Dront-AI/Dront-200m

Dront 200m is a GPT-like model trained from scratch on approximately 5 billion tokens.

Context: 1024 tokens

Tokenizer: GPT2 50k

License: cc0-1.0

Perplexity

Model	openbmb/Ultra-FineWeb-L3	FineWeb-Edu-L4-5
Dront-200m	11.33	13.23
VDrontV2-0.1b	17.52	11.01
GPT-2 120m	10.53	8.54

Generate Example

Prompt: The Python is a...

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Easy Start ( ! Local !)

import torch
import sys
import warnings
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

warnings.filterwarnings('ignore')

torch.backends.cudnn.benchmark = True
torch.backends.cuda.matmul.allow_tf32 = True
torch.set_float32_matmul_precision('high')

MODEL_NAME = "Dront-200m" # LOCAL FOLDER
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, local_files_only=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    local_files_only=True,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)
model.eval()
print("Model loaded")


def generate_text_streaming(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(DEVICE)

    # Создаем стример для потокового вывода
    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    print("Output: ", end="", flush=True)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=1024,
            temperature=0.45,
            do_sample=True,
            top_p=0.95,
            repetition_penalty=1.1,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
            streamer=streamer,  # Добавляем стример
            use_cache=True
        )

    print()  # Новая строка после завершения генерации
    return outputs


print("Text generation started! Type 'exit' to quit.")
while True:
    try:
        user_input = input("Input: ").strip()
        if user_input.lower() in ['exit', 'quit', 'q']:
            print("Goodbye!")
            break
        if not user_input:
            continue

        generate_text_streaming(user_input)

    except KeyboardInterrupt:
        print("\nGoodbye!")
        break
    except Exception as e:
        print(f"Error: {e}")
        continue

Bye!