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 / 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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	{
	"adapter_names": [
	"cti-technique-mapping",
	"text-to-json",
	"genai-attack-vector"
	],
	"adapter_ranks": [
	16,
	16,
	16
	],
	"adapter_substitute_token_ids": [
	100264,
	100264,
	100264
	],
	"adapter_token_ids": [
	100352,
	100353,
	100354
	],
	"architectures": [
	"GraniteSwitchForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attention_multiplier": 0.015625,
	"bos_token_id": 100257,
	"control_token_gain": 15.0,
	"dtype": "bfloat16",
	"embedding_multiplier": 12,
	"eos_token_id": 100257,
	"fused_add_norm": false,
	"hidden_act": "silu",
	"hidden_size": 1024,
	"initializer_range": 0.1,
	"intermediate_size": 2048,
	"layer_types": [
	"attention",
	"attention",
	"attention",
	"attention",
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	"attention",
	"attention",
	"attention",
	"attention",
	"attention",
	"attention",
	"attention",
	"attention"
	],
	"logits_scaling": 4,
	"lora_target_modules": [
	"o_proj",
	"qkv_proj",
	"shared_input_linear",
	"shared_output_linear"
	],
	"mamba_chunk_size": 256,
	"mamba_conv_bias": true,
	"mamba_d_conv": 4,
	"mamba_d_head": 16,
	"mamba_d_state": 256,
	"mamba_expand": 2,
	"mamba_n_groups": 1,
	"mamba_n_heads": 128,
	"mamba_proj_bias": false,
	"max_lora_rank": 16,
	"max_position_embeddings": 32768,
	"model_type": "granite_switch",
	"num_adapters": 3,
	"num_attention_heads": 16,
	"num_experts_per_tok": 0,
	"num_hidden_layers": 29,
	"num_key_value_heads": 4,
	"num_local_experts": 0,
	"output_router_logits": false,
	"pad_token_id": 100256,
	"position_embedding_type": "rope",
	"projection_head_dim": 64,
	"residual_multiplier": 0.263,
	"rms_norm_eps": 1e-05,
	"rope_parameters": {
	"rope_theta": 10000000,
	"rope_type": "default"
	},
	"router_aux_loss_coef": 0.001,
	"shared_intermediate_size": 2048,
	"switch_head_dim": 32,
	"tie_word_embeddings": true,
	"time_step_limit": [
	0.0,
	{
	"__float__": "Infinity"
	}
	],
	"time_step_max": 0.1,
	"time_step_min": 0.001,
	"transformers_version": "5.9.0",
	"use_cache": true,
	"vocab_size": 100355
	}