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----
-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](https://huggingface.co/spaces/davidr99/blackjack-state-extractor). 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`](https://huggingface.co/unsloth/Qwen3.5-0.8B)
-- LoRA adapter: [`davidr99/qwen35-08b-blackjack-reasoning-lora-v3`](https://huggingface.co/davidr99/qwen35-08b-blackjack-reasoning-lora-v3)
-- Training data: [`davidr99/blackjack-wp-reasoning-v3`](https://huggingface.co/datasets/davidr99/blackjack-wp-reasoning-v3)
-
-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.
+---
+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](https://huggingface.co/spaces/davidr99/blackjack-state-extractor). 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`](https://huggingface.co/unsloth/Qwen3.5-0.8B)
+- LoRA adapter: [`davidr99/qwen35-08b-blackjack-reasoning-lora-v4`](https://huggingface.co/davidr99/qwen35-08b-blackjack-reasoning-lora-v4)
+- Training data: [`davidr99/blackjack-wp-reasoning-v4`](https://huggingface.co/datasets/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.