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eswardivi/llama2_telugu
llama2_telugu is a text generation model from eswardivi. Use it when you need the model to write or continue text. It is set up for transformers.
Welcome to the eswardivi/llama2telugu model page on Hugging Face. This model is a result of fine-tuning the powerful PosteriorAI/godavari-telugu-llama2-7B model on the ravithejads/telugualpacaft dataset to better serv…
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From the Hugging Face model README
Welcome to the eswardivi/llama2_telugu model page on Hugging Face. This model is a result of fine-tuning the powerful PosteriorAI/godavari-telugu-llama2-7B model on the ravithejads/telugu_alpaca_ft dataset to better serve the Telugu-speaking community.
PosteriorAI/godavari-telugu-llama2-7B, a state-of-the-art Telugu language model based on the LLaMA architecture, offering advanced natural language understanding and generation capabilities.ravithejads/telugu_alpaca_ft, a curated dataset specifically designed to fine-tune language models for Telugu language tasks .To use this model, you can leverage the Hugging Face API, SDK, or Transformers library. Below is a simple Python example using the Transformers library:
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="eswardivi/llama2_telugu",
device_map="auto",
model_kwargs={"load_in_8bit": True}
)
def create_prompt(instruction: str, input: str = "") -> str:
prompt = f"""
You are a helpful assistant.
### Instruction:
{instruction}
### Input:
{input}
### Response:
"""
return prompt
instruction = "Krindi samaacharam prakaram google news app eppudu release ayyindi?"
input = "Google News is a news aggregator service developed by Google. It presents a continuous flow of links to articles organized from thousands of publishers and magazines. Google News is available as an app on Android, iOS, and the Web. Google released a beta version in September 2002 and the official app in January 2006."
prompt = create_prompt(instruction, input)
print(prompt)
out = pipe(
prompt,
num_return_sequences=1,
max_new_tokens=1024,
temperature=0.7,
top_p=0.9,
do_sample=True
)
print(out[0]['generated_text'])
This model was fine-tuned using Axolotl.
Contributions to this model are welcome. Feel free to submit issues, feature requests, or pull requests via Huggingface.
For inquiries or collaborations, please contact the model maintainer at [email protected].