Instructions to use GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged with Transformers:

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

pipe = pipeline("text-generation", model="GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged")
model = AutoModelForCausalLM.from_pretrained("GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged

SGLang

How to use GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged 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 "GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged" \
    --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": "GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged",
		"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 "GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged" \
        --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": "GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged with Docker Model Runner:
```
docker model run hf.co/GenVRadmin/AryaBhatta-GemmaGenZ-Vikas-Merged
```

Model is finetuned from UltraChat version of Gemma-7b (CorticalStack/gemma-7b-ultrachat-sft) and is finetuned on 9 Indian languages (Hindi, Tamil, Punjabi, Bengali, Gujarati, Oriya, Telugu, Kannada, Malayalam) plus English.

The model is trained on close sourced GenZ_Vikas dataset, created entirely by university students ageing (18-22), hence the name GenZ. Which comprises of 5.5 million Hindi instruction sets and 0.5 million instruction sets in rest of the languages plus English.

The model was trained on single A100 for 9 days, 17 hours.

And is benchmarked on Indic LLM leaderboard:- https://huggingface.co/spaces/Cognitive-Lab/indic_llm_leaderboard Where it outperforms our previous models (GemmaOrca and GemmaUltra) on Hindi benchmarks. And also scores above Meta-llama-3 on all currenty available benchmarks (ARC, Hellaswag) in Hindi language.

Release notes:- https://www.linkedin.com/feed/update/urn:li:activity:7188399797291175936

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

F16