Siyam025/MODEL-3

gpt2-basic-small

Overview

gpt2-basic-small is a compact GPT-2 style causal language model optimized for lightweight text generation tasks. It is trained on a mixed web-text corpus with emphasis on coherent short-form paragraph generation and code-comment style synthesis.

Model Architecture

• Base architecture: GPT-2 (12-layer, 12-heads, 768 hidden)
• Causal language modeling head (softmax over vocabulary)
• Context window: 1024 tokens
• Tokenizer: GPT2 Byte-Pair Encoding (BPE)

Intended Use

• Generating short paragraphs, creative writing prompts, and boilerplate text.
• Assisting with drafting messages, small email templates, summaries, and story seeds.
• Prototyping language-generation features in applications where model size and latency matter.

Not intended for:

• Generating high-accuracy factual content or legal/medical advice without verification.
• Sensitive use-cases where unmoderated text could cause harm.

Limitations

• May produce plausible-sounding but incorrect or fabricated facts (hallucinations).
• Can reflect biases present in the training data.
• Output is not guaranteed to be safe; moderation is required for public deployment.
• Small model size limits long-range coherence compared to larger LMs.

Example Code

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your-username/gpt2-basic-small"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "In the near future, urban gardens will"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=80,
        num_beams=4,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=0.9,
        early_stopping=True,
        pad_token_id=tokenizer.pad_token_id
    )
generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated)