README.md

metadata

title: Blackjack State Extractor (0.8B)
emoji: 🃏
colorFrom: yellow
colorTo: pink
sdk: gradio
sdk_version: 6.13.0
app_file: app.py
pinned: false
license: apache-2.0
hardware: zero-a10g

Blackjack State Extractor — 0.8B

Smaller-model variant of the 2B Space. Fine-tuned Qwen3.5-0.8B vision model that reads a blackjack web-app screenshot and either:

1. Extracts the game state as structured JSON (default prompt), or
2. Answers a natural-language question about the screenshot — e.g. "What is the dealer's hand?", "Should I hit or stand?", "What's my balance?".

Models

• Base: unsloth/Qwen3.5-0.8B
• LoRA adapter: davidr99/qwen35-08b-blackjack-reasoning-lora-v4
• Training data: davidr99/blackjack-wp-reasoning-v4

Trained with Unsloth's FastVisionModel on Colab L4 (bf16 LoRA, r=16, 1 epoch).

Why a smaller model?

For OCR-style structured extraction tasks like this one, the LLM portion of a VLM is rarely the bottleneck — the vision encoder does most of the work and is the same size in both 0.8B and 2B variants. Trade-offs vs the 2B:

• 2.5× smaller VRAM footprint (2 GB vs ~5 GB)
• Faster decoding once vision is encoded
• Slightly lower JSON field accuracy (~5–10 percentage points), most pronounced on small text fields like result
• May struggle more with strategy-style Q&A that requires reasoning capacity

Output format

The model thinks step-by-step inside <think>...</think>, then emits either the JSON state or a short natural-language answer depending on the prompt.

Hardware

This Space targets ZeroGPU (free H200 bursts). Inference takes ~1-3 s per request on GPU; on CPU it's ~10-30 s.