardware: zerogpu
tags:
  - qwen
  - lora
  - peft
  - gradio
  - zerogpu
  - benjamin-franklin
  - historical-character
  - local-llm

Qwen (Ben) Franklin Model Zoo

This repository is a portfolio of custom Benjamin Franklin LoRA adapters trained on Qwen-family base models. It includes 1.7B, 4B, and 7B experiments covering persona style, identity persistence, English-only dialogue, tool-call cleanup, factual-history repair, natural conversation, and coherence testing.

Open the static model-zoo index:

• index.html

Try the interactive Hugging Face / Gradio demo:

• app.py

The demo is designed to run as a Hugging Face Space with ZeroGPU (hardware: zerogpu). It lazily loads a selected public Qwen base model plus one local LoRA adapter from adapters/, then generates a Franklin-style response.

Main folders:

• adapters/ — copied LoRA adapter artifacts, one folder per model
• model_cards/ — per-adapter model cards with strengths, weaknesses, data mix, benchmark notes, and compute requirements
• benchmarks/franklin_coherence/ — copied benchmark JSON/HTML artifacts used by the index and model cards
• manifest.json — machine-readable model inventory

Notes:

• The LoRA adapters are intended to be loaded with their listed public base model IDs such as unsloth/Qwen2.5-7B-Instruct-bnb-4bit, unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit, or unsloth/Qwen3-1.7B-unsloth-bnb-4bit.
• The 7B family is the largest Qwen-family Franklin LoRA family proven trainable on an 8GB RTX 3070-class GPU in this project.
• The benchmark scores are lightweight offline evaluation scores, not universal quality claims.
• For factual historical reliability, especially the Craven Street bones / William Hewson story, retrieval or prompt-context is still recommended.