Geweron/GPT2-Chat-WebCoder-GGUF

WebCoder: Lightweight Web Code Generation (GPT-2 Fine-tuned)

WebCoder is an experimental language model specialized for web development tasks (HTML, CSS, JavaScript, PHP). It's designed to be a lightweight assistant for basic code generation and completion.

Model Lineage: This model represents a second stage of fine-tuning:

1. Base: Standard gpt2 (124M parameters).
2. Stage 1: Fine-tuned on a custom English conversational dataset (resulting in model Geweron/GPT2-Chat-GGUF).
3. Stage 2: GPT2-Chat-WebCoder-GGUF Further fine-tuned the conversational model on a dataset containing web development code snippets (HTML, CSS, JS) and related text.

Goal: To explore the possibility of creating a useful, small-footprint coding assistant for web developers by progressively fine-tuning the gpt2 architecture.

Intended Uses:

• Generating simple HTML/CSS/JS/PHP snippets.
• Basic code completion for web languages.
• Experimenting with lightweight code models.

Limitations:

• Based on the small gpt2 architecture, its coding capabilities are limited compared to larger, dedicated code models.
• May generate incorrect or non-optimal code.
• Accuracy will vary depending on the complexity of the request.
• It inherits limitations from its conversational base model.