JiRackUltra_14b

# JiRack Ultra 14B (CPU)
A fast and efficient 14B model optimized for CPU inference. The model was refactored with BitNet features and an updated tokenizer that includes new Routing, Media, Vision, Sound, Tool call, and Robotics tags. Built on a DeepSeek R1-14B architecture with native ternary (BitNet-style) support and ready-to-run GGUF quantizations.
- JiRack is a cloud-ready model that helps save money on cloud infrastructure. It can be used as an expert model in RAG deployments, with the ONNX JiRack Java server as an alternative.

# JiRack Ternary Architedure & JiRack Tokenizer
- Benefits high quality CPU inference TQ_2 on Llama.cpp and Ollama via QAT
- Robotcs, Routing, Coding, Multimedia, Advanced tool calling via CMSManhattan/JiRackPrecisionTokenizer

## PARTNERSHIP
- NVIDIA
- FISERV

# JiRack sevice options
- Current quantizations were done from the FP16 model, but the model allows for more compression thanks to its ternary architecture.
- If you need to do ternary compression, please write to me and I'll perform QAT from your dataset, tailored specifically to your task.
- Plus double QAT via ONNX QAT.
- Adapt train process to avoid catastrophic forgetting with NDA
- Adapt train process to avoid fast plato in training with NDA
- Convert model to TQ2_0 with support AVX2 and AVX-512 CPU instructions for high performance on CPU
- QAT for TQ_2 Llama.cpp Ternarization docs https://huggingface.co/CMSManhattan/JiRackUltra_14b/blob/main/QAT_to_Llama.cpp_GGUF_TQ2_0_JirackUltra_14b.md
- Adapts to agentic or instruct models for tool calling, using the JiRak tokenizer to enable high-quality tool calling on small models — built as a domain-specific tool expert.
- Deployment and scale

## QAT
- QAT approach 1 script train_qat_ultra_14b_bf16_fulltext_ada_warmup.py
- QAT approach 2 script train_qat_ultra_14b_bf16_with_mask_ada_warmup.py
- The folder for QAT datasets from NVIDIA Nemotron w…

Source: https://huggingface.co/CMSManhattan/JiRackUltra_14b