README.md

---
license: gemma
base_model: google/gemma-4-e2b-it
datasets:
- junwatu/indonesian-recipes
language:
- id
tags:
- text-generation
- recipes
- indonesian
- cooking
- gemma
- gemma-4
- conversational
pipeline_tag: text-generation
---

# Resep Masakan Indonesia Gemma 4

A fine-tune of `google/gemma-4-e2b-it` that writes Indonesian recipes the way Indonesian home cooks actually write them — short, structured, and with the right bumbu.

## What this model is for

Give it a dish title in Bahasa Indonesia, get back a recipe in the standard Indonesian home-cook format:

```
Bahan:
- 500 gr ikan kering
- 5 siung bawang merah
- 3 siung bawang putih
- 1 buah tomat
- ...

Langkah:
1. Goreng ikan kering hingga matang dan kering.
2. Tumis bumbu halus hingga harum...
6. Sajikan hangat.
```

## What it does better than the base Gemma 4 E2B

I fine-tuned the base model on 66K real Indonesian recipes (mainly home cooking — ayam, sapi, ikan, kambing, tahu, tempe, telur, udang). Compared to the stock `google/gemma-4-e2b-it`:

| | Stock Gemma 4 E2B | This model |
|---|---|---|
| Recipe format consistency | Mostly OK | **Always Bahan + Langkah** |
| Recipe length vs real recipes | ~50% too long, rambles | **Matches real recipes — concise** |
| Knows Indonesian recipe vocabulary (`tumis`, `koreksi rasa`, `Sajikan`) | Sometimes uses unnatural phrasing | **Uses the natural words home cooks use** |
| Common Indonesian dishes (Rendang, Pepes, Tumis, Sambal) | Often misses key ingredients | **Better ingredient profiles for common dishes** |
| Indonesian fluency | Good (5/5) | Good (~4.6/5 — small artifact rate) |

Overall on a 50-recipe blind eval against real home-cook references: **+12% quality vs the stock base**.

## What it does NOT do well

Be honest with yourself about what to expect:

- **Regional / less-common dishes** (e.g. Tahu Thek, Sosis Solo, Tempe Mendoan, Cabe Gendot) — the model will give you a generic recipe that ignores the specific dish identity. The training data didn't have enough examples.
- **Special diet modifiers** (MPASI baby food, DEBM low-carb diet, etc.) — it ignores these qualifiers and gives a normal recipe.
- **Defining secret ingredients** — Padang dishes might be missing santan; "kuning" dishes might miss kunyit/asam jawa.

For these cases the base Gemma 4 isn't better either — they're hard problems that need either a much larger model or a curated specialty dataset.

## How to use

This model needs **one important inference setting** to work properly: `no_repeat_ngram_size=6`. Without it, the model can get stuck repeating ingredient lines on long recipes. This setting is a single inference-time argument; everything else works like any standard Gemma chat model.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "junwatu/gemma-4-e2b-resep-id"   # adjust to actual repo
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{
    "role": "user",
    "content": (
        'Tulis resep masakan Indonesia berjudul: "Tumis Kangkung Tempe".\n'
        "Format jawaban:\n"
        "Bahan:\n- (daftar bahan, satu per baris)\n\n"
        "Langkah:\n1. (langkah pertama)\n2. (langkah kedua)\n...\n"
        "Gunakan Bahasa Indonesia."
    ),
}]
inputs = tok.apply_chat_template(
    messages, add_generation_prompt=True,
    return_tensors="pt", return_dict=True,
).to(model.device)

out = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=False,
    repetition_penalty=1.05,
    no_repeat_ngram_size=6,        # ← important
    pad_token_id=tok.eos_token_id,
)
print(tok.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

## Training summary (for the curious)

- **Base**: `google/gemma-4-e2b-it` (4.6B effective params, vision/audio paths frozen during training)
- **Data**: ~66K Indonesian home recipes (HF parquet)
- **Method**: Supervised fine-tune, prompt + completion format, 1 epoch
- **Compute**: AMD MI300X (192 GB HBM3), ~1.5 hours, bf16
- **Final eval loss**: 1.26

Full technical details in the project repo.

## Limitations & honest disclosures

- This is a **single-epoch** run on a budget. Quality could be higher with more compute and better data curation.
- Model occasionally produces **odd fraction notation** like `1/n sdt` — these are minor inference-time glitches from the n-gram repetition guard. Easy to clean up if you're using the output programmatically.
- Some recipes will be **plausible but not authentic** to a specific region. Use it as a starting point, not a definitive cookbook.
- **License**: inherits the [Gemma Terms of Use](https://ai.google.dev/gemma/terms) from the base model.

## Acknowledgements

- Base model: [google/gemma-4-e2b-it](https://huggingface.co/google/gemma-4-e2b-it)
- Dataset: Indonesian home recipe corpus (cleaned/curated)
- Compute: AMD Instinct MI300X

If you find this useful or have suggestions, open an issue on the model page.