Instructions to use sam2ai/gemma3-12b-en-indic-mt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use sam2ai/gemma3-12b-en-indic-mt with PEFT:
```
Task type is invalid.
```

How to use sam2ai/gemma3-12b-en-indic-mt with Transformers:

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

pipe = pipeline("text-generation", model="sam2ai/gemma3-12b-en-indic-mt")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("sam2ai/gemma3-12b-en-indic-mt")
model = AutoModelForImageTextToText.from_pretrained("sam2ai/gemma3-12b-en-indic-mt")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sam2ai/gemma3-12b-en-indic-mt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sam2ai/gemma3-12b-en-indic-mt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sam2ai/gemma3-12b-en-indic-mt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sam2ai/gemma3-12b-en-indic-mt

SGLang

How to use sam2ai/gemma3-12b-en-indic-mt 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 "sam2ai/gemma3-12b-en-indic-mt" \
    --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": "sam2ai/gemma3-12b-en-indic-mt",
		"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 "sam2ai/gemma3-12b-en-indic-mt" \
        --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": "sam2ai/gemma3-12b-en-indic-mt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sam2ai/gemma3-12b-en-indic-mt with Docker Model Runner:
```
docker model run hf.co/sam2ai/gemma3-12b-en-indic-mt
```

See axolotl config

axolotl version: 0.12.2

base_model: google/gemma-3-12b-it

load_in_4bit: true

# gemma3 doesn't seem to play nice with ddp
ddp_find_unused_parameters: true

# huggingface repo
chat_template: gemma3
eot_tokens:
  - <end_of_turn>
datasets:
  - path: sam2ai/en-or-hi-ml-bn-translation
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    roles:
      assistant:
        - gpt
      user:
        - human


dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/gemma-3-12b-wat2025-qlora

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: true


lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|cross_attn|self_attn).(up|down|gate|q|k|v|o)_proj'

wandb_project: gemma3-en-indic-wat2025
wandb_entity:
wandb_watch:
wandb_name: gemma3-12b-qlora
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: true
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
logging_steps: 1
flash_attention: true
eager_attention:

warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
weight_decay: 0.0

# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

outputs/gemma-3-12b-wat2025-qlora

This model is a fine-tuned version of google/gemma-3-12b-it on the sam2ai/en-or-hi-ml-bn-translation dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 45
training_steps: 457

Training results

Framework versions

PEFT 0.17.0
Transformers 4.55.2
Pytorch 2.7.0+gitf717b2a
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: -

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for sam2ai/gemma3-12b-en-indic-mt

Base model

google/gemma-3-12b-pt

Finetuned

google/gemma-3-12b-it

Adapter

(358)

this model

Adapters

1 model

sam2ai
/

gemma3-12b-en-indic-mt

outputs/gemma-3-12b-wat2025-qlora

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for sam2ai/gemma3-12b-en-indic-mt

Dataset used to train sam2ai/gemma3-12b-en-indic-mt