Instructions to use Nanthasit/sakthai-context-0.5b-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-context-0.5b-tools with Transformers:

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

pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-0.5b-tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-0.5b-tools")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-0.5b-tools", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanthasit/sakthai-context-0.5b-tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sakthai-context-0.5b-tools"
# 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-0.5b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-0.5b-tools

SGLang

How to use Nanthasit/sakthai-context-0.5b-tools 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-0.5b-tools" \
    --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-0.5b-tools",
		"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-0.5b-tools" \
        --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-0.5b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanthasit/sakthai-context-0.5b-tools with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-context-0.5b-tools
```

Nanthasit commited on Jul 31

Commit

8be8045

verified ·

1 Parent(s): 1d03e16

Fix broken GGUF cross-link: sakthai-context-0.5b-merged-q4_k_m.gguf → sakthai-0.5b-Q4_K_M.gguf (actual file in merged repo)

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -242,10 +242,10 @@ For CPU-only environments, use the [merged GGUF variant](https://huggingface.co/
 ```bash
 # Download the Q4_K_M GGUF
-wget https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged/resolve/main/sakthai-context-0.5b-merged-q4_k_m.gguf
 # Run with llama.cpp
-./llama-cli -m sakthai-context-0.5b-merged-q4_k_m.gguf \
   --temp 0.01 \
   --ctx-size 32768 \
   -p "What's the weather in Tokyo?"

 ```bash
 # Download the Q4_K_M GGUF
+wget https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged/resolve/main/sakthai-0.5b-Q4_K_M.gguf
 # Run with llama.cpp
+./llama-cli -m sakthai-0.5b-Q4_K_M.gguf \
   --temp 0.01 \
   --ctx-size 32768 \
   -p "What's the weather in Tokyo?"