Update README.md

README.md

@@ -14,4 +14,145 @@ tags:
 - llm-compressor
 - ocr
 - vlm
----
+---
+
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/D6NaXVEc9diE1NThfi1RK.png)
+
+# **chandra-FP8-Latest**
+
+> **chandra-FP8-Latest** is an FP8-compressed evolution built on top of **datalab-to/chandra**. This variant leverages **BF16 · FP8 (F8_E4M3)** precision formats to significantly reduce memory footprint and improve inference efficiency while preserving the high-precision OCR and layout-aware reasoning capabilities of the original architecture.
+> The result is a highly efficient document intelligence vision-language model optimized for complex parsing, structured output generation, and production-scale deployment.
+
+> [!important]
+> FP8 (8-bit floating point) weight and activation quantization using hardware acceleration on GPUs – [FP8 W8A8](https://docs.vllm.ai/en/stable/features/quantization/fp8/). Quantization W8A8 FP8-dynamic recipe – [examples](https://github.com/vllm-project/llm-compressor/tree/main/examples/quantization_w8a8_fp8).
+
+## About the Base Model
+
+**Chandra** from datalab-to is a state-of-the-art open-source OCR vision-language model designed for complex document parsing and high-fidelity layout reconstruction.
+
+It excels at:
+
+* **Handwriting Recognition** across diverse styles
+* **Table Structure Preservation**, including merged and nested cells
+* **Mathematical Equation Rendering** into clean LaTeX
+* **Form Reconstruction** with checkboxes and radio buttons
+* **Multi-Column Layout Parsing**
+* **40+ Language Support**
+* **Precise Bounding Box Extraction** for every text block, table, and image
+
+Chandra outputs structured **Markdown, HTML, or JSON** with layout-aware coordinates, enabling seamless integration into document intelligence pipelines.
+
+It handles challenging real-world inputs such as:
+
+* Doctor notes
+* Financial filings
+* Invoices
+* Textbooks
+* Government forms
+* Low-quality or messy scanned documents
+
+## What FP8 Adds
+
+The **chandra-FP8-Latest** variant introduces:
+
+* **BF16 · FP8 (F8_E4M3) Compression**: Transformer Engine–based quantization reduces VRAM usage while maintaining OCR precision and layout fidelity.
+* **Higher Throughput**: Faster document parsing at scale.
+* **Lower Memory Footprint**: Improved deployment feasibility on Hopper-class and compatible GPUs.
+* **Production Optimization**: Ideal for high-volume PDF ingestion and enterprise document processing.
+
+## Deployment Support
+
+Chandra supports:
+
+* **Hugging Face Transformers** for local inference
+* **vLLM server deployment** for high-throughput production environments
+* Layout-aware prompts such as `"ocr_layout"`
+* Configurable `max_output_tokens` up to **8192 per page**
+* CLI workflows with environment-based configuration
+* Page-range processing for PDFs
+
+This makes it well-suited for enterprise-scale document AI systems.
+
+## Quick Start with Transformers
+
+```python
+from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+# Load the FP8-compressed chandra model
+model = Qwen3VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/chandra-FP8",
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained(
+    "prithivMLmods/chandra-FP8"
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Analyze the fine-grained details in this image."},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+
+image_inputs, video_inputs = process_vision_info(messages)
+
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+).to("cuda")
+
+generated_ids = model.generate(**inputs, max_new_tokens=256)
+
+generated_ids_trimmed = [
+    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+
+output_text = processor.batch_decode(
+    generated_ids_trimmed,
+    skip_special_tokens=True,
+    clean_up_tokenization_spaces=False
+)
+
+print(output_text)
+```
+
+## Intended Use
+
+* High-precision OCR pipelines
+* Financial and legal document processing
+* Academic and textbook digitization
+* Automated form parsing
+* Enterprise document intelligence systems
+* AI data ingestion pipelines
+
+## License
+
+Licensed under a modified **[OpenRAIL-M](https://huggingface.co/datalab-to/chandra/blob/main/LICENSE)** framework:
+
+* Apache 2.0 for code
+* Commercial restrictions for competitors exceeding $2M revenue
+
+Please review the base model license terms before commercial deployment.
+
+## Limitations & Considerations
+
+* FP8 requires compatible GPU hardware for optimal acceleration.
+* Extremely low-resolution or heavily degraded scans may still impact recognition quality.
+* Users are responsible for ensuring lawful and compliant deployment in regulated environments.