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README.md

@@ -1,74 +1,53 @@
 ---
 language:
   - en
-license: mit
-library_name: gguf
+license: apache-2.0
+library_name: llama-cpp
 tags:
   - gguf
-  - deepseek
-  - ocr
-  - document
   - vision
+  - multimodal
   - affectively
-  - edgework
-  - aether
-  - distributed-inference
-  - edge-deployment
-base_model: deepseek-ai/deepseek-vl2-tiny
-base_model_relation: quantized
+base_model: deepseek-ai/DeepSeek-OCR-2
 pipeline_tag: image-text-to-text
 ---
 
-# DeepSeek OCR 2 (GGUF, Q4_K_M)
+# Deepseek Ocr 2
 
-> **Production-ready** GGUF quantization of [deepseek-ai/deepseek-vl2-tiny](https://huggingface.co/deepseek-ai/deepseek-vl2-tiny) for distributed optical character recognition — powered by the [Aether](https://github.com/affectively-ai/aether) edge inference runtime.
-
-## Highlights
-
-- **~2B parameters** — Second-generation OCR model based on DeepSeek VL2. Improved text extraction accuracy.
-- **~2 GB** Q4_K_M quantized — optimized for distributed edge inference
-- **LLaMA architecture** — proven, stable, well-tested
-- **Aether runtime compatible** — layer-sharded across distributed nodes via [Edgework.ai](https://edgework.ai)
+AFFECTIVELY conversion of [deepseek-ai/DeepSeek-OCR-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2) to GGUF format for edge deployment.
 
 ## Model Details
 
-| Property | Value |
-|----------|-------|
-| Base model | [deepseek-ai/deepseek-vl2-tiny](https://huggingface.co/deepseek-ai/deepseek-vl2-tiny) |
-| Parameters | ~2B |
-| Architecture | LLaMA |
-| Quantization | Q4_K_M |
-| Format | GGUF |
-| Size | ~2 GB |
-| License | mit |
+- **Source Model**: [deepseek-ai/DeepSeek-OCR-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2)
+- **Format**: GGUF
+- **Converted by**: [AFFECTIVELY](https://affectively.ai)
 
 ## Usage
 
 ### With llama.cpp
 
 ```bash
-./llama-cli -m deepseek-ocr-2-q4_k_m.gguf -p "Your prompt here" -n 256
+./llama-cli -m deepseek-ocr-2-gguf.gguf -p "Your prompt here" -n 256
 ```
 
-### With Aether (Distributed Inference)
-
-This model is deployed across the [Aether](https://github.com/affectively-ai/aether) distributed inference network. Weights are layer-sharded and distributed across multiple edge nodes for parallel inference.
-
-## Deployment Architecture
+### With Ollama
 
-This model runs on the **Aether distributed inference runtime** — our custom engine that shards model layers across multiple nodes for parallel execution:
+Create a `Modelfile`:
+```
+FROM ./deepseek-ocr-2-gguf.gguf
+```
 
-1. **Coordinator** receives requests and manages token generation
-2. **Layer nodes** each hold a subset of model layers
-3. **Hidden states flow** between nodes via gRPC
-4. **Zero cold start** via warm pool scheduling
+```bash
+ollama create deepseek-ocr-2-gguf -f Modelfile
+ollama run deepseek-ocr-2-gguf
+```
 
-Deployed via [Edgework.ai](https://edgework.ai) — bringing fast, cheap, and private inference as close to the user as possible.
+## About AFFECTIVELY
 
-## About
+[AFFECTIVELY](https://affectively.ai) is an emotion intelligence platform that runs AI models at the edge -- in-browser, on-device, zero cloud cost. These converted models power the platform's real-time emotion analysis, speech recognition, and natural language capabilities.
 
-Published by [AFFECTIVELY](https://huggingface.co/affectively-ai) · Managed by [@buley](https://huggingface.co/buley)
+All conversions are optimized for edge deployment within browser and mobile memory constraints.
 
-We quantize and publish **production-ready models** for distributed edge inference via the [Aether](https://github.com/affectively-ai/aether) runtime. Every release is tested for correctness and stability before publication.
+## License
 
-- [All models](https://huggingface.co/affectively-ai) · [GitHub](https://github.com/affectively-ai) · [Edgework.ai](https://edgework.ai)
+Apache 2.0 (follows upstream model license)