Model Card for gpt2_compact

gpt2_compact is a compact and efficient version of GPT‑2. It is reliable, simple, and powerful, designed for resource‑constrained environments while maintaining strong generative capabilities.

Model Details

• Base model: GPT‑2
• Format: Safetensors
• Library: PEFT (v0.20.0)
• Architecture: GPT‑2 with LoRA adapters, float16, partially frozen layers for efficiency
• Type: Causal language model
• Languages: English (primary, inherited from GPT‑2 training data)
• License: gpl-3.0
• Ownership: This model belongs to StrictAIUser
• Finetuned from: GPT‑2 base model

Model Description

The model integrates LoRA layers for fine‑tuning, uses quantization to reduce memory footprint, and freezes selected layers to improve inference speed. It is stored in the safetensors format for security and efficiency.

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Uses

Direct Use

• Text generation (chatbots, creative writing, prototyping)
• Educational or experimental projects requiring smaller models

Downstream Use

• Can be fine‑tuned with LoRA adapters for domain‑specific tasks (e.g., summarization, dialogue systems).

Out‑of‑Scope Use

• Not suitable for factual question answering without external grounding
• Not recommended for sensitive or high‑stakes applications (medical, legal, financial)

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Bias, Risks, and Limitations

• Inherits biases from GPT‑2 training data (e.g., stereotypes, toxic language).
• May generate inaccurate or misleading information.
• Limited context window compared to larger modern models.

Recommendations

Users should validate outputs before deployment in production and be aware of risks, biases, and limitations.

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How to Get Started with the Model

from transformers import GPT2TokenizerFast, AutoModelForCausalLM

# Load tokenizer
tokenizer = GPT2TokenizerFast.from_pretrained("path/to/gpt2_compact")

# Load model
model = AutoModelForCausalLM.from_pretrained("path/to/gpt2_compact", trust_remote_code=True)

# Generate text
inputs = tokenizer("Hello world!", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))