Instructions to use Navneeth017/Malayalam_gemma-4-E2B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Navneeth017/Malayalam_gemma-4-E2B-it with Transformers:

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

pipe = pipeline("text-generation", model="Navneeth017/Malayalam_gemma-4-E2B-it")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Navneeth017/Malayalam_gemma-4-E2B-it")
model = AutoModelForMultimodalLM.from_pretrained("Navneeth017/Malayalam_gemma-4-E2B-it")
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 Settings

vLLM

How to use Navneeth017/Malayalam_gemma-4-E2B-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Navneeth017/Malayalam_gemma-4-E2B-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Navneeth017/Malayalam_gemma-4-E2B-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Navneeth017/Malayalam_gemma-4-E2B-it

SGLang

How to use Navneeth017/Malayalam_gemma-4-E2B-it 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 "Navneeth017/Malayalam_gemma-4-E2B-it" \
    --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": "Navneeth017/Malayalam_gemma-4-E2B-it",
		"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 "Navneeth017/Malayalam_gemma-4-E2B-it" \
        --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": "Navneeth017/Malayalam_gemma-4-E2B-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Navneeth017/Malayalam_gemma-4-E2B-it with Docker Model Runner:
```
docker model run hf.co/Navneeth017/Malayalam_gemma-4-E2B-it
```

Malayalam_gemma-4-E2B-it

A Malayalam instruction-tuned model built by supervised fine-tuning of google/gemma-4-E2B-it. It is specialised for natural, fluent Malayalam conversation and instruction following.

Highlights

🗣️ Malayalam-first — tuned to respond in clear, idiomatic Malayalam.
🧩 Instruction-following — handles questions, explanations and short-form generation in a chat format.
⚙️ Drop-in — standard transformers text-generation model; use the chat template as usual.

Usage

This is a Gemma-4 model; load it with the multimodal class and use the chat template for text conversation.

from transformers import AutoModelForImageTextToText, AutoTokenizer
import torch

model_id = "Navneeth017/Malayalam_gemma-4-E2B-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
)

messages = [
    {"role": "system", "content": "നിങ്ങൾ സഹായകരമായ ഒരു മലയാളം സഹായിയാണ്."},
    {"role": "user", "content": "കേരളത്തിലെ ഓണം ആഘോഷത്തെക്കുറിച്ച് വിശദീകരിക്കൂ."},
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Training

Fine-tuned with QLoRA (4-bit NF4 base + LoRA adapters) and the adapters merged back into the base for a standalone model.

License

Apache 2.0, inherited from the base model google/gemma-4-E2B-it. See the Gemma 4 license terms.

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Safetensors

Model size

5B params

Tensor type

BF16

Model tree for Navneeth017/Malayalam_gemma-4-E2B-it

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

(211)

this model