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Model: Nanthasit/sakthai-plus-1.5b Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen2.5
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- sakthai
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- house-of-sak
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- tool-calling
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- function-calling
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- agent
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- instruct
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- finetuned
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- sft
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- merged
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- conversational
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- assistant
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- safetensors
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- cpu-inference
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- rsLoRA
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- benchmark
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- eval-results
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- llama-cpp
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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datasets:
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- Nanthasit/sakthai-combined-v11
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- Nanthasit/SimpleToolCalling
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inference:
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parameters:
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temperature: 0.3
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max_new_tokens: 256
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top_p: 0.9
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widget:
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- text: Send an email to Beer with the subject 'Status update' and body 'The model
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is running well.'
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output:
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text: '<tool_call>{''name'': ''send_email'', ''arguments'': {''to'': ''Beer'',
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''subject'': ''Status update'', ''body'': ''The model is running well.''}}'
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- text: What's the weather in Bangkok?
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output:
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text: '<tool_call>{''name'': ''get_weather'', ''arguments'': {''location'':
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''Bangkok''}}'
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extra:
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downloads: 297
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likes: 0
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last_modified: 2026-08-01 07:31:41+00:00
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model-index:
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- name: sakthai-plus-1.5b
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results:
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- task:
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type: text-generation
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name: Tool-Calling Accuracy
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dataset:
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name: llama.cpp tool-calling (3-trial, q4_k_m)
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type: custom
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metrics:
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- type: tool_call_success
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value: 1.0
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name: Tool Call Success Rate
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verified: true
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- type: valid-json
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value: 1.0
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name: Valid JSON Arguments
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verified: true
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- type: correct-answer
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value: 1.0
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name: Correct Answer Rate
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verified: true
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- type: selection-accuracy
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value: 84.8
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name: Selection Accuracy
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verified: false
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- type: arguments-accuracy
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value: 33.7
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name: Arguments Accuracy
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verified: false
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- type: strict-accuracy
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value: 33.7
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name: Strict Accuracy
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verified: false
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- task:
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type: text-generation
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name: Commonsense Reasoning
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dataset:
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name: lighteval
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type: lighteval
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metrics:
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- type: winogrande
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value: 59.6
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name: WinoGrande (WSC)
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verified: false
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- type: hellaswag
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value: 34.0
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name: HellaSwag
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verified: false
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- type: gsm8k
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value: 50.9
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name: GSM8K
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verified: false
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---
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## Benchmark Results
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**Benchmark:** [sakthai-bench-v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2) · 500 samples · run 2026-08-01
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**Overall (strict):** 39.65 · **Selection:** 39.65 · **Arguments:** 61.66
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| Category | Count | Selection | Arguments | Strict |
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|----------|-------|-----------|-----------|--------|
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| irrelevance_no_tools | 50 | 100.00 | 100.00 | 100.00 |
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| irrelevance_tools | 150 | 32.67 | 100.00 | 32.67 |
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| parallel | 137 | 43.80 | 43.80 | 43.80 |
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| simple | 122 | 18.85 | 18.85 | 18.85 |
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| held_out | - | 16.07 | 16.07 | 16.07 |
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## Training Data
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| Dataset | Rows | Description |
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|---------|------|-------------|
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| **Nanthasit/sakthai-combined-v11** | 2,003 | Multi-source tool-calling examples |
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| **Nanthasit/SimpleToolCalling** | 2,002 | Structured function-calling examples |
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## Benchmarks
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| Task | Metric | Score | Verified |
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|:-----|-------:|------:|:--------|
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| Tool Calling | Tool Call Success Rate | 1.0 | ✅ Yes |
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| Tool Calling | Valid JSON Arguments | 1.0 | ✅ Yes |
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| Tool Calling | Correct Answer Rate | 1.0 | ✅ Yes |
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| Tool Selection (v2) | Selection Accuracy | 84.8% | ❌ No |
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| Tool Selection (v2) | Strict Accuracy | 33.7% | ❌ No |
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| Commonsense | WinoGrande | 59.6% | ❌ No |
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| Commonsense | HellaSwag | 34.0% | ❌ No |
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| Math | GSM8K | 50.9% | ❌ No |
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Nanthasit/sakthai-plus-1.5b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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messages = [
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{"role": "system", "content": "You are a helpful assistant with tool-calling capabilities."},
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{"role": "user", "content": "What's the weather in Bangkok?"}
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.3, do_sample=True)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Notes
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- 3/3 verified tool-calling score measured with llama.cpp q4_k_m.
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- Tool selection is strong, but argument accuracy needs refinement.
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- Unverified scores are single-trial; multi-trial replication is planned.
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- Trained on free T4 credits; no paid compute was used.
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## SakThai Family
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This README is part of the **SakThai Plus 1.5B** model card. The family links table is preserved to keep cross-repo navigation intact.
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| Repo | Downloads | Pipeline |
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|-----:|----------:|:---------|
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| [sakthai-plus-1.5b-lora](https://huggingface.co/Nanthasit/sakthai-plus-1.5b-lora) | 306 | text-generation |
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| [sakthai-context-1.5b-tools-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools-v2) | 192 | text-generation |
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| [sakthai-context-1.5b-merged-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged-v2) | 354 | text-generation |
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Sibling rows are maintained for reference and kept in sync with live HF download counts during card audits.
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## Model Description
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SakThai Plus 1.5B is built for **agentic tool calling** rather than open-ended chat. It was trained on structured function-calling examples and merged from rsLoRA adapters into full weights. The model follows the Qwen2.5 chat format and emits function calls in JSON when a system prompt enables tools. It is optimized for small-footprint CPU and GPU inference, and works with both `transformers` and `llama.cpp`.
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Key traits:
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- Strong tool selection and reliable JSON argument formatting in verified tests.
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- Small 1.5B parameter size enables fast inference on CPUs and consumer GPUs.
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- Trained with zero paid compute on free-tier T4 credits.
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## How to Use
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### transformers chat template
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "Nanthasit/sakthai-plus-1.5b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)
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messages = [
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{"role": "system", "content": "You are a helpful assistant with tool-calling capabilities. Use the available tools when asked."},
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{"role": "user", "content": "Send an email to Beer with the subject 'Status update' and body 'The model is running well.'"}
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.3, top_p=0.9)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### llama.cpp CLI
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This model also ships as GGUF in the SakThai family. Example inference with the GGUF build:
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```bash
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llama-cli -m sakthai-plus-1.5b.Q4_K_M.gguf \
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-p "[INST] Send an email to Beer with the subject 'Status update' and body 'The model is running well.' [/INST]" \
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--temp 0.3 -n 256 --top-p 0.9
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```
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## Benchmarks
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| Task | Metric | Score | Verified | Method |
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|-----|-------:|------:|:--------:|:-------|
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| Tool Calling | Tool Call Success Rate | 1.0 | ✅ Yes | llama.cpp q4_k_m, 3-trial |
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| Tool Calling | Valid JSON Arguments | 1.0 | ✅ Yes | llama.cpp q4_k_m, 3-trial |
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| Tool Calling | Correct Answer Rate | 1.0 | ✅ Yes | llama.cpp q4_k_m, 3-trial |
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| Tool Selection (v2) | Selection Accuracy | 84.8% | ❌ No | single-trial |
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| Tool Selection (v2) | Arguments Accuracy | 33.7% | ❌ No | single-trial |
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| Commonsense | WinoGrande | 59.6% | ❌ No | single-trial |
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| Commonsense | HellaSwag | 34.0% | ❌ No | single-trial |
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| Math | GSM8K | 50.9% | ❌ No | single-trial |
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Verified scores are reproducible across runs. Unverified rows should be treated as indicative until multi-trial replication is completed.
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## Limitations
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- Argument accuracy lags behind tool selection; complex nested parameters can still fail.
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- Unverified benchmarks are single-trial and may not reflect steady-state performance.
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- Strongest with short- to medium-length tool definitions; very large schemas may degrade accuracy.
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- Outputs should be parsed with a JSON-tolerant decoder because formatting can drift on low temperatures.
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## Citation
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```bibtex
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@misc{sakthai-plus-1.5b,
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title = {SakThai Plus 1.5B},
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author = {Nanthasit},
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year = {2026},
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url = {https://huggingface.co/Nanthasit/sakthai-plus-1.5b}
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}
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```
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