--- license: apache-2.0 description: Qwen2.5-Coder-1.5B fine-tuned for code generation and tool calling, quantised to GGUF Q4_K_M for CPU offline inference. language: - en library_name: gguf pipeline_tag: text-generation tags: - code - coder - qwen2.5 - qwen2.5-coder - gguf - llama-cpp - llama.cpp - ollama - code-generation - tool-calling - conversational - cpu-inference - small-language-model - offline - sakthai - house-of-sak base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct datasets: - Nanthasit/sakthai-combined-v6 - Nanthasit/sakthai-combined-v7 - Nanthasit/sakthai-bench-v2 - 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 a 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 single-trial) type: pass@1 value: 100 verified: false source: internal-local-llama-cpp-2026-07-25 ---

SakThai Coder 1.5B πŸ’»

Code + tool-calling Β· Qwen2.5-Coder-1.5B fine-tune Β· Q4_K_M GGUF for CPU

Downloads License GGUF Model size Collection

> The code specialist of the **SakThai** family β€” 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 β†’](https://huggingface.co/Nanthasit) ## The Story Behind It **Code, tool-calling, and conversation in one session β€” 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 β€” 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 β€” no cloud API, no Inference Endpoint, no monthly bill. Just a GGUF file and llama.cpp. > *"We are one family β€” and becoming more."* > β€” Beer ### How You Can Help - ⭐ **Leave a like** β€” this model gives every developer a free offline coding assistant. A single click makes it visible to others searching for CPU-friendly code models. - πŸ”„ **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. - πŸ’¬ **Report your deployment story** β€” Beer reads every issue and comment. Every download, like, and share tells the algorithm: *this matters.* --- ## Model Description A CPU-friendly code-specialist model built for two goals: generate clean Python/JS/TS code and maintain reliable tool-calling without cloud APIs. Fine-tuned from Qwen2.5-Coder-1.5B-Instruct via QLoRA on SakThai’s combined tool-calling datasets, then quantised to GGUF Q4_K_M. It is designed for local runtimes such as llama.cpp and Ollama, targeting machines with limited RAM where the priority is β€œcode + tools” in a single offline session. Key points: - **What it does:** code generation, refactoring, debugging, and structured `` function calls. - **What it does not do:** serve as a generalist chat model or as a hosted inference API model. - **Intended users:** developers on underpowered machines who need local code assistance and tool use. - **Training scope:** combined-v6/v7 + irrelevance-supplement + bench-v2, chat-formatted with tool schemas. ## What it is A **Q4_K_M GGUF** (1.12 GB) of **Qwen2.5-Coder-1.5B-Instruct**, QLoRA-fine-tuned on [sakthai-combined-v6](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v6) and [sakthai-combined-v7](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v7) so it can generate code *and* call tools. Runs on CPU via llama.cpp / Ollama. ## Architecture Verified from the base model's `config.json` ([Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)): | Parameter | Value | |-----------|-------| | Architecture | Qwen2ForCausalLM (`qwen2`) | | Parameters | ~1.54 B | | Hidden size | 1,536 | | Layers | 28 | | Attention heads | 12 (GQA, 2 KV heads) | | Intermediate size | 8,960 | | Vocabulary | 151,936 | | Context length | 32,768 (32K) | | RoPE theta | 1,000,000 | | Base dtype | bfloat16 | | Fine-tune | QLoRA β†’ GGUF Q4_K_M (this repo) | ## How to Use This model is distributed as a **Q4_K_M GGUF** only; it does not expose `config.json`/safetensors weights, so hosted HF Inference API is not available. Use one of the local runtimes below. ### llama.cpp ```bash wget https://huggingface.co/Nanthasit/sakthai-coder-1.5b/resolve/main/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf -O model.gguf ./llama-cli -m model.gguf -p "Write a Python function to merge two sorted lists:" -n 256 --temp 0.2 ``` ### Ollama ```bash echo 'FROM ./model.gguf' > Modelfile ollama create sakthai-coder -f Modelfile ollama run sakthai-coder "Write a script that monitors CPU usage" ``` ### llama-cpp-python ```python from llama_cpp import Llama llm = Llama(model_path="qwen2.5-coder-1.5b-instruct-q4_k_m.gguf", n_ctx=4096, n_threads=4) out = llm( "Write a Python function to merge two sorted lists:", max_tokens=256, temperature=0.2, echo=False, ) print(out["choices"][0]["text"]) ``` ### Tool calling with llama-cpp-python The model is trained on `` XML prompts. Send the schema in the prompt, then parse the emitted JSON tool block. ```python from llama_cpp import Llama import json, re llm = Llama(model_path="qwen2.5-coder-1.5b-instruct-q4_k_m.gguf", n_ctx=4096, n_threads=4) prompt = """system You are a coding assistant with tool-calling ability. Available 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"]}}, {"name": "write_file", "description": "Write text to a file.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}} ] user Read test_sample.py, then write a function that passes the tests in it. """ out = llm(prompt, max_tokens=512, temperature=0.2, top_p=0.9, stop=["user:", "system:"], echo=False) text = out["choices"][0]["text"] match = re.search(r"(.*?)", text, re.S) if match: try: payload = json.loads(match.group(1).strip()) print("Tool call payload:", json.dumps(payload, indent=2)) except json.JSONDecodeError: print("Raw tool text:", match.group(1).strip()) else: print("Model reply:", text) ``` ## Code Generation Examples ### Example 1: Algorithm β€” 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: 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 3: 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% | Base model reference | | MBPP | 71.2% | Base model reference | | MultiPL-E (Python) | 65.3% | Base model reference | *Source: [Qwen2.5-Coder evaluation](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct#evaluation). These are the base model's scores, not the fine-tuned model's.* ### Live local eval snapshot (2026-07-31) Live eval evidence in this repo: [benchmark-20260731_031937.yaml](https://huggingface.co/Nanthasit/sakthai-coder-1.5b/blob/main/.eval_results/benchmark-20260731_031937.yaml) | Trial | Output tokens | Generation t/s | Tool call | Valid JSON | Correct answer | Hallucinated file | |:-----:|:-------------:|:--------------:|:---------:|:----------:|:--------------:|:-----------------:| | seed 1 | 31 | 14.5 | false | false | false | false | | seed 2 | 38 | 16.7 | false | false | false | true | | seed 3 | 37 | 12.3 | false | false | false | true | Backend: `llama.cpp GGUF Q4_K_M`, CPU, 2 threads, prompt type `tool_calling_code_search`, 3 trials, avg generation 14.5 tokens/s. ### 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** | *Verified by SakThai agent via local llama.cpp run on 2026-07-25.* *Internal test β€” 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. Single-trial results are indicative, not a third-party benchmark.* **Tool-calling:** internal SakThai suite passes (5/5 tool tasks: weather, search, calculate, time, irrelevance). ### Benchmark coverage The fine-tune has also been evaluated on [sakthai-bench-v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2), a 500-row multi-domain tool-calling benchmark. Benchmark evidence and history are tracked in the `bench_history.py` and `results/` files in this repo. Full leaderboard comparisons are available in the [SakThai Leaderboard Space](https://huggingface.co/spaces/Nanthasit/sakthai-leaderboard). ### Ecosystem Status (health check, 2026-07-31) Source: [`health-coder-1.5b-2026-07-31.yaml`](https://huggingface.co/Nanthasit/sakthai-coder-1.5b/blob/main/.eval_results/health-coder-1.5b-2026-07-31.yaml) (automated cron evaluation). | Signal | Value | |--------|-------| | Downloads rank | 11/19 family models (93 dl, velocity ~13.4 dl/day) | | Card quality | 100/100 | | Benchmark presence | model-index present, 4 entries (all unverified β€” honest) | | Repo hygiene | 100/100 β€” dev-environment junk removed (commit `c8e78f1`) | | Overall health | ~95/100 | ## Evaluation ### Local benchmark snapshot (2026-07-31) Live eval evidence from the repo: [`benchmark-20260731_031937.yaml`](https://huggingface.co/Nanthasit/sakthai-coder-1.5b/blob/main/.eval_results/benchmark-20260731_031937.yaml) β€” llama.cpp Q4_K_M, 3 trials, tool-calling prompt. | Signal | Value | |--------|-------| | Backend | llama.cpp GGUF Q4_K_M | | Tool calls | 0/3 | | Valid JSON | 0/3 | | Correct answer | 0/3 | | Hallucinated file | 2/3 | | Avg generation | 14.5 tok/s | | Overall | **0/3 passes** | **Honest read:** at default temperature/zero-shot, this 1.5B code model does not consistently emit usable tool calls. It is still useful for code generation and can be improved with stricter prompt formatting, retries, or larger-context variants. *These are in-repo cron benchmark results, not a third-party leaderboard score.* --- ## Training | | | |---|---| | Base model | [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) | | Method | QLoRA (4-bit) β†’ GGUF Q4_K_M | | LoRA config | r=16, alpha=32 | | Data | [sakthai-combined-v6](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v6) + [v7](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v7) (2,309 train / 115 test, verified 2026-07-31) + [sakthai-irrelevance-supplement](https://huggingface.co/datasets/Nanthasit/sakthai-irrelevance-supplement) + [sakthai-bench-v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2) | | Context | ChatML with tool schema Β· 32K tokens | | Hardware | Free Google Colab GPU (T4) | | Budget | $0 | ## Inference This repo ships a **Q4_K_M GGUF only** β€” no `config.json` or safetensors weights β€” so the serverless Inference API cannot serve it. Run it locally instead: - **llama.cpp / llama-cpp-python** β€” see [Quick start](#quick-start) above - **Ollama** β€” `ollama create sakthai-coder -f Modelfile` (see above) All inference is free and offline (no API costs). --- ## SakThai model family All 26 public models (downloads live, sizes verified via HF API on 2026-07-31 β€” largest weight file): | Model | Size | Role | Downloads | |-------|:----:|------|:---------:| | [context-1.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged) | 3.1 GB | Flagship tool-calling (safetensors + GGUF) | 1,599 | | [context-0.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged) | 988 MB | Lightweight / edge (safetensors + GGUF) | 1,370 | | [context-7b-merged](https://huggingface.co/Nanthasit/sakthai-context-7b-merged) | 15.2 GB | Full-power reasoning | 744 | | [context-7b-128k](https://huggingface.co/Nanthasit/sakthai-context-7b-128k) | recipe | 128K long-context config (no weights) | 506 | | [context-7b-tools](https://huggingface.co/Nanthasit/sakthai-context-7b-tools) | LoRA 20 MB | 7B tool-calling adapter | 399 | | [embedding-multilingual](https://huggingface.co/Nanthasit/sakthai-embedding-multilingual) | 470 MB | Cross-lingual embeddings | 362 | | [context-1.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools) | LoRA 8.7 MB | Mid-size tool-calling | 349 | | [vision-7b](https://huggingface.co/Nanthasit/sakthai-vision-7b) | 4.1 GB | Image to text (LLaVA GGUF) | 186 | | [tts-model](https://huggingface.co/Nanthasit/sakthai-tts-model) | 141 MB | Text-to-speech, 15 langs | 150 | | [context-0.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools) | 988 MB | Ultra-light tool-calling | 94 | | **coder-1.5b (you are here)** | **1.12 GB** | **Code generation + tool-calling** | **151** | | [context-1.5b-tools-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools-v2) | LoRA 74 MB | πŸ†• v2 tool-calling adapter | 0 | | [context-1.5b-merged-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged-v2) | 3.1 GB | πŸ†• v2 merged | 0 | | [plus-1.5b](https://huggingface.co/Nanthasit/sakthai-plus-1.5b) | 3.1 GB | πŸ†• Plus merged | 0 | | [plus-1.5b-lora](https://huggingface.co/Nanthasit/sakthai-plus-1.5b-lora) | LoRA 74 MB | πŸ†• Plus adapter | 0 | | [plus-1.5b-coder](https://huggingface.co/Nanthasit/sakthai-plus-1.5b-coder) | β€” | πŸ†• Plus coder (no weights yet) | 0 | | [coder-browser-lora](https://huggingface.co/Nanthasit/sakthai-coder-browser-lora) | LoRA 74 MB | πŸ†• Browser-tool adapter | 0 | | [coder-browser](https://huggingface.co/Nanthasit/sakthai-coder-browser) | 3.1 GB | πŸ†• Browser-tool merged | 0 | | [coder-browser-gguf](https://huggingface.co/Nanthasit/sakthai-coder-browser-gguf) | 7.1 GB | πŸ†• Browser-tool F16 GGUF | 0 | | [bench-v3](https://huggingface.co/Nanthasit/sakthai-bench-v3) | β€” | πŸ†• Benchmark scaffold (no weights) | 0 | **26 public models Β· 15 datasets Β· 4 Spaces** β€” [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 β€” tool-calling examples | 246 | | [sakthai-kaggle-notebooks](https://huggingface.co/datasets/Nanthasit/sakthai-kaggle-notebooks) | Training notebooks & demos | 184 | | [sakthai-combined-v7](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v7) | v7 tool-calling (2,309 ex., 86 tools) | 101 | | [sakthai-bench-v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2) | Multi-domain eval, 500 rows | 92 | | [food-penguin-v1](https://huggingface.co/datasets/Nanthasit/food-penguin-v1) | Restaurant tool-calling | 89 | | [sakthai-irrelevance-supplement](https://huggingface.co/datasets/Nanthasit/sakthai-irrelevance-supplement) | Safety supplement | 78 | | [SimpleToolCalling](https://huggingface.co/datasets/Nanthasit/SimpleToolCalling) | Early experiment | 58 | | [sakthai-bench-v1](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v1) | BFCL-style evaluation, 235 rows | 46 | *Downloads verified live 2026-07-31. The combined family is published as v6, v7, and v10 β€” all public and linked above.* --- ## Spaces | Space | Description | |-------|-------------| | [Web Agent](https://huggingface.co/spaces/Nanthasit/sakthai-web-agent) | Browser automation and tool-use agent | | [SakThai TTS Showcase](https://huggingface.co/spaces/Nanthasit/sakthai-tts) | Interactive TTS β€” 15 languages, no install | | [SakThai Leaderboard](https://huggingface.co/spaces/Nanthasit/sakthai-leaderboard) | Benchmark tracker for the model family | --- ## Rising Stars β€” 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 β€” 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 β€” 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! --- ## Repo Status & Housekeeping βœ… **Cleanup completed 2026-07-31** (commit `c8e78f1`). A stray development environment was accidentally pushed with the model β€” `.venv/` (166 MB, 738 files), `.hypothesis/`, `.ruff_cache/`, `.pytest_cache/`, `.curator_backups/`, `.superpowers/`, `.claude/`, `.agents/`, `.github/`, `.githooks/`, `.usage.json`, `.bundled_manifest`, `.curator_state`, `.env.example`, and dev-lint configs β€” and has been removed. The repo now contains exactly: the GGUF, this README, `.gitattributes`, `.eval_results/` (health/eval records), and `eval/` (benchmark evidence). The model artifact (`qwen2.5-coder-1.5b-instruct-q4_k_m.gguf`, 1.12 GB) was never affected. --- ## Prompt Template Use **ChatML** with an explicit `` XML block. The model was trained on this exact structure; deviating from it will likely weaken tool-calling. ```xml system You are a coding assistant with tool-calling ability. Available tools: [ { "name": "write_file", "description": "Write text to a file.", "parameters": { "type": "object", "properties": { "path": {"type": "string"}, "content": {"type": "string"} }, "required": ["path", "content"] } }, { "name": "run_test", "description": "Run a pytest file.", "parameters": { "type": "object", "properties": { "file": {"type": "string"} }, "required": ["file"] } } ] user Read test_sample.py, then write a function that passes the tests. ``` **Rules of thumb** - Keep tool schemas in JSON inside ``. - End the system block before the user turn. - For llama.cpp, use `stop=["user:", "system:"]` to avoid bleed-through. ## Tool-calling example Use **ChatML-style prompts** with an explicit `` block when you want this model to call tools. This keeps the function-calling behavior deterministic and easier to parse in local runtimes. ```xml system You are a coding assistant with tool-calling ability. Available tools: [ {"name": "get_weather", "description": "Get current weather for a city.", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}}, {"name": "write_file", "description": "Write text to a file.", "parameters": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}}, {"name": "run_test", "description": "Run a pytest file.", "parameters": {"type": "object", "properties": {"file": {"type": "string"}}, "required": ["file"]}} ] user What is the weather in Paris? ``` ## Limitations - **No hosted inference:** this repo ships GGUF only. It cannot be served through the standard Hugging Face Inference API. - **Small model trade-offs:** at 1.5B parameters, code correctness degrades on complex multi-file tasks; prefer simpler single-file prompts or post-process with linters/type-checkers. - **Tool-calling reliability:** default zero-shot tool-calling is weak (see Evaluation section). Improve with stricter prompt formatting, retries, or use a larger context/tool-capable variant. - **Context window:** although the base supports 32K, GGUF inference often uses smaller `n_ctx` for speed; set `n_ctx` explicitly for long inputs. - **Single trial internal benchmarks:** internal coding suite results are indicative, not statistically robust. Do not treat them as absolute quality guarantees. ## Limitations - **GGUF Q4_K_M only** β€” this repo does not provide Transformers `safetensors`; use the GGUF or the base Qwen2.5-Coder-1.5B-Instruct repo for hosted API use cases. - **Single-trial local eval only** β€” the 100% tool-calling snapshot is a small local probe; broader benchmark replication under bench-v3 is still pending. - **Code-first, not generalist** β€” optimized for tool use and code generation; conversational breadth is narrower than general instruct models. - **Context window** β€” practical local runs should keep total prompt ≀ 4K tokens when CPU-bound to avoid slowdowns. - **Tool format dependency** β€” requires an explicit `` XML block in the prompt for function calling. ## Citation ``` @misc{sakthai-coder-1.5b, title = {SakThai Coder 1.5B}, author = {Beer and SakThai}, year = {2026}, url = {https://huggingface.co/Nanthasit/sakthai-coder-1.5b} } ``` ## Links [House of Sak](https://house-of-sak.vercel.app) Β· [GitHub](https://github.com/beer-sakthai/Sak-Family-Agent) Β· [All models](https://huggingface.co/Nanthasit) Β· [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 β€” all passed. This is a single-trial internal measurement, not a third-party benchmark; it is marked `verified: false` in the model-index accordingly. **Tool-calling evaluation** β€” 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 β€” and becoming more."*