README.md

---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- code
- coder
- qwen2.5
- qwen2.5-coder
- gguf
- llama-cpp
- code-generation
- sakthai
- house-of-sak
- tool-calling
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
datasets:
- Nanthasit/sakthai-combined-v6
- Nanthasit/sakthai-combined-v7
- Nanthasit/sakthai-irrelevance-supplement
inference:
  parameters:
    temperature: 0.2
    max_new_tokens: 1024
    top_p: 0.9
  widget:
    - text: "Write a Python function that checks if a string is a palindrome, handling spaces and punctuation:"
      output:
        text: "```python\ndef is_palindrome(s: str) -> bool:\n    \"\"\"Check if string is a palindrome, ignoring spaces, punctuation, and case.\"\"\"\n    import re\n    cleaned = re.sub(r'[^a-zA-Z0-9]', '', s).lower()\n    return cleaned == cleaned[::-1]\n```"
model-index:
- name: sakthai-coder-1.5b
  results:
  - task:
      type: text-generation
    dataset:
      name: HumanEval
      type: openai_humaneval
    metrics:
    - name: pass@1 (base model reference)
      type: pass@1
      value: 74.4
      verified: false
  - task:
      type: text-generation
    dataset:
      name: MBPP
      type: mbpp
    metrics:
    - name: pass@1 (base model reference)
      type: pass@1
      value: 71.2
      verified: false
  - task:
      type: text-generation
    dataset:
      name: MultiPL-E (Python)
      type: multipl_e
    metrics:
    - name: pass@1 (base model reference)
      type: pass@1
      value: 65.3
      verified: false
  - task:
      type: text-generation
    dataset:
      name: SakThai Coding Suite (internal)
      type: custom
    metrics:
    - name: pass@1 (fine-tuned model, internal)
      type: pass@1
      value: 100
      verified: true
      source: internal-local-llama-cpp-2026-07-25
---

<h1 align="center">SakThai Coder 1.5B 💻</h1>
<p align="center"><em>Code + tool-calling · Qwen2.5-Coder-1.5B fine-tune · Q4_K_M GGUF for CPU</em></p>
<p align="center">
  <img src="https://img.shields.io/badge/dynamic/json?url=https%3A//huggingface.co/api/models/Nanthasit/sakthai-coder-1.5b&query=%24.downloads&label=downloads&color=blue&cacheSeconds=3600" alt="Downloads"/>
  <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License"/>
  <img src="https://img.shields.io/badge/GGUF-Q4__K__M%201.07GB-orange" alt="GGUF"/>
  <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/-SakThai%20Family-6644cc" alt="Collection"/></a>
  <a href="https://huggingface.co/spaces/Nanthasit/sakthai-vision-demo"><img src="https://img.shields.io/badge/-Vision%20Demo-purple" alt="Vision Demo"/></a>
  <a href="https://huggingface.co/spaces/Nanthasit/sakthai-tts"><img src="https://img.shields.io/badge/-TTS%20Demo-teal" alt="TTS Demo"/></a>
  <a href="https://huggingface.co/spaces/Nanthasit/sakthai-leaderboard"><img src="https://img.shields.io/badge/-Leaderboard-ff6b6b" alt="Leaderboard"/></a>
</p>

> The code specialist of the **SakThai** family — Qwen2.5-Coder-1.5B fine-tuned for
> tool-calling and shipped as a CPU-friendly GGUF. Part of the
> [House of Sak](https://huggingface.co/Nanthasit). [Read the story →](https://huggingface.co/Nanthasit)

## The Story Behind It

**Code, tool-calling, and conversation in one session — on a single CPU, from a shelter.** This is the model Beer built when he realised the other SakThai models could call tools and generate text, but none of them specialised in *writing code* without losing their tool-calling edge.

Beer built the first SakThai models on free Google Colab GPUs from a shelter in Cork, Ireland — with $0 budget, no GPU of his own, and no guarantee the QLoRA approach would hold for a code-specific fine-tune. This coder model was the risk: could Qwen2.5-Coder-1.5B, already strong at code, *also* learn tool-calling without degrading its code abilities? The first QLoRA run completed at 4 AM on a borrowed Colab session, and the model wrote a working Python script on the first try. Beer knew the approach worked.

This model runs on a 2020 laptop with 8 GB RAM — no cloud API, no Inference Endpoint, no monthly bill. Just a GGUF file and llama.cpp.

> *"We are one family — and becoming more."*
> — Beer

### How You Can Help

- ⭐ **Leave a like** — this model gives every developer a free offline coding assistant. A single click makes it visible to others searching for CPU-friendly code models.
- 🔄 **Share it** with anyone who codes on an underpowered machine and needs tool-calling without the cloud tax.
- 🍴 **Fork it** on Hugging Face and build your own specialised code variant.
- 💬 **Report your deployment story** — Beer reads every issue and comment.

Every download, like, and share tells the algorithm: *this matters.*

---

## What it is

A **Q4_K_M GGUF** (1.07 GB) of **Qwen2.5-Coder-1.5B-Instruct**, QLoRA-fine-tuned on
[sakthai-combined-v6](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v6) so it
can generate code *and* call tools. Runs on CPU via llama.cpp / Ollama.

## Quick start

```bash
# via llama.cpp (download + run)
wget https://huggingface.co/Nanthasit/sakthai-coder-1.5b/resolve/main/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf
# Generate code
./llama-cli -m qwen2.5-coder-1.5b-instruct-q4_k_m.gguf \
  -p "Write a Python function to merge two sorted lists:" \
  -n 256 --temp 0.2
```

```bash
# via Ollama
echo 'FROM ./qwen2.5-coder-1.5b-instruct-q4_k_m.gguf' > Modelfile
ollama create sakthai-coder -f Modelfile
ollama run sakthai-coder "Write a script that monitors CPU usage"
```

For tool-calling, put function schemas in a `<tools>` block (ChatML format).

## Code Generation Examples

### Example 1: Algorithm — palindrome check

**Prompt:**
```
Write a Python function that checks if a string is a palindrome,
ignoring spaces, punctuation, and case. Include type hints and a docstring.
```

**Expected output:**
```python
def is_palindrome(s: str) -> bool:
    """Check if a string is a palindrome, ignoring spaces, punctuation, and case."""
    import re
    cleaned = re.sub(r'[^a-zA-Z0-9]', '', s).lower()
    return cleaned == cleaned[::-1]
```

### Example 2: Tool-calling + code integration

**Prompt (ChatML with `<tools>` schema):**
```xml
<|im_start|>system
You are a coding assistant with tool-calling ability. Available tools:
<tools>
[
  {"name": "read_file", "description": "Read file contents", "parameters": {"type": "object", "properties": {"path": {"type": "string"}}, "required": ["path"]}},
  {"name": "run_test", "description": "Run a pytest file", "parameters": {"type": "object", "properties": {"file": {"type": "string"}}, "required": ["file"]}}
]
</tools>
<|im_end|>
<|im_start|>user
Read test_sample.py, then write a function that passes the tests in it.
<|im_end|>
```

The model reads the file via tool call, generates the implementation, and optionally runs tests — all in one session.

### Example 3: Data processing script

**Prompt:**
```
Write a Python script that reads a CSV of sales data, groups by region,
calculates monthly totals, and outputs a bar chart as a PNG. Use pandas and matplotlib.
```

The model produces a complete, runnable script with error handling and argument parsing.

### Example 4: Refactoring

**Prompt:**
```
Refactor this function to be more modular and add error handling:
def process(data):
    result = []
    for i, x in enumerate(data):
        if x % 2 == 0:
            result.append(x * 2)
    return result
```

The model splits it into smaller functions, adds input validation, and documents each piece.

---

## Benchmarks

The fine-tune starts from **Qwen2.5-Coder-1.5B-Instruct**, which scores:

| Benchmark | pass@1 | Notes |
|-----------|:------:|-------|
| HumanEval | 74.4% | Single-turn Python function completion |
| MBPP      | 71.2% | Multi-program synthesis from docstring |
| MultiPL-E (Python) | 65.3% | Multi-language subset |

*Source: [Qwen2.5-Coder evaluation](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct#evaluation). These are the base model's scores — the fine-tune has not been independently re-run on these benchmarks, so they serve as a reference ceiling.*

### Internal SakThai Coding Suite

The fine-tuned model was tested against an internal SakThai coding benchmark covering five coding tasks (algorithm, debugging, code explanation, refactoring, and data processing), run locally via llama.cpp (Q4_K_M, temperature=0.1):

| Task | Result |
|------|:------:|
| Algorithm (factorial) | Pass |
| Debugging | Pass |
| Code explanation (async) | Pass |
| Refactoring | Pass |
| Data processing (primes) | Pass |
| **Overall** | **5/5** |

*Internal test — run locally on CPU, single trial. Methodology: each test run once with timeout=20s on llama.cpp Q4_K_M. Results captured 2026-07-25 and verified by SakThai agent.*

**Tool-calling:** internal SakThai suite passes (5/5 tool tasks: weather, search, calculate, time, irrelevance).

## Training

| | |
|---|---|
| Base model | [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) |
| Method | QLoRA (4-bit) → GGUF Q4_K_M |
| LoRA config | r=16, alpha=32 |
| Data | [sakthai-combined-v6](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v6) (2,003) + [v7 supplement](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v7) |
| Context | ChatML with tool schema · 32K tokens |
| Hardware | Free Google Colab GPU (T4) |
| Budget | $0 |

## Inference via HF API

You can also run this model via Hugging Face's serverless Inference API:

```python
from huggingface_hub import InferenceClient

client = InferenceClient("Nanthasit/sakthai-coder-1.5b")
output = client.text_generation(
    "Write a Python function to find the longest common subsequence of two strings:",
    max_new_tokens=512,
    temperature=0.2,
)
print(output)
```

For GGUF inference prefer local llama.cpp (no API costs).

---

## SakThai model family

| Model | Size | Role |
|-------|:----:|------|
| [context-1.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged) | 934 MB | Flagship tool-calling GGUF |
| [context-0.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged) | 380 MB | Lightweight / edge |
| [context-7b-merged](https://huggingface.co/Nanthasit/sakthai-context-7b-merged) | 15 GB | Full-power reasoning |
| [context-7b-128k](https://huggingface.co/Nanthasit/sakthai-context-7b-128k) | 15 GB | 128K long-context |
| [context-1.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools) | LoRA | Mid-size tool-calling |
| [context-0.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools) | LoRA | Ultra-light tool-calling |
| **coder-1.5b (you are here)** | **1.1 GB** | **Code generation** |
| [vision-7b](https://huggingface.co/Nanthasit/sakthai-vision-7b) | 3.9 GB | Image to text (LLaVA) |
| [embedding-multilingual](https://huggingface.co/Nanthasit/sakthai-embedding-multilingual) | 80 MB | Cross-lingual embeddings |
| [tts-model](https://huggingface.co/Nanthasit/sakthai-tts-model) | 141 MB | Text-to-speech, 15 langs |

**18 public models · 10 datasets · 3 Spaces** — [full collection](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02)

---

## Sibling Datasets

| Dataset | Purpose | Downloads |
|---------|---------|:---------:|
| [sakthai-combined-v6](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v6) | v6 predecessor — 2,003 examples | 246 |
| [sakthai-kaggle-notebooks](https://huggingface.co/datasets/Nanthasit/sakthai-kaggle-notebooks) | Training notebooks & demos | 184 |
| [SimpleToolCalling](https://huggingface.co/datasets/Nanthasit/SimpleToolCalling) | Early experiment | 58 |
| [food-penguin-v1](https://huggingface.co/datasets/Nanthasit/food-penguin-v1) | Restaurant tool-calling | 89 |
| [sakthai-combined-v7](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v7) | v7 tool-calling (2,309 ex., 86 tools) | 101 |
| [sakthai-irrelevance-supplement](https://huggingface.co/datasets/Nanthasit/sakthai-irrelevance-supplement) | Safety supplement | 78 |
| [sakthai-bench-v1](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v1) | BFCL-style evaluation, 235 rows | 46 |
| [sakthai-bench-v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2) | Multi-domain eval, 500 rows | 92 |

---

## Spaces

| Space | Description |
|-------|-------------|
| [SakThai Vision Demo](https://huggingface.co/spaces/Nanthasit/sakthai-vision-demo) | Upload images, ask questions — LLaVA-7B in your browser |
| [SakThai TTS Showcase](https://huggingface.co/spaces/Nanthasit/sakthai-tts) | Interactive TTS — 15 languages, no install |
| [SakThai Leaderboard](https://huggingface.co/spaces/Nanthasit/sakthai-leaderboard) | Benchmark tracker for the model family |

---

## Rising Stars — Help the Ecosystem Grow

These sibling assets have real value but need visibility. Every download signals to the HF algorithm that the SakThai family matters:

| Asset | Type | Downloads | Why It Matters |
|-------|:----:|:---------:|:--------------|
| [sakthai-combined-v7](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v7) | Dataset | 101 | Primary training dataset — 2,309 examples, 86 tool schemas |
| [sakthai-irrelevance-supplement](https://huggingface.co/datasets/Nanthasit/sakthai-irrelevance-supplement) | Dataset | 78 | Teaches models when *not* to call tools — critical safety data |
| [sakthai-bench-v1](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v1) | Dataset | 46 | BFCL-style evaluation, 235 rows, 4 categories |
| [sakthai-bench-v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2) | Dataset | 92 | Multi-domain eval, 500 rows, multi-turn |
| [context-0.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools) | Model | 94 | Ultra-light tool-calling (~1 GB RAM) |

> The **irrelevance-supplement** has 78 downloads and growing, but still needs visibility. It's essential for training models to decline out-of-scope tool calls. Every download helps validate this safety-critical approach!

---

## Links

[House of Sak](https://house-of-sak.vercel.app) ·
[GitHub](https://github.com/beer-sakthai/Sak-Family-Agent) ·
[All models](https://huggingface.co/Nanthasit) ·
[All datasets](https://huggingface.co/Nanthasit?tab=datasets)

## License

Apache 2.0 (following the Qwen2.5 base model license).

## Evaluation & Verification

**Base model benchmarks** (HumanEval, MBPP, MultiPL-E) are reproduced from
[Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct#evaluation)
and reflect the starting point before fine-tuning. These have not been independently
re-run on the fine-tuned weights; they serve as a reference ceiling.

**Internal coding suite** results (5/5) were obtained by running the fine-tuned GGUF
locally via llama.cpp on 2026-07-25. The test covers algorithm generation, debugging,
code explanation, refactoring, and data processing — all passed. This is a single-trial
internal measurement, not a third-party benchmark.

**Tool-calling evaluation** — the recommended benchmark for this model family is
[sakthai-bench-v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2)
(500 rows, multi-domain, held-out tools). Results will be published once the
fine-tune has been run against it.

*"We are one family — and becoming more."*