{"v":1,"id":"model:hf:CMSManhattan/JiRackUltra_14b","slug":"model-cmsmanhattan-jirackultra-14b","kind":"model","category":"llm","title":"JiRackUltra_14b","summary":"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, Sou…","source":{"provider":"hf","ref":"CMSManhattan/JiRackUltra_14b","url":"https://huggingface.co/CMSManhattan/JiRackUltra_14b","rev":"1998717242fdacc84bcf10365e05d2de9639d79b","fetchedAt":"2026-10-02T20:59:32.622Z","etag":"W/\"a2ad-ZrAAXIY0ABekq64UhHeB/Fd/7Rs\""},"author":{"name":"CMSManhattan","url":"https://huggingface.co/CMSManhattan"},"license":{"spdx":"mit","raw":"mit","open":true},"metrics":{"downloads":963843,"downloadsWeek":327838,"likes":2,"takenAt":"2026-10-02T20:59:32.622Z"},"tags":["safetensors","gguf","qwen2","text-generation","ternary","bitnet","1.58bit","cpu","qwen2.5","deepseek","efficient","low-memory","jirack","web-ui","routing","tool-call","robotics","conversational","en","zh","ja","ko","fr","es","pt","de","it","ru","ar","vi","th","endpoints_compatible"],"pipeline":"text-generation","links":{"github":"ggml-org/llama.cpp","npm":"node-llama-cpp"},"updatedAt":"2026-10-01T18:11:59.000Z","collectedAt":"2026-10-02T20:59:32.622Z","review":{"numbers":["963,843 downloads on Hugging Face","2 likes","license mit","8.4 GB for JiRackUltra_14b_Q4_K_M.gguf","327,838 npm downloads a week for node-llama-cpp","latest node-llama-cpp@3.22.1"],"log":null},"trust":"unlabeled","health":{"status":"alive","checkedAt":"2026-10-02T20:59:32.622Z","http":200},"description":"# JiRack Ultra 14B (CPU)\nA 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.\n- 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.\n\n# JiRack Ternary Architedure & JiRack Tokenizer\n- Benefits high quality CPU inference TQ_2 on Llama.cpp and Ollama via QAT\n- Robotcs, Routing, Coding,  Multimedia, Advanced tool calling via CMSManhattan/JiRackPrecisionTokenizer\n\n## PARTNERSHIP\n- NVIDIA\n- FISERV\n\n# JiRack sevice options\n- Current quantizations were done from the FP16 model, but the model allows for more compression thanks to its ternary architecture.\n- If you need to do ternary compression, please write to me and I'll perform QAT from your dataset, tailored specifically to your task.\n- Plus double QAT via ONNX QAT.\n- Adapt train process to avoid catastrophic forgetting with NDA\n- Adapt train process to avoid fast plato in training with NDA\n- Convert model to TQ2_0 with support AVX2 and AVX-512 CPU instructions for high performance on CPU\n- 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\n- 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.\n- Deployment and scale\n\n## QAT\n- QAT approach 1 script train_qat_ultra_14b_bf16_fulltext_ada_warmup.py\n- QAT approach 2 script train_qat_ultra_14b_bf16_with_mask_ada_warmup.py\n- The folder for QAT datasets from NVIDIA Nemotron w…\n\nSource: 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