Instructions to use barha/granite-switch-4.0-350m-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use barha/granite-switch-4.0-350m-demo with Transformers:

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

pipe = pipeline("text-generation", model="barha/granite-switch-4.0-350m-demo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("barha/granite-switch-4.0-350m-demo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use barha/granite-switch-4.0-350m-demo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "barha/granite-switch-4.0-350m-demo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "barha/granite-switch-4.0-350m-demo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/barha/granite-switch-4.0-350m-demo

SGLang

How to use barha/granite-switch-4.0-350m-demo 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 "barha/granite-switch-4.0-350m-demo" \
    --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": "barha/granite-switch-4.0-350m-demo",
		"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 "barha/granite-switch-4.0-350m-demo" \
        --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": "barha/granite-switch-4.0-350m-demo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use barha/granite-switch-4.0-350m-demo with Docker Model Runner:
```
docker model run hf.co/barha/granite-switch-4.0-350m-demo
```

granite-switch-4.0-350m-demo / tokenizer_config.json

barha

granite-switch-4.0-350m 3-adapter demo: cti-technique-mapping + text-to-json + genai-attack-vector

1b04e1b verified 2 months ago

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	{
	"add_prefix_space": false,
	"backend": "tokenizers",
	"bos_token": "<\|end_of_text\|>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|end_of_text\|>",
	"extra_special_tokens": [
	"<\|cti-technique-mapping\|>",
	"<\|text-to-json\|>",
	"<\|genai-attack-vector\|>"
	],
	"is_local": true,
	"local_files_only": false,
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "<\|pad\|>",
	"padding_side": "left",
	"tokenizer_class": "TokenizersBackend",
	"unk_token": "<\|unk\|>"
	}