The model can be fine-tuned for any downstream task that requires the use of the aforementioned Indian languages
PEFT LoRA is advised.
Can be stacked with an Encoder if needed for any Sequence to Sequence task that requires aforementioned Indian languages

Example of getting inference from the model

from transformers import AutoModel, AutoConfig, AutoModelForCausalLM, AutoTokenizer

# Path to the directory containing the model files
model_directory = "autopilot-ai/Indic-sentence-completion"
tokenizer = AutoTokenizer.from_pretrained(model_directory)
model = AutoModelForCausalLM.from_pretrained(
    model_directory,
    load_in_8bit=True,
    device_map="auto",
)

# Load the model configuration
config = AutoConfig.from_pretrained(model_directory)

# Load the model
model = AutoModel.from_pretrained(model_directory, config=config)
batch = tokenizer("હેલો કેમ છો?", return_tensors='pt')

with torch.cuda.amp.autocast():
   output_tokens = model.generate(**batch, max_new_tokens=10)

print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))

To run the above code snippet (in 8 bits), make sure to install the following

pip install accelerate bitsandbytes

RichardErkhov/autopilot-ai_-_Indic-sentence-completion-4bits

Original model description:

Indic-Sentence-Completion

license: other

Details

Architecture

Motivation behind the model fine-tuning

Example of getting inference from the model

To run the above code snippet (in 8 bits), make sure to install the following

RichardErkhov/
autopilot-ai_-_Indic-sentence-completion-4bits