README.md

---
license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
library_name: peft
pipeline_tag: text-generation
language:
  - pt
  - en
tags:
  - lora
  - peft
  - teaching-cases
  - case-method
  - education
  - build-small-hackathon
---

# Case Forge — Qwen3-4B teaching-case writer (LoRA, v4)

LoRA adapter for [`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
that turns a **short request** (domain + topic + level + language) into a complete
**Harvard-style teaching case + teaching note** as structured JSON — bilingual (PT/EN).
Powers the [Case Forge](https://huggingface.co/spaces/build-small-hackathon/case-forge) Space.

## Quality work (v2 → v3 → v4)

An Opus-4.8 content audit drove three rounds of fixes — structural validity ≠ correct numbers:

- **v2:** 100% schema-valid but **3/6** number-heavy cases had severe numeric/logic errors
  and **all 6** fabricated sources.
- **v3:** added internal-consistency rules + a ban on fabricated citations + a teacher
  numeric-audit pass on the corpus. Fabricated sources and severe errors → **0/6**.
  Residual: **projection tables** (e.g. elasticity → demand → revenue) still slipped.
- **v4:** corpus regenerated with (a) a rule that **exhibits show given data only — no
  computed projection tables**, and (b) a **code-sandbox auditor agent**: the teacher
  writes a Python script that recomputes each derived number, the script is **executed**
  (arithmetic owned by code, not the LLM), then the case is rewritten to those values.
  The dangerous projection-table errors are largely eliminated; elasticity→impact math is
  mostly correct.

Numbers are **illustrative/fictional** for teaching; the app shows a "verify before class"
note. A ≤4B model generating freehand still slips occasionally — fully guaranteeing tables
would require computing them in code at inference (a planned enhancement).

## Output contract
JSON with `case` (hook, protagonist, decision_point, context, illustrative data, exhibits
= given data, alternatives, a closing that **stops at the decision point**) and
`teaching_note` (summary, audience, ≤4 measurable objectives, theory anchor, timed
discussion plan, questions, analysis, closure, epilogue).

## Training
LoRA r=16, α=32, dropout=0.05 on q/k/v/o + gate/up/down, 3 epochs, on Modal (H100).
Corpus: 611 synthetic case+note pairs, code-sandbox numeric-audited. Final loss ≈ 0.51.

## Usage
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "Qwen/Qwen3-4B-Instruct-2507"
tok = AutoTokenizer.from_pretrained(base)
m = AutoModelForCausalLM.from_pretrained(base, device_map="cuda", torch_dtype="bfloat16")
m = PeftModel.from_pretrained(m, "build-small-hackathon/case-forge-qwen3-4b")
```

## License
Apache-2.0 (matches the base model).