Text Generation
Transformers
Safetensors
Bengali
English
bengali
gemma
fine-tuned
conversational-ai
multimodal
voice-synthesis
langchain
LoRA
4bit-quantization
conversational
Instructions to use retro56/gemma3-4b-bengali-multimodal-persona with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "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)" | |
| } | |
| } |