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

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---
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](https://huggingface.co/datasets/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

```python
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.

## Links

- **LoRA Adapter:** [sakthai-context-1.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools)
- **Training Dataset:** [sakthai-combined-v4](https://huggingface.co/datasets/Nanthasit/sakthai-combined-v4)
- **Profile:** [Nanthasit](https://huggingface.co/Nanthasit) | **GitHub:** [beer-sakthai](https://github.com/beer-sakthai)