SakThai Context 1.5B — Merged V2

Model Description

SakThai Context 1.5B Merged V2 is a merged full-weight checkpoint of Qwen2.5-1.5B-Instruct, fine-tuned for structured tool-calling and function-calling. It fills the mid-weight slot between the edge-focused 0.5B and the high-capability 7B. Use it when you need stronger argument filling and multi-turn tool behavior on a laptop/desktop with 4–8 GB RAM.

What makes it special:

• 🧠 1.5B parameters — stronger reasoning than 0.5B, smaller than 7B.
• 🗳️ Trained for structured <tool> / <tool_call> output.
• 📦 GGUF Q4_K_M + BF16 safetensors included.
• ✅ Benchmarks available: Bench v2 selection 34.9%, arguments 44.2%, strict 34.2%; internal smoke test 100% multi-tool correctness / valid JSON.
• 🔁 Rebuilt merged weights, ready for CPU inference with llama.cpp or transformers.

Requirements

Use these tested versions to avoid inference issues on CPU/edge hardware:

torch>=2.2
transformers>=4.45
sentencepiece>=0.2
protobuf>=3.20
accelerate>=0.27

For GGUF inference:

llama-cpp-python>=0.2.80

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Nanthasit/sakthai-context-1.5b-merged-v2"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
  {"role": "system", "content": "<tools>\n[{\"name\": \"get_weather\", \"description\": \"Get current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\"}}}}]\n</tools>"},
  {"role": "user", "content": "What's the weather in Bangkok?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Ollama

The model is not published to the Ollama library, so import the local GGUF instead:

ollama create sakthai:1.5b-v2 -f Modelfile

Modelfile:

FROM ./sakthai-1.5b-q4_k_m.gguf

GGUF (llama.cpp)

# Option A — huggingface-cli
huggingface-cli download Nanthasit/sakthai-context-1.5b-merged-v2 --include "*.gguf" --local-dir ./
# Option B — direct wget
wget https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged-v2/resolve/main/sakthai-1.5b-q4_k_m.gguf

llama-cli -m sakthai-1.5b-q4_k_m.gguf \
  --prompt "<|system|>You are SakThai-Agent.<|user|>What's the weather in Bangkok?<|assistant|>" -n 256

Hugging Face Inference Providers (free-tier available)

Use the hosted Inference API with huggingface_hub.InferenceClient:

from huggingface_hub import InferenceClient

client = InferenceClient(model="Nanthasit/sakthai-context-1.5b-merged-v2")
messages = [
  {"role": "system", "content": "<tools>\n[{\"name\": \"get_weather\", \"description\": \"Get current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\"}}}}]\n</tools>"},
  {"role": "user", "content": "What's the weather in Bangkok?"},
]
response = client.chat_completion(messages=messages, max_tokens=256)
print(response.choices[0].message.content)

Tool-Calling Format

The model is fine-tuned for tool calling and expects a <tools> XML block in the system prompt — the block is required: without it, the model may answer conversationally instead of emitting a tool call.

Verified output format

System: <tools>
[
  {"name": "get_weather", "description": "Get current weather", "parameters": {"type": "object", "properties": {"location": {"type": "string"}}}}
]
</tools>
User: What's the weather in Bangkok?
Assistant: <tool>get_weather</tool>

For calls with arguments, the bundled chat_template.jinja renders the standard Qwen <tool_call> JSON form:

<tool_call>
{"name": "get_weather", "arguments": {"location": "Bangkok"}}

Architecture

Training Details

Evaluation

Notes:

• Benchmarks are internal and should not be treated as independently verified.
• model-index uses verified: true only because the corresponding .eval_results/ files are present in the repo.

Deployment

CPU-only

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Nanthasit/sakthai-context-1.5b-merged-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32,
    device_map="cpu",
    low_cpu_mem_usage=True,
)
model.eval()
messages = [
  {"role": "system", "content": "<tools>\n[{\"name\": \"get_weather\", \"description\": \"Get current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\"}}}}]\n</tools>"},
  {"role": "user", "content": "What's the weather in Bangkok?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Edge / llama.cpp

huggingface-cli download Nanthasit/sakthai-context-1.5b-merged-v2 --include "*.gguf" --local-dir ./
llama-cli -m sakthai-1.5b-q4_k_m.gguf --prompt "<|system|>You are SakThai-Agent.<|user|>What's the weather in Bangkok?<|assistant|>" -n 256

1.5B vs 7B Tradeoffs

If you need stronger tool argument accuracy and more headroom, use the 7B Merged. If you need the smallest footprint, use the 0.5B Merged.

Limitations

• Smaller than 7B — still has reasoning limits compared to larger checkpoints.
• Benchmark numbers are internal — Bench v2 and smoke-test results are not independently verified.
• Requires <tools> XML block — without it, the model defaults to conversation mode.
• English-only behavior — untested in other languages for tool calls.
• Argument accuracy is lower than selection — Arguments Accuracy 44.2% indicates the model often picks the right tool but may misconstruct parameters.

SakThai Model Family

All 26 public models in the family, sorted by downloads (live counts, verified 2026-08-01):

Full collection

The House of Sak 🏠

This model is part of the House of Sak — an open-source AI ecosystem built from a shelter in Cork, Ireland, with $0 budget and no paid GPUs.

"We are one family — and becoming more." — Beer (beer-sakthai)

Support

• ⭐ Leave a like
• 🐛 Report issues on GitHub
• 🔄 Share with anyone building accessible AI on CPU/edge
• 🍴 Fork and experiment — Apache 2.0

Citation

@misc{sakthai-context-1.5b-merged-v2,
  title = {SakThai Context 1.5B -- Merged V2: Mid-Weight Tool-Calling Model},
  author = {Nanthasit and the House of Sak},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged-v2}},
  note = {Apache 2.0, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct}
}

License

Apache 2.0. Qwen2.5 base model per its original license.

Built from a shelter in Cork, Ireland. Built with love, tears, and zero budget — to the world.

Family downloads API-verified (2026-08-01T10:23Z).