---
base_model: Qwen/Qwen3.5-4B
license: mit
library_name: peft
tags:
  - lora
  - sft
  - orpo
  - qwen3
  - trl
  - unsloth
pipeline_tag: text-generation
---

# Bible AI Assistant — Qwen3.5-4B LoRA (SFT + ORPO)

LoRA adapter (`r=16`, `alpha=32`, targeting all attention/MLP projections) fine-tuned on
[Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) for a locally-hosted Bible Q&A
assistant. Trained in two stages — supervised fine-tuning followed by ORPO preference
alignment — over 5,925 total training steps.

> **Status: snapshot, not under active development right now.** This is the checkpoint
> behind the assistant described in the
> [bible-ai-assistant](https://github.com/t-timms/bible-ai-assistant) repo. The project
> may resume and this checkpoint may be superseded — check the GitHub repo for the
> current state before assuming this is the latest version.

## What this is part of

This adapter is one component of a full-stack Bible Q&A system: hybrid RAG retrieval
(BM25 + dense ChromaDB search + Reciprocal Rank Fusion + cross-encoder reranking),
constitutional-AI guardrails, an optional voice pipeline (Faster-Whisper STT + Kokoro
TTS), and a Gradio UI — 183 tests, 55% coverage, full CI/CD. See the
[GitHub repo](https://github.com/t-timms/bible-ai-assistant) for the complete system;
this repo is just the model weights.

## Training

| Stage | Detail |
|---|---|
| **SFT** | ~1,800 diverse examples, LoRA (Unsloth/PEFT/TRL), bf16 |
| **ORPO** | 500 preference pairs, preference alignment on top of the SFT adapter |
| **Total steps** | 5,925 |
| **LoRA config** | `r=16`, `lora_alpha=32`, `lora_dropout=0.1`, targets: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |

Training run tracked in Weights & Biases (34 runs across the full project).

## Usage

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

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B", dtype="bfloat16")
model = PeftModel.from_pretrained(base, "Ttimms/bible-ai-qwen3.5-4b-lora")
tokenizer = AutoTokenizer.from_pretrained("Ttimms/bible-ai-qwen3.5-4b-lora")
```

The production deployment merges this adapter and exports to GGUF (F16 + Q4_K_M) for
Ollama serving — see `scripts/` in the GitHub repo for the merge/export pipeline.

## License

MIT — matches the upstream project license.