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README.md

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+---
+pipeline_tag: image-to-text
+library_name: onnxruntime
+tags:
+- falcon
+- ocr
+- vision-language
+- document-understanding
+license: apache-2.0
+---
+
+
+# ONNX model for [Falcon-OCR](https://huggingface.co/tiiuae/Falcon-OCR)
+
+## try with [ningpp/flux](https://github.com/ningpp/flux)
+
+Flux is a Java-based OCR
+
+
+
+# Falcon OCR
+
+Falcon OCR is a 300M parameter early-fusion vision-language model for document OCR. Given an image, it can produce plain text, LaTeX for formulas, or HTML for tables, depending on the requested output format.
+
+Most OCR VLM systems are built as a pipeline with a vision encoder feeding a separate text decoder, plus additional task-specific glue. Falcon OCR takes a different approach: a single Transformer processes image patches and text tokens in a shared parameter space from the first layer, using a hybrid attention mask where image tokens attend bidirectionally and text tokens decode causally conditioned on the image.
+
+We built it this way for two practical reasons. First, it keeps the interface simple: one backbone, one decoding path, and task switching through prompts rather than a growing set of modules. Second, a 0.3B model has a lower latency and cost footprint than 0.9B-class OCR VLMs, and in our vLLM-based serving setup this translates into higher throughput, often 2–3× faster depending on sequence lengths and batch configuration. To our knowledge, this is one of the first attempts to apply this early-fusion single-stack recipe directly to competitive document OCR at this scale.
+
+

config.json

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+{
+  "architectures": [
+    "FalconOCRForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_falcon_ocr.FalconOCRConfig",
+    "AutoModelForCausalLM": "modeling_falcon_ocr.FalconOCRForCausalLM"
+  },
+  "model_type": "falcon_ocr",
+  "torch_dtype": "float32",
+  "dim": 768,
+  "n_layers": 22,
+  "n_heads": 16,
+  "head_dim": 64,
+  "n_kv_heads": 8,
+  "vocab_size": 65536,
+  "ffn_dim": 2304,
+  "norm_eps": 1e-05,
+  "max_seq_len": 8192,
+  "rope_theta": 10000,
+  "channel_size": 3,
+  "spatial_patch_size": 16,
+  "temporal_patch_size": 1,
+  "eos_id": 11,
+  "img_id": 227,
+  "image_cls_token_id": 244,
+  "image_reg_1_token_id": 245,
+  "image_reg_2_token_id": 246,
+  "image_reg_3_token_id": 247,
+  "image_reg_4_token_id": 248,
+  "img_end_id": 230
+}

falcon_ocr_kv_token.onnx

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+oid sha256:e31c4feadd5dfdb1fb1321c7e4e46f3db6db917054b48ba5a47b71cb4f9b94d5
+size 1081568222

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+}

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+}