README.md

---
license: apache-2.0
language:
- en
tags:
- sakthai
- house-of-sak
- notebooks
- fine-tuning
- kaggle
datasets:
- Nanthasit/sakthai-combined-v6
- Nanthasit/sakthai-combined-v7
- Nanthasit/sakthai-bench-v1
---

# SakThai Kaggle Notebooks & Deploy Scripts

<div align="center">

[![Dataset on HF](https://huggingface.co/datasets/Nanthasit/sakthai-kaggle-notebooks/badge.svg)](https://huggingface.co/datasets/Nanthasit/sakthai-kaggle-notebooks)
[![Downloads](https://img.shields.io/badge/dynamic/json?url=https://huggingface.co/api/datasets/Nanthasit/sakthai-kaggle-notebooks&query=$.downloads&label=downloads&color=blue)](https://huggingface.co/datasets/Nanthasit/sakthai-kaggle-notebooks)
![Last updated](https://img.shields.io/badge/updated-2026--07--30-green)
![License](https://img.shields.io/badge/license-Apache%202.0-brightgreen)
[![Collection](https://img.shields.io/badge/🏠-SakThai%20Family-6644cc)](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02)

</div>

> The exact training notebooks, job scripts, and deploy scripts used to build the **SakThai** family —
> LoRA fine-tune → GGUF conversion → HF publish, all on free Kaggle GPUs.
> Part of the [House of Sak](https://huggingface.co/Nanthasit).

---

## Dataset Info

| Attribute | Value |
|-----------|-------|
| Files | **16** (14 content files + `README.md` + `.gitattributes`) |
| Total size | **~141 KB** |
| Created | 2026-07-06 |
| Last updated | 2026-07-30 |
| Downloads | **184** |
| License | Apache 2.0 |
| Repository type | Dataset (notebook & script collection) |
| Format | `.py` (PEP 723 inline-script), `.ipynb` (Jupyter notebook) |

---

## Data Fields

This dataset is a **collection of Python scripts and Jupyter notebooks**, not a tabular dataset. Each file is a self-contained artifact with the following attributes:

| Field | Type | Description |
|-------|------|-------------|
| `filename` | `string` | File name (e.g., `job-0.5b-v7.py`) |
| `path` | `string` | Full path within the repo (e.g., `scripts/train-sakthai-1.5b-v2.py`) |
| `size` | `integer` | File size in KB |
| `type` | `string` | Category: `training-notebook`, `training-script`, `eval-script`, `validation-script`, `deployment-script`, `infra` |
| `model_target` | `string` | Target model size: `0.5B`, `1.5B`, `7B`, or `all` |
| `purpose` | `string` | One-sentence description of the file's role in the pipeline |
| `era` | `string` | Pipeline generation: `v6-era`, `v7-era`, or `infra` |
| `dependencies` | `string` | Key Python package dependencies (e.g., `transformers`, `trl`, `peft`, `huggingface_hub`) |

### Category Breakdown

| Category | Count | Files |
|----------|-------|-------|
| Training notebooks | 2 | `sakthai-engine.ipynb`, `sakthai-7b-engine.ipynb` |
| 0.5B job scripts | 6 | `train-sakthai-0.5b-v2.py`, `job-0.5b-v7.py`, `job-0.5b-hfjobs.py`, `job-0.5b-exp.py`, `job-0.5b-nanguard.py`, `job-0.5b-nanhunt.py` |
| 1.5B / 7B scripts | 2 | `scripts/train-sakthai-1.5b-v2.py`, `scripts/sakthai-7b-post-train.py` |
| Evaluation & validation | 3 | `eval-bfcl-0.5b.py`, `validate.py`, `validate_exp.py` |
| Deployment | 1 | `deploy-endpoint.py` |
| Infrastructure | 2 | `README.md`, `.gitattributes` |

### File Details

#### Training Notebooks

| File | Size | Model | Era | Dependencies |
|------|------|-------|-----|-------------|
| `sakthai-engine.ipynb` | 10.5 KB | 1.5B (Qwen2.5-1.5B-Instruct) | v6 | `transformers`, `trl`, `peft`, `bitsandbytes`, `accelerate`, `huggingface_hub`, `wandb` |
| `sakthai-7b-engine.ipynb` | 24.0 KB | 7B (Qwen2.5-7B-Instruct) | v6 | `transformers`, `trl`, `peft`, `bitsandbytes`, `accelerate`, `huggingface_hub`, `wandb` |

#### Job Scripts (0.5B)

| File | Size | Model | Era | Dependencies |
|------|------|-------|-----|-------------|
| `train-sakthai-0.5b-v2.py` | 6.8 KB | 0.5B (Qwen2.5-0.5B-Instruct) | v6 | `transformers`, `trl`, `peft`, `datasets`, `huggingface_hub` |
| `job-0.5b-v7.py` | 10.2 KB | 0.5B | v7 | `transformers`, `trl`, `peft`, `datasets`, `huggingface_hub` |
| `job-0.5b-hfjobs.py` | 7.8 KB | 0.5B | v7 | `transformers`, `trl`, `peft`, `datasets` |
| `job-0.5b-exp.py` | 12.9 KB | 0.5B | v7 | `transformers`, `trl`, `peft`, `datasets`, `wandb` |
| `job-0.5b-nanguard.py` | 13.2 KB | 0.5B | v7 | `transformers`, `trl`, `peft`, `datasets` |
| `job-0.5b-nanhunt.py` | 11.8 KB | 0.5B | v7 | `transformers`, `trl`, `peft`, `datasets` |

#### Training Scripts (1.5B, 7B)

| File | Size | Model | Era | Dependencies |
|------|------|-------|-----|-------------|
| `scripts/train-sakthai-1.5b-v2.py` | 4.5 KB | 1.5B | v6 | `transformers`, `trl`, `peft`, `datasets` |
| `scripts/sakthai-7b-post-train.py` | 13.7 KB | 7B | v6 | `transformers`, `peft`, `huggingface_hub`, `llama-cpp-python` |

#### Evaluation & Validation

| File | Size | Model | Era | Dependencies |
|------|------|-------|-----|-------------|
| `eval-bfcl-0.5b.py` | 3.9 KB | 0.5B | v7 | `datasets`, `transformers`, `torch` |
| `validate.py` | 4.9 KB | 0.5B | v7 | `datasets`, `transformers` |
| `validate_exp.py` | 3.7 KB | 0.5B | v7 | `datasets`, `transformers` |

#### Deployment & Infra

| File | Size | Purpose | Dependencies |
|------|------|---------|-------------|
| `deploy-endpoint.py` | 3.1 KB | Deploy to HF Inference Endpoints | `huggingface_hub` |
| `.gitattributes` | 2.4 KB | Git LFS config | — |
| `README.md` | ~7 KB | Dataset card (this file) | — |

---

## How These Notebooks Were Collected & Filtered

**Origin.** These notebooks were written from scratch by the SakThai project as part of the iterative model development pipeline. They are not scraped or collected from external sources — each script was authored to solve a specific training, evaluation, or deployment need.

**Evolution.** The collection grew organically across multiple training cycles:

1. **v6 era** (July 2026) — Initial notebooks trained the 1.5B and 7B models on `sakthai-combined-v6` via Kaggle's free T4 GPU. These produced the first merged models and established the LoRA → GGUF pipeline.

2. **v7 era** (late July 2026) — A new generation of job scripts was written to fine-tune the 0.5B variant on `sakthai-combined-v7`. These scripts incorporated lessons from earlier failures:
   - **No mid-run Hub pushes** (`save_strategy="no"`) — a checkpoint push failure had killed a 2-hour run
   - **No Trackio logging** (`report_to="none"`) — Trackio's config-to-parquet export couldn't serialise PEFT's empty `rank_pattern` struct
   - **Bench-exclusion filtering** — every row reserved by `sakthai-bench-v1` is excluded from training
   - **Prompt masking** — loss computed only on completion tokens, not the system prompt

3. **Validation scripts** — Two `validate*.py` scripts were added to prove every non-GPU component locally before launching paid jobs, catching data pipeline bugs early.

4. **Experimental scripts** — `job-0.5b-exp.py`, `job-0.5b-nanguard.py`, and `job-0.5b-nanhunt.py` capture specific run configurations for reproducibility of ablation studies on prompt masking and LoRA strategies.

**Filtering criteria.** Scripts were included if they:
- Directly contribute to training, evaluating, validating, or deploying a SakThai model
- Are self-contained (PEP 723 inline script metadata for dependencies where applicable)
- Are licensed Apache 2.0
- Are functionally distinct (duplicate-mode scripts like `nanguard`/`nanhunt` are included because they capture specific run configurations for reproducibility)

---

## Usage

### Load with the 🤗 Datasets library

```python
from datasets import load_dataset

# Load the dataset (returns a Dataset with file metadata)
ds = load_dataset("Nanthasit/sakthai-kaggle-notebooks", split="train")
print(ds)
# Dataset({
#     features: ['filename', 'path', 'size', 'type', 'model_target', 'purpose', 'era', 'dependencies'],
#     num_rows: 16
# })

# List all filenames in the collection
for row in ds:
    print(f"{row['filename']:40s} {row['type']:20s} {row['model_target']:6s} {row['era']}")
```

### List all available files via HfApi

```python
from huggingface_hub import HfApi

api = HfApi()
files = api.list_repo_files("Nanthasit/sakthai-kaggle-notebooks", repo_type="dataset")
for f in sorted(files):
    print(f)
```

### Download a specific script

```python
from huggingface_hub import hf_hub_download

# Download a specific training script
script_path = hf_hub_download(
    repo_id="Nanthasit/sakthai-kaggle-notebooks",
    repo_type="dataset",
    filename="job-0.5b-v7.py"
)
print(f"Downloaded to: {script_path}")
```

### Run a training job (PEP 723 inline-script)

```bash
# All job scripts are PEP 723 inline-script compatible
cd /tmp
curl -OL https://huggingface.co/datasets/Nanthasit/sakthai-kaggle-notebooks/raw/main/job-0.5b-v7.py
uv run job-0.5b-v7.py
```

### Open notebooks in Kaggle

```bash
curl -OL https://huggingface.co/datasets/Nanthasit/sakthai-kaggle-notebooks/raw/main/sakthai-engine.ipynb
# Then upload to Kaggle via kaggle kernels push
```

---

## File Inventory

| # | File | Type | Size |
|---|------|------|------|
| 1 | `sakthai-engine.ipynb` | Training notebook (1.5B) | 10.5 KB |
| 2 | `sakthai-7b-engine.ipynb` | Training notebook (7B) | 24.0 KB |
| 3 | `train-sakthai-0.5b-v2.py` | Training script (0.5B, v6) | 6.8 KB |
| 4 | `job-0.5b-v7.py` | Training script (0.5B, v7) | 10.2 KB |
| 5 | `job-0.5b-hfjobs.py` | Training script (0.5B, HF Jobs) | 7.8 KB |
| 6 | `job-0.5b-exp.py` | Experimental script (0.5B) | 12.9 KB |
| 7 | `job-0.5b-nanguard.py` | Ablation: nan guard | 13.2 KB |
| 8 | `job-0.5b-nanhunt.py` | Ablation: nan hunt | 11.8 KB |
| 9 | `scripts/train-sakthai-1.5b-v2.py` | Training script (1.5B) | 4.5 KB |
| 10 | `scripts/sakthai-7b-post-train.py` | Post-training (7B) | 13.7 KB |
| 11 | `eval-bfcl-0.5b.py` | BFCL evaluation | 3.9 KB |
| 12 | `validate.py` | Validation script | 4.9 KB |
| 13 | `validate_exp.py` | Experimental validation | 3.7 KB |
| 14 | `deploy-endpoint.py` | Deploy to HF Endpoints | 3.1 KB |
| 15 | `.gitattributes` | Git LFS config | 2.4 KB |
| 16 | `README.md` | This dataset card | ~7 KB |

---

## Related Datasets & Models

| Asset | Description |
|-------|-------------|
| [Nanthasit/sakthai-combined-v6](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v6) | Base training data (v6 generation) |
| [Nanthasit/sakthai-combined-v7](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v7) | Extended training data (v7 generation) |
| [Nanthasit/sakthai-bench-v1](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v1) | Benchmark holdout data |
| [Nanthasit/sakthai-tool-calling-v1](https://huggingface.co/datasets/Nanthasit/sakthai-tool-calling-v1) | Tool-calling training data (SimpleToolCalling) |
| 🤖 [SakThai Agents](https://huggingface.co/Nanthasit) | All models in the House of Sak |
| 🏠 [SakThai Model Family](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02) | Complete model family collection |

---

<div align="center">
<sub>Built with ❤️ by Beer · Part of the <a href="https://huggingface.co/Nanthasit">House of Sak</a> — one family, one home.</sub>
</div>