Instructions to use OdiaGenAI-LLM/odia-gemma-2b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OdiaGenAI-LLM/odia-gemma-2b-base with Transformers:

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

pipe = pipeline("text-generation", model="OdiaGenAI-LLM/odia-gemma-2b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OdiaGenAI-LLM/odia-gemma-2b-base")
model = AutoModelForCausalLM.from_pretrained("OdiaGenAI-LLM/odia-gemma-2b-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OdiaGenAI-LLM/odia-gemma-2b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OdiaGenAI-LLM/odia-gemma-2b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OdiaGenAI-LLM/odia-gemma-2b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OdiaGenAI-LLM/odia-gemma-2b-base

SGLang

How to use OdiaGenAI-LLM/odia-gemma-2b-base 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 "OdiaGenAI-LLM/odia-gemma-2b-base" \
    --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": "OdiaGenAI-LLM/odia-gemma-2b-base",
		"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 "OdiaGenAI-LLM/odia-gemma-2b-base" \
        --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": "OdiaGenAI-LLM/odia-gemma-2b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OdiaGenAI-LLM/odia-gemma-2b-base with Docker Model Runner:
```
docker model run hf.co/OdiaGenAI-LLM/odia-gemma-2b-base
```

odia-gemma-2b-base / README.md

shantipriya

Update README.md

1b5cb64 verified about 2 years ago

preview code

raw

history blame contribute delete

1.59 kB

	---
	license: gemma
	language:
	- or
	- en
	---

	# odia-gemma-2b-base (Pre-trained)

	Odia-Gemma-2B-Base is a pre-trained Odia large language model with 2 billion parameters, and it is based on Google/Gemma 2B. The model is pre-trained on the Culturex-Odia dataset, a filtered version of the original CulturaX dataset for Odia text. The training dataset contains 49 million tokens. The CulturaX-Odia dataset is sourced from mc4 and four distinct OSCAR corpora.

	For more details about the model, data, training procedure, and evaluations, go through the blog [post]().

	## Model Description
	* Model type: A 2B pre-trained decoder-only model
	* Primary Language(s): Odia and English
	* License: Gemma Terms of Use

	NOTE

	This is not an instruction-tuned model, so it may not be able to follow human instructions without using one/few-shot learning or instruction fine-tuning. The model has no moderation mechanisms and may generate harmful or inappropriate responses.
	It is recommended to first fine-tune it on the task(s) you are interested in.


	### Citation Information

	If you find this model useful, please consider giving 👏 and citing:

	```
	@misc{odia-gemma-2b-base,
	author = {Sambit Sekhar and Shantipriya Parida and Debasish Dhal and Guneet Singh Kohli},
	title = {OdiaGenAI Introduces Gemma 2B Pre-Trained LLM Catered to Odia Speakers},
	year = {2024},
	publisher = {Hugging Face},
	journal = {Hugging Face repository},
	howpublished = {\url{https://huggingface.co/OdiaGenAI}},
	}
	```

	### Contributions

	- Sambit Sekhar
	- Shantipriya Parida
	- Debasish Dhal
	- Guneet Singh Kohli