🚀 Indenta-13M-Python (GGUF)
An optimized from-scratch model made with a custom tokenizer and GPT-2 architecture.
This model is built specifically for lightning-fast Python code completions and basic code generation. At ~13M parameters, it runs with near-zero latency on absolutely any hardware!
🛠️ Web UI & Multi-Turn Stability Note
This model features a hyper-lightweight 13M parameter footprint optimized for single-task completions based directly on structural templates.
Because it lacks the large capacity required for conversational context processing, it can drop formatting structure if a graphical Web UI forces a massive, multi-message chat stream into its context layers.
For Best Performance in Web UIs:
- Use New Chat Threads: Click the "New Chat" or "Clear" button in your user interface between coding tasks. This keeps the model's focus squarely on your active prompt.
- Synchronized Template: This model card includes an aligned layout format (
## Instruction: and ### Input:) matching the training data to stop token bleeding across chat iterations.
