| --- |
| 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. |
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|
