Instructions to use OdiaGenAI/odia_llama2_7B_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OdiaGenAI/odia_llama2_7B_base with Transformers:

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

pipe = pipeline("text-generation", model="OdiaGenAI/odia_llama2_7B_base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OdiaGenAI/odia_llama2_7B_base")
model = AutoModelForCausalLM.from_pretrained("OdiaGenAI/odia_llama2_7B_base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OdiaGenAI/odia_llama2_7B_base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OdiaGenAI/odia_llama2_7B_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/odia_llama2_7B_base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OdiaGenAI/odia_llama2_7B_base

SGLang

How to use OdiaGenAI/odia_llama2_7B_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/odia_llama2_7B_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/odia_llama2_7B_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/odia_llama2_7B_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/odia_llama2_7B_base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OdiaGenAI/odia_llama2_7B_base with Docker Model Runner:
```
docker model run hf.co/OdiaGenAI/odia_llama2_7B_base
```

Model Card for Model ID

Model description

odia_llama2_7B_base is based on Llama2-7b and finetuned with 180k Odia instruction set. The instruction set is translated data from open-source resources and a prepared domain knowledge instruction set, resulting in good Odia instruction understanding and response generation capabilities.

The code of Odia data generation and other detailed information can be found in our Github project repository: https://github.com/OdiaGenAI/GenerativeAI_and_LLM_Odia.

Instructions for running it can be found at https://github.com/OdiaGenAI/GenerativeAI_and_LLM_Odia.

Licensing Information

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation Information

If you find this helpful repository, please consider giving 👏 and citing:

@misc{OdiaGenAI_Odia_Llama2_7b_base,
  author = {Shantipriya Parida and Sambit Sekhar and Guneet Singh Kohli},
  title = {OdiaGenAI_Odia_Llama2_7b_base},
  year = {2023},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}

Contributors:

Guneet Singh Kohli
Sambit Shekhar
Shantipriya Parida

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