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
```

barha commited on Jun 16

Commit

a6517e6

verified ·

1 Parent(s): 7f50733

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

Browse files

Files changed (4) hide show

BUILD.md +2 -2
compose_report.json +0 -0
config.json +3 -1
model.safetensors +2 -2

BUILD.md CHANGED Viewed

@@ -23,8 +23,8 @@ Total adapters: **3**
 ## Composition Details
 - base_param_count: 352,379,904
-- composed_param_count: 370,274,304
-- Param delta: +5.08%
 - compose_settings:
   - technology: "alora"
   - adapter_substitute_token_ids:

 ## Composition Details
 - base_param_count: 352,379,904
+- composed_param_count: 395,046,912
+- Param delta: +12.11%
 - compose_settings:
   - technology: "alora"
   - adapter_substitute_token_ids:

compose_report.json CHANGED Viewed

The diff for this file is too large to render. See raw diff

config.json CHANGED Viewed

@@ -69,7 +69,9 @@
   "logits_scaling": 4,
   "lora_target_modules": [
     "o_proj",
-    "qkv_proj"
   ],
   "mamba_chunk_size": 256,
   "mamba_conv_bias": true,

   "logits_scaling": 4,
   "lora_target_modules": [
     "o_proj",
+    "qkv_proj",
+    "shared_input_linear",
+    "shared_output_linear"
   ],
   "mamba_chunk_size": 256,
   "mamba_conv_bias": true,

model.safetensors CHANGED Viewed

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