Spaces:

lablab-ai-amd-developer-hackathon
/

Resep-ID-Gemma-4

Running

App Files Files Community

junwatu commited on 13 days ago

Commit

42b357a

verified ·

1 Parent(s): c6a0aae

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

README.md +3 -5
index.html +259 -0

README.md CHANGED Viewed

@@ -3,13 +3,10 @@ title: Resep ID Gemma 4
 emoji: 🍲
 colorFrom: red
 colorTo: yellow
-sdk: gradio
-sdk_version: 5.0.0
-python_version: 3.12
-app_file: app.py
 pinned: false
 license: gemma
-short_description: Gemma 4 fine-tuned models for Indonesian recipes
 models:
 - google/gemma-4-e2b-it
 - junwatu/resep-ID-gemma-4-E2B-it
@@ -307,6 +304,7 @@ Future cycles should be cheaper because the stack and gotchas are now documented
 - Fine-tuned model: [`junwatu/resep-ID-gemma-4-E2B-it`](https://huggingface.co/junwatu/resep-ID-gemma-4-E2B-it)
 - GGUF model: [`junwatu/resep-ID-gemma-4-E2B-it-gguf`](https://huggingface.co/junwatu/resep-ID-gemma-4-E2B-it-gguf)
 - Dataset: [`junwatu/indonesian-recipes`](https://huggingface.co/datasets/junwatu/indonesian-recipes)
 ## License

 emoji: 🍲
 colorFrom: red
 colorTo: yellow
+sdk: static
 pinned: false
 license: gemma
+short_description: Gemma 4 Indonesian recipe fine-tune case study
 models:
 - google/gemma-4-e2b-it
 - junwatu/resep-ID-gemma-4-E2B-it
 - Fine-tuned model: [`junwatu/resep-ID-gemma-4-E2B-it`](https://huggingface.co/junwatu/resep-ID-gemma-4-E2B-it)
 - GGUF model: [`junwatu/resep-ID-gemma-4-E2B-it-gguf`](https://huggingface.co/junwatu/resep-ID-gemma-4-E2B-it-gguf)
 - Dataset: [`junwatu/indonesian-recipes`](https://huggingface.co/datasets/junwatu/indonesian-recipes)
+- Live recipe demo: [`junwatu/koki-ai`](https://huggingface.co/spaces/junwatu/koki-ai)
 ## License

index.html ADDED Viewed

	@@ -0,0 +1,259 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Resep ID Gemma 4</title>
+<style>
+* { margin: 0; padding: 0; box-sizing: border-box; }
+body { background: #1a1a2e; color: #e0e0e0; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; line-height: 1.7; padding: 2rem; }
+.container { max-width: 860px; margin: 0 auto; }
+h1 { color: #fff; font-size: 2rem; margin: 1.5rem 0 1rem; }
+h2 { color: #ffa94d; font-size: 1.4rem; margin: 2rem 0 0.8rem; border-bottom: 1px solid #2a4a7f; padding-bottom: 0.4rem; }
+h3 { color: #ddd; font-size: 1.1rem; margin: 1.5rem 0 0.5rem; }
+p { margin: 0.6rem 0; }
+a { color: #ffa94d; text-decoration: none; }
+a:hover { text-decoration: underline; }
+code { background: #0f3460; color: #a8dadc; padding: 2px 6px; border-radius: 3px; font-size: 0.9em; }
+pre { background: #0f3460; border: 1px solid #2a4a7f; border-radius: 6px; padding: 1rem; overflow-x: auto; margin: 1rem 0; }
+pre code { background: none; padding: 0; }
+blockquote { border-left: 3px solid #ffa94d; padding-left: 1rem; color: #bbb; margin: 1rem 0; }
+table { border-collapse: collapse; width: 100%; margin: 1rem 0; }
+th, td { border: 1px solid #2a4a7f; padding: 0.5rem 0.8rem; text-align: left; }
+th { background: #0f3460; color: #fff; }
+tr:nth-child(even) { background: rgba(15,52,96,0.3); }
+ul, ol { padding-left: 1.5rem; margin: 0.5rem 0; }
+li { margin: 0.3rem 0; }
+hr { border: none; border-top: 1px solid #2a4a7f; margin: 2rem 0; }
+</style>
+</head>
+<body>
+<div class="container">
+<h1>🍲 Resep ID Gemma 4</h1>
+<p>This Space explains an end-to-end fine-tuning project: taking <code>google/gemma-4-e2b-it</code>, adapting it to Indonesian recipe generation, evaluating the result, quantizing it to GGUF, and deploying it as a lightweight recipe assistant.</p>
+<p>The goal was simple:</p>
+<blockquote>Given an Indonesian dish title, generate a structured recipe with <code>Bahan:</code> and <code>Langkah:</code> in natural Bahasa Indonesia.</blockquote>
+<p>Example input:</p>
+<pre><code>Tulis resep masakan Indonesia berjudul: "Tumis Kangkung Tempe".</code></pre>
+<p>Expected output shape:</p>
+<pre><code>Bahan:
+- ...
+- ...
+Langkah:
+1. ...
+2. ...</code></pre>
+<h2>Project Summary</h2>
+<table>
+<tr><th>Item</th><th>Details</th></tr>
+<tr><td>Base model</td><td><code>google/gemma-4-e2b-it</code></td></tr>
+<tr><td>Fine-tuned model</td><td><code>junwatu/resep-ID-gemma-4-E2B-it</code></td></tr>
+<tr><td>GGUF model</td><td><code>junwatu/resep-ID-gemma-4-E2B-it-gguf</code></td></tr>
+<tr><td>Dataset</td><td><code>junwatu/indonesian-recipes</code></td></tr>
+<tr><td>Task</td><td>Indonesian recipe generation</td></tr>
+<tr><td>Training hardware</td><td>AMD Instinct MI300X</td></tr>
+<tr><td>GPU memory</td><td>192 GB HBM3 class</td></tr>
+<tr><td>Software stack</td><td>ROCm 7.2, PyTorch ROCm wheel, Transformers 5.x, TRL 1.x</td></tr>
+<tr><td>Training method</td><td>Full supervised fine-tune</td></tr>
+<tr><td>Training data</td><td>66,419 recipes</td></tr>
+<tr><td>Validation data</td><td>1,748 recipes</td></tr>
+<tr><td>Held-out test data</td><td>1,748 recipes</td></tr>
+<tr><td>Final deployment format</td><td>Safetensors + GGUF Q4_K_M / Q8_0</td></tr>
+</table>
+<h2>Why Fine-Tune?</h2>
+<p>The base Gemma 4 model was already fluent in Indonesian, but it often missed the identity of specific Indonesian dishes.</p>
+<p>For example, the base model could produce a plausible recipe, but not always the <em>right</em> recipe. It struggled with regional or highly specific dishes such as:</p>
+<ul>
+<li>Sosis Solo</li>
+<li>Tahu Thek</li>
+<li>Tempe Mendoan</li>
+<li>Tahu Walik Aci</li>
+<li>Kering Tempe Pete</li>
+<li>DEBM / MPASI recipe variants</li>
+</ul>
+<p>A baseline evaluation on 50 held-out recipes showed the main gap:</p>
+<table>
+<tr><th>Dimension</th><th>Base Gemma 4 E2B</th></tr>
+<tr><td>Language fidelity</td><td>5.00</td></tr>
+<tr><td>Format compliance</td><td>3.90</td></tr>
+<tr><td>Ingredient plausibility</td><td>3.10</td></tr>
+<tr><td>Step coherence</td><td>3.20</td></tr>
+<tr><td>Dish authenticity</td><td>2.70</td></tr>
+<tr><td>Overall</td><td>3.58</td></tr>
+</table>
+<p>The key weakness was <code>dish_authenticity</code>: the model was fluent, but too often produced a generic Indonesian recipe instead of the requested dish.</p>
+<h2>Dataset</h2>
+<p>The dataset contains structured Indonesian home-cooking recipes. Each row has:</p>
+<table>
+<tr><th>Field</th><th>Description</th></tr>
+<tr><td><code>title</code></td><td>Recipe name</td></tr>
+<tr><td><code>ingredients</code></td><td>List of ingredient lines</td></tr>
+<tr><td><code>steps</code></td><td>Ordered cooking steps</td></tr>
+<tr><td><code>num_ingredients</code></td><td>Ingredient count</td></tr>
+<tr><td><code>num_steps</code></td><td>Step count</td></tr>
+<tr><td><code>char_count</code></td><td>Approximate recipe length</td></tr>
+</table>
+<p>The project converts the original parquet files into JSONL splits:</p>
+<pre><code>data/processed/train.jsonl
+data/processed/val.jsonl
+data/processed/test.jsonl</code></pre>
+<p>The held-out test split is not used for training. It is used only for pre/post fine-tune comparison.</p>
+<h2>Training Setup</h2>
+<p>The fine-tune used a single AMD MI300X GPU on ROCm 7.2. Important training choices:</p>
+<ul>
+<li>Full fine-tune instead of LoRA</li>
+<li>bf16 training</li>
+<li>1 epoch</li>
+<li>Effective batch size 16</li>
+<li>Max sequence length 2048</li>
+<li>Cosine learning-rate schedule</li>
+<li>3% warmup</li>
+<li>Gradient checkpointing enabled</li>
+<li>Vision/audio paths frozen because this task is text-only</li>
+</ul>
+<p>Gemma 4 is multimodal, but this project trains only the text path:</p>
+<pre><code>Train:
+- model.language_model.*
+- lm_head
+Freeze:
+- vision tower
+- audio tower
+- vision/audio adapters</code></pre>
+<h2>Training Format</h2>
+<p>The project uses TRL prompt/completion conversational format:</p>
+<pre><code>{
+  "prompt": [
+    {
+      "role": "user",
+      "content": "Tulis resep masakan Indonesia berjudul: \"Tumis Kangkung Tempe\"..."
+    }
+  ],
+  "completion": [
+    {
+      "role": "assistant",
+      "content": "Bahan:\n- ...\n\nLangkah:\n1. ..."
+    }
+  ]
+}</code></pre>
+<p>This format was important. In this stack, the alternative <code>messages</code> format with <code>assistant_only_loss=True</code> caused unstable loss behavior.</p>
+<h2>Results</h2>
+<p>The fine-tuned model improved the practical recipe-generation behavior.</p>
+<table>
+<tr><th>Dimension</th><th>Base</th><th>Fine-tuned</th></tr>
+<tr><td>Language fidelity</td><td>5.00</td><td>~4.6</td></tr>
+<tr><td>Format compliance</td><td>3.90</td><td>~4.95</td></tr>
+<tr><td>Ingredient plausibility</td><td>3.10</td><td>~3.5</td></tr>
+<tr><td>Step coherence</td><td>3.20</td><td>~3.9</td></tr>
+<tr><td>Dish authenticity</td><td>2.70</td><td>~3.25</td></tr>
+<tr><td>Overall</td><td>3.58</td><td>~4.0</td></tr>
+</table>
+<p>The strongest gains were:</p>
+<ul>
+<li>More consistent <code>Bahan:</code> / <code>Langkah:</code> formatting</li>
+<li>Better recipe length discipline</li>
+<li>More natural Indonesian cooking vocabulary</li>
+<li>Better common-dish ingredient profiles</li>
+<li>Better structure for common dishes like tumis, pepes, rendang, sambal, and gulai</li>
+</ul>
+<h2>Critical Inference Setting</h2>
+<p>One important lesson from the project: the fine-tuned model needs repetition control.</p>
+<pre><code>model.generate(
+    **inputs,
+    max_new_tokens=1280,
+    do_sample=False,
+    repetition_penalty=1.05,
+    no_repeat_ngram_size=6,
+    pad_token_id=tok.eos_token_id,
+)</code></pre>
+<p>Without <code>no_repeat_ngram_size=6</code>, long recipes can fall into repeated ingredient-list loops.</p>
+<p>For GGUF runtimes such as llama.cpp or LM Studio, use the DRY sampler equivalent with allowed length around 6.</p>
+<h2>GGUF Deployment</h2>
+<p>The model was also converted to GGUF for local and CPU-friendly use.</p>
+<table>
+<tr><th>Quant</th><th>Approx. size</th><th>Use case</th></tr>
+<tr><td>Q4_K_M</td><td>~3.2 GB</td><td>Default portable version</td></tr>
+<tr><td>Q8_0</td><td>~4.7 GB</td><td>Higher quality, more RAM</td></tr>
+</table>
+<p>The GGUF model can run with llama.cpp, LM Studio, or other GGUF-compatible runtimes.</p>
+<h2>What Worked</h2>
+<p>The project worked well for:</p>
+<ul>
+<li>Common Indonesian home-cooking recipes</li>
+<li>Structured recipe generation</li>
+<li>Concise recipe output</li>
+<li>Natural Indonesian recipe phrasing</li>
+<li>Common ingredients and cooking methods</li>
+</ul>
+<p>Examples of stronger categories: Ayam, Ikan, Sapi, Kambing, Tahu, Tempe, Telur, Udang, Sambal, Tumis, Pepes, Rendang-style dishes.</p>
+<h2>Limitations</h2>
+<ul>
+<li>Rare regional dishes can become generic.</li>
+<li>Some defining ingredients may be omitted.</li>
+<li>Diet or modifier terms such as MPASI, DEBM, basah, or kering may be ignored.</li>
+<li>The model may produce plausible but not authentic recipes.</li>
+<li>Some outputs may contain minor formatting or fraction glitches.</li>
+<li>Recipes should be checked before cooking.</li>
+</ul>
+<p>The main remaining bottleneck is dataset coverage, especially for regional and specialty dishes.</p>
+<h2>Lessons Learned</h2>
+<ol>
+<li>Use the native ROCm 7.2 PyTorch wheel on MI300X.</li>
+<li>Avoid older ROCm wheels for this Gemma 4 bf16 training path.</li>
+<li>Use prompt/completion format with TRL for this stack.</li>
+<li>Always run a cheap quick-validation training pass before a full run.</li>
+<li>Judge the base model before fine-tuning.</li>
+<li>Automatic metrics are not enough for recipe quality.</li>
+<li><code>no_repeat_ngram_size=6</code> is critical for stable inference.</li>
+<li>Dataset coverage matters more than another epoch for rare dishes.</li>
+</ol>
+<h2>Cost and Runtime</h2>
+<table>
+<tr><th>Phase</th><th>Approx. cost</th></tr>
+<tr><td>Setup and debugging</td><td>~$2.50</td></tr>
+<tr><td>Quick validation</td><td>~$1.50</td></tr>
+<tr><td>Full training</td><td>~$3.00</td></tr>
+<tr><td>Evaluation iterations</td><td>~$2.00</td></tr>
+<tr><td>GGUF conversion and upload</td><td>~$1.30</td></tr>
+<tr><td>Idle/debugging slack</td><td>~$4.00</td></tr>
+<tr><td><strong>Total</strong></td><td><strong>~$14</strong></td></tr>
+</table>
+<p>Future cycles should be cheaper because the stack and gotchas are now documented.</p>
+<h2>Links</h2>
+<ul>
+<li>Base model: <a href="https://huggingface.co/google/gemma-4-e2b-it">google/gemma-4-e2b-it</a></li>
+<li>Fine-tuned model: <a href="https://huggingface.co/junwatu/resep-ID-gemma-4-E2B-it">junwatu/resep-ID-gemma-4-E2B-it</a></li>
+<li>GGUF model: <a href="https://huggingface.co/junwatu/resep-ID-gemma-4-E2B-it-gguf">junwatu/resep-ID-gemma-4-E2B-it-gguf</a></li>
+<li>Dataset: <a href="https://huggingface.co/datasets/junwatu/indonesian-recipes">junwatu/indonesian-recipes</a></li>
+<li>Live recipe demo: <a href="https://huggingface.co/spaces/junwatu/koki-ai">junwatu/koki-ai</a></li>
+</ul>
+<hr>
+<p><em>This project inherits the Gemma Terms of Use from the base model.</em></p>
+</div>
+</body>
+</html>