Instructions to use retro56/gemma3-4b-bengali-multimodal-persona with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use retro56/gemma3-4b-bengali-multimodal-persona with Transformers:

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

pipe = pipeline("text-generation", model="retro56/gemma3-4b-bengali-multimodal-persona")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("retro56/gemma3-4b-bengali-multimodal-persona", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use retro56/gemma3-4b-bengali-multimodal-persona with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "retro56/gemma3-4b-bengali-multimodal-persona"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "retro56/gemma3-4b-bengali-multimodal-persona",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/retro56/gemma3-4b-bengali-multimodal-persona

SGLang

How to use retro56/gemma3-4b-bengali-multimodal-persona 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 "retro56/gemma3-4b-bengali-multimodal-persona" \
    --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": "retro56/gemma3-4b-bengali-multimodal-persona",
		"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 "retro56/gemma3-4b-bengali-multimodal-persona" \
        --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": "retro56/gemma3-4b-bengali-multimodal-persona",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use retro56/gemma3-4b-bengali-multimodal-persona with Docker Model Runner:
```
docker model run hf.co/retro56/gemma3-4b-bengali-multimodal-persona
```

retro56 commited on May 31, 2025

Commit

21350ea

verified ·

1 Parent(s): 44539d4

Add training information

Browse files

Files changed (1) hide show

training_info.json +13 -0

training_info.json ADDED Viewed

	@@ -0,0 +1,13 @@

+{
+  "training_completed": "2025-05-31T19:56:51.222432",
+  "model_name": "gemma-2-27b-bengali-multimodal-persona",
+  "base_model": "google/gemma-2-27b-it",
+  "training_epochs": 3,
+  "learning_rate": 0.0002,
+  "lora_rank": 16,
+  "dataset_size": 5004,
+  "performance_metrics": {
+    "final_loss": "Check training logs",
+    "bengali_accuracy": "High (qualitative assessment)"
+  }
+}