README.md

---
license: mit
datasets:
- HuggingFaceFW/fineweb-edu
language:
- en
library_name: transformers
---

30,142,848 trainable parameters.

Embedding parameters: 19,298,688

Non-embedding parameters: 10,844,160

Tokenizer: GPT-2

Vocabulary size: 50,257

Compute: single T4 GPU

Total train time: 2 hours and 40 minutes

Total train tokens: 136,000,000

Epochs: 2

Final train Loss: 2.9811

Final test Loss: 2.7963

_________________________________________

try the following script for inference:

!pip install huggingface_hub
!pip install transformers
!pip install torch
from transformers import GPT2Tokenizer, GPT2Config, GPT2LMHeadModel
from huggingface_hub import hf_hub_download
import torch

# Name
model_name = 'Mizule/Dense-30M'

# Authenticate
token = input("Enter your Hugging Face token: ")

# Download
model_file = hf_hub_download(repo_id=f"{model_name}", filename="Dense-30M.pth", use_auth_token=token)

# Custom config
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

config = GPT2Config(
    vocab_size=tokenizer.vocab_size,
    n_positions=512,
    n_ctx=512,
    n_embd=384,
    n_layer=6,
    n_head=8
)
# Load model
model = GPT2LMHeadModel(config)
model.load_state_dict(torch.load(model_file, map_location=torch.device('cpu')))
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()

# Inference settings
def generate_text(prompt, max_length=128, temperature=0.2, top_k=50, top_p=0.9):
    inputs = tokenizer(prompt, return_tensors="pt")
    inputs = {key: value.to("cuda" if torch.cuda.is_available() else "cpu") for key, value in inputs.items()}
    outputs = model.generate(
        **inputs,
        max_length=max_length,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        num_return_sequences=1,
        no_repeat_ngram_size=2,
        do_sample=True,
        early_stopping=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Interactive loop (it's an undertrained base model, don't expect it to chat)
while True:
    prompt = input("Prompt: ")
    if prompt.lower() == 'exit':
        break
    output = generate_text(prompt)
    print(f"Generated text: {output}")