Instructions to use Nanthasit/sakthai-context-7b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-context-7b-merged with Transformers:

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

pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-7b-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-7b-merged")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-7b-merged")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanthasit/sakthai-context-7b-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-7b-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-7b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-7b-merged

SGLang

How to use Nanthasit/sakthai-context-7b-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-7b-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-7b-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-7b-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-7b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanthasit/sakthai-context-7b-merged with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-context-7b-merged
```

SakThai Context 7B — Merged Model

The best-performing model in the SakThai Context family. A full-parameter merged checkpoint of Qwen2.5-7B-Instruct with LoRA adapters fine-tuned for structured tool-calling and instruction following.

LoRA adapter: Nanthasit/sakthai-context-7b-tools
Training data: Nanthasit/sakthai-combined-v5

Model Details

Property	Value
Developed by	Nanthasit
Base model	Qwen/Qwen2.5-7B-Instruct
Parameters	7.6B
Architecture	Qwen2.5 decoder-only transformer
Precision	BF16
Fine-tuning method	LoRA → merged (rank=16, alpha=32, target=q/k/v/o/gate/up/down)
License	Apache 2.0

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Nanthasit/sakthai-context-7b-merged",
    torch_dtype="bfloat16",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-7b-merged")

messages = [{"role": "user", "content": "What's the weather like in Bangkok?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Tool Calling

The model supports structured tool-calling via Qwen2.5's tokenizer tool schema:

messages = [
    {"role": "system", "content": "You are a helpful assistant with access to tools."},
    {"role": "user", "content": "What's the weather in Bangkok?"}
]
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather",
            "parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}
        }
    }
]
text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)

Evaluation Results

Tested on a Tesla T4 (5.56 GB VRAM) via Hugging Face Jobs:

Category	Result
Basic Response	✅ 1/1
Context Recall	✅ 1/1
Factual Accuracy	✅ 1/1
Instruction Following	✅ 1/1
JSON Output	✅ 1/1
Multi-turn	✅ 1/1
Name Recognition	✅ 1/1
Tool Calling	✅ 1/1
Overall	✅ 8/8 (100%)

Model load time: 137s on T4
Full eval report: eval/workbench-7b-2026-07-07.json

Smaller Variants

Model	Size	Description
⭐ sakthai-context-1.5b-merged	1.5B	Balanced size/quality
⭐ sakthai-context-0.5b-merged	0.5B	Lightweight for edge/CPU

Files

File	Description
`model.safetensors`	Merged BF16 weights (13.8 GB)
`config.json`	Model configuration
`tokenizer.json` / `tokenizer_config.json`	Qwen2.5 tokenizer with chat template
`generation_config.json`	Default generation parameters
`eval/`	Workbench evaluation scripts and results