Instructions to use OdiaGenAI/odiagenAI-bengali-base-model-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OdiaGenAI/odiagenAI-bengali-base-model-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OdiaGenAI/odiagenAI-bengali-base-model-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OdiaGenAI/odiagenAI-bengali-base-model-v1") model = AutoModelForCausalLM.from_pretrained("OdiaGenAI/odiagenAI-bengali-base-model-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OdiaGenAI/odiagenAI-bengali-base-model-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OdiaGenAI/odiagenAI-bengali-base-model-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OdiaGenAI/odiagenAI-bengali-base-model-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OdiaGenAI/odiagenAI-bengali-base-model-v1
- SGLang
How to use OdiaGenAI/odiagenAI-bengali-base-model-v1 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/odiagenAI-bengali-base-model-v1" \ --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/odiagenAI-bengali-base-model-v1", "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/odiagenAI-bengali-base-model-v1" \ --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/odiagenAI-bengali-base-model-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OdiaGenAI/odiagenAI-bengali-base-model-v1 with Docker Model Runner:
docker model run hf.co/OdiaGenAI/odiagenAI-bengali-base-model-v1
Model Card for Model ID
Model description
odiagenAI-bengali-base-model-v1 is based on Llama-7b and finetuned with 252k Bengali instruction set. The instruction set is translated data from open-source resources, resulting in good Bengali instruction understanding and response generation capabilities.
The code of Bengali data generation and other detailed information can be found in our Github project repository: https://github.com/OdiaGenAI/GenerativeAI_and_LLM_Odia.
Training hyper-parameters
| Parameter | Value |
|---|---|
| Batch size | 128 |
| Learning rate | 3e-4 |
| Epochs | 5 |
| Cutoff length | 256 |
| Weight_decay | 0.001 |
| Warmup_rate | 0.1 |
| LR_scheduler | linear |
| Lora r | 16 |
| Lora target modules | (q_proj, k_proj, v_proj, o_proj) |
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-Bengali-LLM,
author = {Shantipriya Parida and Sambit Sekhar and Guneet Singh Kohli and Arghyadeep Sen and Shashikanta Sahoo},
title = {Bengali Instruction-Tuning Model},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}
Contributions
- Shantipriya Parida
- Sambit Sekhar
- Guneet Singh Kohli
- Arghyadeep Sen
- Shashikanta Sahoo
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