YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
NVIDIA Nemotron Parse v1.1 (GGUF, Q4_K_M)
Production-ready GGUF quantization of nvidia/nemotron-parse-v1.1 for distributed document parsing and extraction — powered by the Aether edge inference runtime.
Highlights
- ~2B parameters — NVIDIA document parsing model. Extracts structured data from documents, PDFs, and web pages.
- ~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
Model Details
| Property | Value |
|---|---|
| Base model | nvidia/nemotron-parse-v1.1 |
| Parameters | ~2B |
| Architecture | LLaMA |
| Quantization | Q4_K_M |
| Format | GGUF |
| Size | ~2 GB |
| License | other |
Usage
With llama.cpp
./llama-cli -m nvidia-nemotron-parse-v1.1-q4_k_m.gguf -p "Your prompt here" -n 256
With Aether (Distributed Inference)
This model is deployed across the Aether distributed inference network. Weights are layer-sharded and distributed across multiple edge nodes for parallel inference.
Deployment Architecture
This model runs on the Aether distributed inference runtime — our custom engine that shards model layers across multiple nodes for parallel execution:
- Coordinator receives requests and manages token generation
- Layer nodes each hold a subset of model layers
- Hidden states flow between nodes via gRPC
- Zero cold start via warm pool scheduling
Deployed via Edgework.ai — bringing fast, cheap, and private inference as close to the user as possible.
About
Published by AFFECTIVELY · Managed by @buley
We quantize and publish production-ready models for distributed edge inference via the Aether runtime. Every release is tested for correctness and stability before publication.