metadata

license: apache-2.0
language:
  - el
pipeline_tag: text-generation

Model Description

This is an instruction tuned model based on the gsar78/GreekLlama-1.1B-base model.

The dataset used has 52k instruction/response pairs, all in Greek language

Notice: The model is for experimental & research purposes.

Usage

To use you can just run the following in a Colab configured with a GPU:

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch


# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("gsar78/GreekLlama-1.1B-it")
model = AutoModelForCausalLM.from_pretrained("gsar78/GreekLlama-1.1B-it")


# Check if CUDA is available and move the model to GPU if possible
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

prompt = "Ποιά είναι τα δύο βασικά πράγματα που πρέπει να γνωρίζω για την Τεχνητή Νοημοσύνη:"

# Tokenize the input prompt
inputs = tokenizer(prompt, return_tensors="pt").to(device)

# Generate the output
generation_params = {
    #"max_new_tokens": 250,  # Adjust the number of tokens generated
    "do_sample": True,  # Enable sampling to diversify outputs
    "temperature": 0.1,  # Sampling temperature
    "top_p": 0.9,  # Nucleus sampling
    "num_return_sequences": 1,
}

output = model.generate(**inputs, **generation_params)

# Decode the generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print("Generated Text:")
print(generated_text)