Add model card (weights uploading separately)

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-text-to-text
+tags:
+- multimodal
+- vlm
+- artemis-vlm
+- document-understanding
+- chart-understanding
+- ocr
+- mistral
+- qwen3-vl
+- schneewolf-labs
+- a-series
+base_model: schneewolflabs/A3
+datasets:
+- schneewolflabs/ChartDocMix-v1
+---
+
+# A3-Doc
+
+**A3-Doc** is a document + chart understanding specialist in the Schneewolf
+Labs A-series — a focused-excellence Stage-2 full fine-tune of
+[**A3**](https://huggingface.co/schneewolflabs/A3) on
+[ChartDocMix-v1](https://huggingface.co/datasets/schneewolflabs/ChartDocMix-v1).
+Where [A3-Instruct](https://huggingface.co/schneewolflabs/A3-Instruct) is the
+generalist sibling, A3-Doc trades breadth for depth on the ChartQA / DocVQA /
+InfoVQA / TextVQA / OCRBench class of tasks.
+
+## What it is
+
+| | |
+|---|---|
+| Architecture | Qwen3-VL ViT (frozen, ~0.41 B) + 2-layer MLP projector (trained) + A2/Mistral decoder (full FFT) |
+| Total params | **12.69 B** (12.28 B trainable in Stage-2; ViT frozen) |
+| Base | [`schneewolflabs/A3`](https://huggingface.co/schneewolflabs/A3) |
+| Training corpus | [`schneewolflabs/ChartDocMix-v1`](https://huggingface.co/datasets/schneewolflabs/ChartDocMix-v1) (241,435 rows: ~96% doc/chart/OCR VQA + 4% identity rehearsal) |
+| Epochs | 1 (15,075 steps) |
+| Effective batch | 16 (bs 1 × grad-accum 16) |
+| Optimizer | paged AdamW 8-bit |
+| Learning rate | 1e-5, cosine, warmup 3% |
+| Max seq length | 2048 |
+| **Vision token cap** | **max_pixels = 512×512 (262 K px)** — see the resolution note below |
+| Hardware | 1× NVIDIA GB10 (DGX Spark, 128 GB unified) |
+| Wall-clock | ~3.3 days |
+| Final eval loss | **0.499** (down from 0.647 at the first eval) |
+
+The single-domain corpus is far more learnable than the generalist mix: A3-Doc
+reaches eval loss 0.499, well under A3-Instruct's 0.752 on the broad corpus.
+
+## Benchmarks
+
+Greedy decoding, lmms-eval terse-answer prompt convention, 500-row validation
+slices (a fast read — see caveats). Metrics: ChartQA relaxed accuracy, DocVQA /
+InfoVQA ANLS, TextVQA VQA-accuracy, OCRBench contains-accuracy.
+
+| Benchmark | A3-Doc | Metric |
+|---|---|---|
+| ChartQA  | 53.2 | relaxed acc |
+| DocVQA   | 48.4 | ANLS |
+| InfoVQA  | 34.2 | ANLS |
+| TextVQA  | 71.6 | VQA acc |
+| OCRBench | 67.0 (670/1000) | contains |
+
+For a Path-B graft trained on 241 K rows, **TextVQA and OCRBench are genuinely
+respectable** — scene-text and OCR transferred well. DocVQA/InfoVQA are the
+weak spots, and the reason is known (below).
+
+**Caveats:** numbers are a 500-row slice, not full splits. ChartQA's `test`
+split interleaves `human_test` (harder) and `augmented_test` (easier) and the
+published number averages both — a flat 500-row sample may over-represent one
+type. Treat these as indicative, not leaderboard-final.
+
+## The resolution finding (important)
+
+A3-Doc was trained **and** evaluated at `max_pixels = 512×512`. DocVQA and
+InfoVQA are high-resolution document scans where text is tiny, so at 512² much
+of the text is illegible. This is the dominant limiter on those two tasks.
+
+Diagnostic — **eval-only**, no retraining, same 200 rows:
+
+| Benchmark | @512² (262 K px) | @1280² (1.64 M px) | Δ |
+|---|---|---|---|
+| DocVQA (ANLS)  | 0.525 | **0.580** | **+5.5** |
+| InfoVQA (ANLS) | 0.385 | **0.420** | **+3.5** |
+
+The frozen ViT + projector + decoder **generalize to higher visual-token
+counts despite only seeing 512² in training**. The eval-only gain is a floor;
+a retrain at higher `max_pixels` should beat it. If you run A3-Doc yourself,
+**raise `max_pixels`** (the `ArtemisVLMProcessor` accepts it) for document
+tasks — it costs more tokens/latency but helps.
+
+## Intended use
+
+Document & chart VQA, infographic QA, OCR-style reading, chart captioning. For
+broad conversation/creative use reach for
+[A3-Instruct](https://huggingface.co/schneewolflabs/A3-Instruct); for dense
+image captioning reach for [A3](https://huggingface.co/schneewolflabs/A3).
+
+## Inference
+
+```python
+from transformers import AutoConfig, AutoTokenizer
+from artemis_vlm import ArtemisVLMForConditionalGeneration, ArtemisVLMProcessor
+import torch
+
+ckpt = "schneewolflabs/A3-Doc"
+model = ArtemisVLMForConditionalGeneration.from_pretrained(ckpt, dtype=torch.bfloat16).to("cuda")
+cfg = AutoConfig.from_pretrained(ckpt, trust_remote_code=True)
+tok = AutoTokenizer.from_pretrained(ckpt, trust_remote_code=True)
+# raise max_pixels for document tasks (training default was 512*512):
+proc = ArtemisVLMProcessor(tokenizer=tok, vision_config=cfg.vision_config,
+                           max_pixels=1280*1280)
+```
+
+Also runs in llama.cpp via the `Schneewolf-Labs/llama.cpp` fork's Artemis VLM
+mmproj graft (same pattern as A3 / A3-Instruct).
+
+## Roadmap — A3-Doc-v2
+
+The resolution finding points to the obvious next lever: **retrain at
+1024²–1280² max_pixels** rather than 512². Same corpus, same recipe, higher
+vision budget. Expected to push DocVQA/InfoVQA well past the eval-only gains.
+
+## Lineage
+
+- [`schneewolflabs/A3`](https://huggingface.co/schneewolflabs/A3) — Stage-1 base (projector-only alignment)
+- [`schneewolflabs/A2`](https://huggingface.co/schneewolflabs/A2) — text decoder (Mistral 12.3 B)
+- [`schneewolflabs/ChartDocMix-v1`](https://huggingface.co/datasets/schneewolflabs/ChartDocMix-v1) — training corpus
+- [`schneewolflabs/i-DPO`](https://huggingface.co/datasets/schneewolflabs/i-DPO) — identity/voice anti-drift bedrock
+
+## License
+
+apache-2.0, consistent with the rest of the A-series lineage. Constituent
+training sources carry their own licenses (see the ChartDocMix-v1 card).