Instructions to use pankajpandey-dev/gemma-4-e4b-hindi-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajpandey-dev/gemma-4-e4b-hindi-instruct with Transformers:

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

pipe = pipeline("text-generation", model="pankajpandey-dev/gemma-4-e4b-hindi-instruct")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("pankajpandey-dev/gemma-4-e4b-hindi-instruct")
model = AutoModelForMultimodalLM.from_pretrained("pankajpandey-dev/gemma-4-e4b-hindi-instruct", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pankajpandey-dev/gemma-4-e4b-hindi-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajpandey-dev/gemma-4-e4b-hindi-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajpandey-dev/gemma-4-e4b-hindi-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pankajpandey-dev/gemma-4-e4b-hindi-instruct

SGLang

How to use pankajpandey-dev/gemma-4-e4b-hindi-instruct 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 "pankajpandey-dev/gemma-4-e4b-hindi-instruct" \
    --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": "pankajpandey-dev/gemma-4-e4b-hindi-instruct",
		"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 "pankajpandey-dev/gemma-4-e4b-hindi-instruct" \
        --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": "pankajpandey-dev/gemma-4-e4b-hindi-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use pankajpandey-dev/gemma-4-e4b-hindi-instruct with Docker Model Runner:
```
docker model run hf.co/pankajpandey-dev/gemma-4-e4b-hindi-instruct
```

🇮🇳 Gemma-4-E4B-Hindi-Instruct (16-bit)

A Hindi instruction-tuned fine-tune of Gemma 4 E4B. This is the merged 16-bit model for use with 🤗 Transformers / vLLM / further fine-tuning.

For local CPU/edge use, see the GGUF build.

Part of my Hindi LLM Series — small, openly-documented Indic models that actually follow instructions in Hindi and run on your own machine.

Usage (Transformers)

from transformers import AutoModelForCausalLM, AutoProcessor
import torch

model_id = "pankajpandey-dev/gemma-4-e4b-hindi-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
proc  = AutoProcessor.from_pretrained(model_id)

msgs = [{"role": "user", "content": [{"type": "text", "text": "मशीन लर्निंग को आसान शब्दों में समझाओ।"}]}]
inputs = proc.apply_chat_template(msgs, add_generation_prompt=True, tokenize=True,
                                  return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, use_cache=True)
print(proc.decode(out[0], skip_special_tokens=True))

Example outputs

Prompt: भारत के बारे में एक रोचक तथ्य बताओ।

भारत दुनिया में सबसे अधिक भाषाओं वाले देशों में से एक है — 22 आधिकारिक भाषाएँ और 1,000 से अधिक बोलियाँ। हिंदी एक इंडो-आर्यन भाषा है, जबकि तमिल एक द्रविड़ भाषा है।

Training details


Base model	`unsloth/gemma-4-E4B-it`
Method	LoRA (r=16, α=16), response-only loss
Framework	Unsloth
Data	~10k Hindi instruction pairs (AI4Bharat indic-instruct: anudesh + dolly, hi splits)
Epochs	2
LR / schedule	1e-4, cosine
Precision	bf16 (4-bit QLoRA base)
Hardware	Single NVIDIA L4 (24 GB)
Final train loss	~0.29

Trained text-only (vision layers frozen), single-BOS chat template to avoid double-BOS corruption.

Related repos

GGUF (Q4/Q5/Q8): pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF
LoRA adapter: pankajpandey-dev/gemma-4-e4b-hindi-instruct-lora

Provenance & license (please read)

Mixed-license lineage — review all before redistribution or commercial use:

Weights derive from Gemma 4, under the Gemma Terms of Use.
Data from AI4Bharat indic-instruct-data-v0.1:
- Dolly split — from databricks-dolly-15k, CC-BY-SA-3.0.
- Anudesh split — responses from Llama-2-70B, so the Llama 2 Community License applies.

Raw training data is not redistributed here. You are responsible for complying with the Gemma, Llama 2, and CC-BY-SA terms.

Limitations

~8B-class model: strong Hindi fluency, but can hallucinate facts and occasionally repeat phrasing on long open-ended generation.
Tuned for single-turn Hindi instructions; long multi-turn chat is not the focus.
Not safety-aligned for production.