Instructions to use PosteriorAI/godavari-telugu-llama2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PosteriorAI/godavari-telugu-llama2-7B with Transformers:

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

pipe = pipeline("text-generation", model="PosteriorAI/godavari-telugu-llama2-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PosteriorAI/godavari-telugu-llama2-7B")
model = AutoModelForCausalLM.from_pretrained("PosteriorAI/godavari-telugu-llama2-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PosteriorAI/godavari-telugu-llama2-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PosteriorAI/godavari-telugu-llama2-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PosteriorAI/godavari-telugu-llama2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PosteriorAI/godavari-telugu-llama2-7B

SGLang

How to use PosteriorAI/godavari-telugu-llama2-7B 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 "PosteriorAI/godavari-telugu-llama2-7B" \
    --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": "PosteriorAI/godavari-telugu-llama2-7B",
		"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 "PosteriorAI/godavari-telugu-llama2-7B" \
        --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": "PosteriorAI/godavari-telugu-llama2-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PosteriorAI/godavari-telugu-llama2-7B with Docker Model Runner:
```
docker model run hf.co/PosteriorAI/godavari-telugu-llama2-7B
```

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Model Details

Model Description

The Telugu Llama, codenamed #Godavari, is a state-of-the-art Large Language Model (LLM) fine-tuned for the Telugu language using Meta's Llama-2 as a foundation. Tailored to understand and engage with over 81 million Telugu-speaking individuals, it harnesses the nuances of a language rich in heritage and complexity to offer culturally sensitive responses and interpretations.

Developed by: PosteriorAI
Model type: Large Language Model (LLM), specifically fine-tuned Llama-2 model for Telugu
Language(s) (NLP): Telugu
License: Open-source releases on Hugging Face, MIT Licensed.
Finetuned from model [optional]: Llama-2

Model Sources [optional]

Repository: PosteriorAI/godavari-telugu-llama2-7B

Uses

This model is intended for various stakeholders, including researchers, developers, and the broader Telugu-speaking community. It aims to enhance communication, education, and technology access by providing an AI tool that understands and interacts in Telugu. The model's applications range from personal assistance to educational content creation and more. It addresses the gap in AI for Indic languages and is designed to promote inclusivity in technology.

Find mode details in our blog post at Unveiling Godavari: Telugu LLama

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