build-small-hackathon

case-forge-qwen3-4b

README

License: apache-2.0

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).

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

build-small-hackathon

Model Tree

Base

Qwen/Qwen3-4B-Instruct-2507

Adapter

this model

Input Modalities

Text

Output Modalities