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Model: Nanthasit/sakthai-context-0.5b-tools Source: Original Platform
2026-08-22 10:15:18 +08:00
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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- agent
+- conversational
+- ollama
+- transformers
+- small-language-model
+- slm
+- tool-use
+- qwen
+- qwen2.5
+- sakthai
+- house-of-sak
+- tool-calling
+- function-calling
+- merged
+- edge
+- lightweight
+- low-resource
+- raspberry-pi
+- on-device
+- benchmark
+- eval
+datasets:
+- Nanthasit/sakthai-combined-v7
+- Nanthasit/sakthai-bench-v2
+model-index:
+- name: sakthai-context-0.5b-tools
+  results:
+  - task:
+      type: text-generation
+      name: Tool-Calling
+    dataset:
+      name: SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
+      type: Nanthasit/sakthai-bench-v2
+    metrics:
+    - type: selection
+      value: 91.2
+      name: Selection Accuracy
+      verified: true
+      evidence: .eval_results/sakthai-bench-v2.yaml
+    - type: arguments
+      value: 45.7
+      name: Arguments Accuracy
+      verified: true
+      evidence: .eval_results/sakthai-bench-v2.yaml
+    - type: strict
+      value: 45.7
+      name: Strict Accuracy
+      verified: true
+      evidence: .eval_results/sakthai-bench-v2.yaml
+    - type: held-out
+      value: 87.8
+      name: Held-Out Tool Accuracy
+      verified: true
+      evidence: .eval_results/sakthai-bench-v2.yaml
+    - type: degenerate
+      value: 0
+      name: Degenerate Outputs
+      verified: true
+      evidence: .eval_results/sakthai-bench-v2.yaml
+inference:
+  parameters:
+    temperature: 0.01
+    max_new_tokens: 256
+    top_p: 0.9
+widget:
+- text: What is the weather in Tokyo?
+  example_title: Tool-calling
+- text: Who wrote Romeo and Juliet?
+  example_title: Direct answer
+- text: Search the web for latest AI news
+  example_title: Search tool
+---
+
+<p align="center">
+  <img src="https://img.shields.io/badge/dynamic/json?url=https%3A//huggingface.co/api/models/Nanthasit/sakthai-context-0.5b-tools&query=%24.downloads&label=downloads&color=blue&cacheSeconds=3600" alt="Downloads"/>
+  <img src="https://img.shields.io/badge/size-~1.0GB-blue" alt="Size"/>
+  <img src="https://img.shields.io/badge/pipeline-tool--calling-orange" alt="Pipeline"/>
+  <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License"/>
+  <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/%F0%9F%8F%A0-SakThai%20Family-6644cc" alt="Collection"/></a>
+  <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/%F0%9A%80-Explore%20Family-47d147" alt="Family"/></a>
+</p>
+
+<h1 align="center">SakThai Context 0.5B Tools</h1>
+<p align="center"><em>Ultra-light tool-calling agent · Qwen2.5-0.5B fine-tune · runs in ~1 GB RAM</em></p>
+
+**SakThai Context 0.5B Tools** is a prompt-masked SFT of [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) optimized for browser/tool calling. It achieves **91.2% selection accuracy** on SakThai Bench v2, with **0% degenerate outputs** in multi-trial evaluation.
+
+## Model Description
+
+**SakThai Context 0.5B Tools** is a prompt-masked supervised fine-tune of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) focused on reliable tool/function calling in conversational agents. The model is trained to select the correct tool, generate valid JSON-style arguments, and avoid degenerate outputs. It is optimized for edge deployment and can run on consumer hardware with ~1 GB RAM.
+
+Key points:
+- Base: `Qwen/Qwen2.5-0.5B-Instruct`
+- Training: prompt-masked SFT on tool-calling traces from `Nanthasit/sakthai-combined-v7`
+- Primary use: lightweight agents, on-device assistants, Raspberry Pi / edge deployments
+- License: Apache-2.0
+
+## Quick Start — Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Nanthasit/sakthai-context-0.5b-tools"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get current weather",
+            "parameters": {
+                "type": "object",
+                "properties": {"location": {"type": "string"}},
+                "required": ["location"],
+            },
+        }
+    }
+]
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What's the weather in Tokyo?"},
+]
+
+text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.01, top_p=0.9)
+response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+## Quick Start — llama.cpp / Ollama
+
+```bash
+# Convert with llama.cpp and run locally
+llama-quantize ./sakthai-context-0.5b-tools-f16.gguf ./model-q4_k_m.gguf Q4_K_M
+ollama create sakthai-context-0.5b-tools -f Modelfile
+ollama run sakthai-context-0.5b-tools
+```
+
+### Usage notes
+- For tool calling, always use `apply_chat_template(..., tools=tools, tokenize=False, add_generation_prompt=True)` so the model receives the proper `<tools>` block.
+- If you want stricter outputs, reduce `temperature` further, e.g. `0.0`.
+- For CPU-only inference, set `device_map="cpu"`; GPU/MPS/CPU auto-detection works with `device_map="auto"`.
+
+## Architecture & Config
+
+| Field | Value |
+|------:|-------|
+| Architecture | `Qwen2ForCausalLM` |
+| Model type | `qwen2` |
+| Vocab size | `151936` |
+| Hidden size | `896` |
+| Layers | `24` |
+| Attention heads | `14` |
+| KV heads | `2` |
+| Intermediate size | `4864` |
+| Activation | `silu` |
+| Max position | `32768` |
+| Transformers | `5.14.1` |
+
+## Benchmarks
+
+| Metric | Value | Verified |
+|------:|------:|:--------|
+| Selection Accuracy | 91.2% | true |
+| Arguments Accuracy | 45.7% | true |
+| Strict Accuracy | 45.7% | true |
+| Held-Out Tool Accuracy | 87.8% | true |
+| Degenerate Outputs | 0% | true |
+
+Evidence: `.eval_results/sakthai-bench-v2.yaml` in repo.
+
+## Limitations
+
+- 0.5B parameter scale limits reasoning depth; arguments accuracy is lower than selection accuracy.
+- Tool schema adherence degrades on nested arguments and long context traces.
+- Current weights are merged; if you need the unmerged adapter, use `Nanthasit/sakthai-context-0.5b-tools-sft` or `Nanthasit/sakthai-context-0.5b-tools-sft-v2`.
+
+## Citation
+
+If you use this model, please cite the SakThai model family and benchmark:
+
+```bibtex
+@misc{sakthai2025context05btools,
+  title  = {SakThai Context 0.5B Tools},
+  author = {Nanthasit},
+  year   = {2026},
+  url    = {https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools}
+}
+```
+
+## SakThai Family
+
+| Repo | Downloads | Size | Pipeline |
+|-----:|----------:|-----:|---------|
+| [Nanthasit/sakthai-context-1.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged) | 1855 | ~4.07 GB | text-generation |
+| [Nanthasit/sakthai-context-0.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged) | 1692 | ~1.39 GB | text-generation |
+| [Nanthasit/sakthai-context-7b-merged](https://huggingface.co/Nanthasit/sakthai-context-7b-merged) | 1024 | ~15.23 GB | text-generation |
+| [Nanthasit/sakthai-embedding-multilingual](https://huggingface.co/Nanthasit/sakthai-embedding-multilingual) | 627 | ~471 MB | sentence-similarity |
+| [Nanthasit/sakthai-context-7b-128k](https://huggingface.co/Nanthasit/sakthai-context-7b-128k) | 610 | — | text-generation |
+| [Nanthasit/sakthai-context-7b-tools](https://huggingface.co/Nanthasit/sakthai-context-7b-tools) | 489 | ~20 MB | text-generation |
+| [Nanthasit/sakthai-context-1.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools) | 477 | ~8.7 MB | text-generation |
+| [Nanthasit/sakthai-context-1.5b-merged-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged-v2) | 337 | ~3.09 GB | text-generation |
+| [Nanthasit/sakthai-vision-7b](https://huggingface.co/Nanthasit/sakthai-vision-7b) | 315 | ~4.71 GB | image-text-to-text |
+| [Nanthasit/sakthai-plus-1.5b-lora](https://huggingface.co/Nanthasit/sakthai-plus-1.5b-lora) | 306 | ~74 MB | text-generation |
+| [Nanthasit/sakthai-context-0.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools) | 251 | ~1.0 GB | text-generation |
+| [Nanthasit/sakthai-tts-model](https://huggingface.co/Nanthasit/sakthai-tts-model) | 248 | ~143 MB | text-to-speech |
+| [Nanthasit/sakthai-plus-1.5b](https://huggingface.co/Nanthasit/sakthai-plus-1.5b) | 244 | ~3.09 GB | text-generation |
+| [Nanthasit/sakthai-context-1.5b-tools-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools-v2) | 173 | ~74 MB | text-generation |
+| [Nanthasit/sakthai-coder-1.5b](https://huggingface.co/Nanthasit/sakthai-coder-1.5b) | 151 | ~1.12 GB | text-generation |
+| [Nanthasit/sakthai-coder-browser](https://huggingface.co/Nanthasit/sakthai-coder-browser) | 54 | ~3.09 GB | text-generation |
+| [Nanthasit/sakthai-coder-browser-gguf](https://huggingface.co/Nanthasit/sakthai-coder-browser-gguf) | 35 | ~7.11 GB | text-generation |
+| [Nanthasit/sakthai-embedding](https://huggingface.co/Nanthasit/sakthai-embedding) | 23 | ~110 MB | sentence-similarity |
+| [Nanthasit/sakthai-coder-browser-lora](https://huggingface.co/Nanthasit/sakthai-coder-browser-lora) | 21 | ~74 MB | text-generation |
+
+Download counts and sizes were verified from the Hub API at upload time.