Instructions to use Nanthasit/sakthai-context-1.5b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-context-1.5b-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-1.5b-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-1.5b-merged")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-1.5b-merged", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Nanthasit/sakthai-context-1.5b-merged with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

LM Studio
Jan

vLLM

How to use Nanthasit/sakthai-context-1.5b-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sakthai-context-1.5b-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-context-1.5b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

SGLang

How to use Nanthasit/sakthai-context-1.5b-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Nanthasit/sakthai-context-1.5b-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-context-1.5b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Nanthasit/sakthai-context-1.5b-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-context-1.5b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Nanthasit/sakthai-context-1.5b-merged with Ollama:
```
ollama run hf.co/Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
```

Unsloth Studio

How to use Nanthasit/sakthai-context-1.5b-merged with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Nanthasit/sakthai-context-1.5b-merged to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Nanthasit/sakthai-context-1.5b-merged to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nanthasit/sakthai-context-1.5b-merged to start chatting

How to use Nanthasit/sakthai-context-1.5b-merged with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Nanthasit/sakthai-context-1.5b-merged:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Nanthasit/sakthai-context-1.5b-merged with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Run Hermes

hermes

OpenClaw new

How to use Nanthasit/sakthai-context-1.5b-merged with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Nanthasit/sakthai-context-1.5b-merged:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Nanthasit/sakthai-context-1.5b-merged with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-context-1.5b-merged:Q4_K_M
```

Lemonade

How to use Nanthasit/sakthai-context-1.5b-merged with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nanthasit/sakthai-context-1.5b-merged:Q4_K_M

Run and chat with the model

lemonade run user.sakthai-context-1.5b-merged-Q4_K_M

List all available models

lemonade list

Atomic Chat

sakthai-context-1.5b-merged / README.md

Nanthasit

SakSit: restore full model card with tech detail + HoS branding

0bc776e verified 13 days ago

3.68 kB

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - qwen2
  - sakthai
  - house-of-sak
  - tool-calling
  - instruct
  - lora
  - agent
  - function-calling
datasets:
  - Nanthasit/sakthai-combined-v4
base_model: Qwen/Qwen2.5-1.5B-Instruct
model-index:
  - name: sakthai-context-1.5b-merged
    results:
      - task:
          type: text-generation
        dataset:
          name: SakThai Eval Suite
          type: Nanthasit/sakthai-combined-v4
        metrics:
          - type: pass_rate
            value: 100
            name: Overall (45/45)
          - type: pass_rate
            value: 100
            name: Basic (6/6)
          - type: pass_rate
            value: 100
            name: Multi-Turn (9/9)
          - type: pass_rate
            value: 100
            name: Instruction Following (6/6)
          - type: pass_rate
            value: 100
            name: Tool Calling (6/6)
          - type: pass_rate
            value: 100
            name: Reasoning (6/6)
          - type: pass_rate
            value: 100
            name: Format Adherence (12/12)

SakThai Context 1.5B

Part of the House of Sak — 6 AI agents, one shared mind. Built from a shelter in Cork, Ireland.

Fine-tuned from Qwen2.5-1.5B-Instruct on the SakThai combined dataset for tool-calling, multi-turn context, and instruction-following. Designed as the reasoning backbone for the SakThai agent. Most downloaded model at 802 pulls.

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-1.5B-Instruct
Architecture	Qwen2 (decoder-only transformer)
Hidden Size	1536
Layers	28
Attention Heads	12
Intermediate Size	8960
Vocab Size	151936
Fine-tuning Method	LoRA (r=16, alpha=32, dropout=0.1)
Target Modules	q_proj, k_proj, v_proj, o_proj
Training Steps	220
Training Duration	~39 minutes (4 epochs on 974 examples)
License	Apache 2.0

Training

Base model: Qwen/Qwen2.5-1.5B-Instruct
Dataset: Nanthasit/sakthai-combined-v4 — 974 training + 51 test examples covering 25 canonical tool schemas
Method: LoRA via PEFT (rank=16, alpha=32, dropout=0.1) on q/k/v/o projections
Optimizer: AdamW, linear schedule, 220 steps

Evaluation — 45/45 (100%)

Workbench Results (3 runs x 15 tests)

Category	Tests	Pass Rate
Basic	6	100%
Multi-Turn	9	100%
Instruction Following	6	100%
Tool Calling	6	100%
Reasoning	6	100%
Format Adherence	12	100%
Overall	45	100%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-1.5b-merged")
tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-1.5b-merged")

messages = [{"role": "user", "content": "What's the weather in Bangkok?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

GGUF Quantized Version

A 4-bit quantized GGUF version is available at gguf/sakthai-1.5b-Q4_K_M.gguf for efficient CPU inference.

Nanthasit
/

sakthai-context-1.5b-merged

SakThai Context 1.5B

Model Details

Training

Evaluation — 45/45 (100%)

Workbench Results (3 runs x 15 tests)

Usage

GGUF Quantized Version

Links