sakthai-plus-1.5b

Benchmark Results

Benchmark: sakthai-bench-v2 · 500 samples · run 2026-08-01

Overall (strict): 39.65 · Selection: 39.65 · Arguments: 61.66

Training Data

Benchmarks

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Nanthasit/sakthai-plus-1.5b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "system", "content": "You are a helpful assistant with tool-calling capabilities."},
    {"role": "user", "content": "What's the weather in Bangkok?"}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.3, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Notes

• 3/3 verified tool-calling score measured with llama.cpp q4_k_m.
• Tool selection is strong, but argument accuracy needs refinement.
• Unverified scores are single-trial; multi-trial replication is planned.
• Trained on free T4 credits; no paid compute was used.

SakThai Family

This README is part of the SakThai Plus 1.5B model card. The family links table is preserved to keep cross-repo navigation intact.

Sibling rows are maintained for reference and kept in sync with live HF download counts during card audits.

Model Description

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.

Key traits:

• Strong tool selection and reliable JSON argument formatting in verified tests.
• Small 1.5B parameter size enables fast inference on CPUs and consumer GPUs.
• Trained with zero paid compute on free-tier T4 credits.

How to Use

transformers chat template

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Nanthasit/sakthai-plus-1.5b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)

messages = [
    {"role": "system", "content": "You are a helpful assistant with tool-calling capabilities. Use the available tools when asked."},
    {"role": "user", "content": "Send an email to Beer with the subject 'Status update' and body 'The model is running well.'"}
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.3, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

llama.cpp CLI

This model also ships as GGUF in the SakThai family. Example inference with the GGUF build:

llama-cli -m sakthai-plus-1.5b.Q4_K_M.gguf \
  -p "[INST] Send an email to Beer with the subject 'Status update' and body 'The model is running well.' [/INST]" \
  --temp 0.3 -n 256 --top-p 0.9

Benchmarks

Verified scores are reproducible across runs. Unverified rows should be treated as indicative until multi-trial replication is completed.

Limitations

• Argument accuracy lags behind tool selection; complex nested parameters can still fail.
• Unverified benchmarks are single-trial and may not reflect steady-state performance.
• Strongest with short- to medium-length tool definitions; very large schemas may degrade accuracy.
• Outputs should be parsed with a JSON-tolerant decoder because formatting can drift on low temperatures.

Citation

@misc{sakthai-plus-1.5b,
  title  = {SakThai Plus 1.5B},
  author = {Nanthasit},
  year   = {2026},
  url    = {https://huggingface.co/Nanthasit/sakthai-plus-1.5b}
}