Instructions to use pavan-naik/gemma-3-1b-it-exp-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pavan-naik/gemma-3-1b-it-exp-init with Transformers:

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

pipe = pipeline("text-generation", model="pavan-naik/gemma-3-1b-it-exp-init")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pavan-naik/gemma-3-1b-it-exp-init")
model = AutoModelForCausalLM.from_pretrained("pavan-naik/gemma-3-1b-it-exp-init")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pavan-naik/gemma-3-1b-it-exp-init with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pavan-naik/gemma-3-1b-it-exp-init"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pavan-naik/gemma-3-1b-it-exp-init",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pavan-naik/gemma-3-1b-it-exp-init

SGLang

How to use pavan-naik/gemma-3-1b-it-exp-init 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 "pavan-naik/gemma-3-1b-it-exp-init" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pavan-naik/gemma-3-1b-it-exp-init",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "pavan-naik/gemma-3-1b-it-exp-init" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pavan-naik/gemma-3-1b-it-exp-init",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pavan-naik/gemma-3-1b-it-exp-init with Docker Model Runner:
```
docker model run hf.co/pavan-naik/gemma-3-1b-it-exp-init
```

Cross-lingual Initialized Gemma 3 1B IT

A cross-lingual version of Google's Gemma 3 1B Instruction-Tuned model with extended vocabulary and initialized embeddings for multilingual support.

Model Details

Base Model: google/gemma-3-1b-it
Model Type: Causal Language Model with Cross-lingual Initialization
Initialization Method: Cross-lingual embedding initialization using English token mappings
Extended Vocabulary: Additional tokens for multilingual support

Description

This model extends the original Gemma 3 1B IT model with:

Extended tokenizer vocabulary for additional language support
Cross-lingual embedding initialization where new language tokens are initialized with embeddings from semantically equivalent English tokens
Preserved model architecture and instruction-tuning capabilities

⚠️ Important Note

This model is NOT pretrained after token extension and initialization. This is a base model with extended tokens and initialized embeddings only. The new language tokens require additional pretraining/fine-tuning to achieve optimal performance. This model serves as a starting point for multilingual adaptation rather than a ready-to-use multilingual model.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("pavan-naik/gemma-3-1b-it-exp-init")
tokenizer = AutoTokenizer.from_pretrained("pavan-naik/gemma-3-1b-it-exp-init")

# Use like any other Gemma model
inputs = tokenizer("Your multilingual text here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)

Technical Details

Initialization Strategy: New language tokens initialized with embeddings from mapped English equivalents
Preserved Components: Original model weights, architecture, and instruction-following capabilities
Extended Components: Input embeddings and output projection layer (LM head)

Intended Use

This model serves as a starting point for multilingual model development. It is designed for:

Further pretraining on multilingual corpora
Fine-tuning for specific multilingual tasks
Research into cross-lingual transfer learning

This model requires additional training before production use. The extended tokens have only been initialized but not trained on actual multilingual data.

Limitations

Requires additional training: New language tokens are only initialized, not trained on multilingual data
Not production-ready: This is a base model for further development, not a finished multilingual model
Performance: Extended tokens will have limited performance without additional pretraining/fine-tuning
Cross-lingual initialization: Provides a starting point but may not capture all linguistic nuances
Token mapping quality: Performance depends on the quality of English token mappings used during initialization