Qwen3-8B-GGUF
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JiRackDeltaNet_180b
CMSManhattanA 180B MoE model built on the Qwen3.8-Flash-Next architecture: Gated DeltaNet linear attention interleaved with Qwen Sparse Attention (QSA), 512 experts with top-10 routing plus a shared expert, gated residual (hyper-connection) streams, and a 51B-parameter hashed n-gram embedding table. Only ~6B parameters are computed per token, so throughput on CPU is closer to a 6B dense model while quality is that of a much larger one. - 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. **Important:** until QAT is complete, the JiRack 180B weights are numerically identical to the base model (lambda = 0 means BitLinear is a pure passthrough). The ternary benefits described on this card apply after QAT. Published in this repository. Before QAT all of them are quantizations of the unchanged base-model weights.
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詳細を見る ↗JiRackDeltaNet_27b
CMSManhattanA fast and efficient 27B model optimized for CPU inference. Built on a Qwen3.8-style DeltaNet architecture (hybrid attention + SSM), with an updated tokenizer that includes **Routing**, **Media**, **Vision**, **Sound**, **Tool call**, and **Robotics** tags. Ready-to-run GGUF quantizations, and native Ollama support with reasoning disabled by default for fast, direct responses. - 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. Once the container is running, open your browser and navigate to: `http://localhost:7869` This opens the JiRack UI — a clean web interface. The listening port can be easily modified directly from the **Settings** panel within the JiRack UI.
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詳細を見る ↗JiRackPrecisionTokenizer
CMSManhattan基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗JiRackTernaryGemma4_26b
CMSManhattan基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗JiRackTernaryPro_1b
CMSManhattan基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗JiRackUltra_14b
CMSManhattanA 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. https://huggingface.co/spaces/CMSManhattan/JiRackUltra_14b - https://huggingface.co/spaces/CMSManhattan/JiRack-36B-Chat - https://huggingface.co/spaces/CMSManhattan/JiRack-16B-Chat - https://huggingface.co/spaces/CMSManhattan/JiRack-8B-Chat - https://huggingface.co/spaces/CMSManhattan/JiRack-2B-Chat Once the container is running, open your browser and navigate to: `http://localhost:7869` This opens the JiRack UI — a clean web interface. The listening port can be easily modified directly from the **Settings** panel within the JiRack UI. - Model weights are released under the MIT License — free to use, modify, and distribute for any purpose, including commercial. No royalties, no per-user fees, no subscription. - The Docker image with UI and the pre-built Ollama quantizations are separate paid products. If you prefer to build your own secure deployment — take the weights, assemble your own stack, and you're done. - The JiRack Ultra 14B model for Docker and Ollama is provided under a commercial license ($12 per user per year). - All JiRack UI clients are provided under a commercial license. - However, the UI clients can be used for free when running together with the official JiRack Docker containers, as long as they are not redistributed separately. For commercial licensing, cluster deployment, or enterprise use of JiRack models, please contact us. - **JiRack MS Windows 11 Desktop Client (with Ollama API):** https://huggingface.co/kgrabko/JiRackTernary_1b/resolve/main/jirack-chat.zip - **Live email chat with the model:** support@cmsmanhattan.com | Use Case | CPU | RAM | Recommended Quant | Expected Speed | Recommendation | |-----------
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詳細を見る ↗JiRackUltra_1b
CMSManhattanA fast and efficient ~1.5B model optimized for CPU inference. The model was refactored with BitNet features and an updated tokenizer that includes new **Routing**, **Tool call**, and **Robotics** tags. Built on a redesigned DeepSeek R1 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. - Benefits high quality CPU inference TQ_2 on Llama.cpp and Ollama via QAT - Robotcs, Routing, Coding, Multimedia, Advanced tool calling via CMSManhattan/JiRackPrecisionTokenizer - We are working to support JiRack on Ollama for production systems also - added Jirack chat without reasoning feature https://ollama.com/cmsmanhattan - Follow fresh Ollama platform updates
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詳細を見る ↗JiRackUltra_32b
CMSManhattanA fast and efficient 32B 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 R1-32B 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. Once the container is running, open your browser and navigate to: `http://localhost:7869` This opens the JiRack UI — a clean web interface. The listening port can be easily modified directly from the **Settings** panel within the JiRack UI. - Model weights are released under the MIT License — free to use, modify, and distribute for any purpose, including commercial. No royalties, no per-user fees, no subscription. - The Docker image with UI and the pre-built Ollama quantizations are separate paid products. If you prefer to build your own secure deployment — take the weights, assemble your own stack, and you're done. - The JiRack Ultra 32B model for Docker and Ollama is provided under a commercial license ($12 per user per year). - All JiRack UI clients are provided under a commercial license. - However, the UI clients can be used for free when running together with the official JiRack Docker containers, as long as they are not redistributed separately. For commercial licensing, cluster deployment, or enterprise use of JiRack models, please contact us. - **JiRack MS Windows 11 Desktop Client (with Ollama API):** https://huggingface.co/kgrabko/JiRackTernary_1b/resolve/main/jirack-chat.zip - **Live email chat with the model:** support@cmsmanhattan.com | Use Case | CPU | RAM | Recommended Quant | Expected Speed | Recommendation | |-------------------|----------------------------|-----------|-------------------|---------------------|----------------| | Recommended | Ryzen 9 / Intel i9 / Xeon | 32–48 GB | Q4_K_M | Good interactive | Best choice | | High Performance | High-core server CPU | 64 GB+ | Full / Q4 | Excellent | Excellent | | Low
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詳細を見る ↗JiRackUltra_7b
CMSManhattanA fast and efficient 7B 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 -7B 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. The listening port can be easily modified directly from the **Settings** panel within the JiRack UI. For commercial licensing, cluster deployment, or enterprise use of JiRack models, please contact us.
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詳細を見る ↗Llama3Ternary_10b
CMSManhattan基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Llama3Ternary_2b
CMSManhattan基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗osim-8b
cmu-lti基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗wav2vec2-french-phonemizer
Cnam-LMSSCFine-tuned facebook/wav2vec2-base-fr-voxpopuli-v2 for **French speech-to-phoneme** (without language model) using the train and validation splits of Common Voice v13. As this model is specifically trained for a speech-to-phoneme task, the output is sequence of IPA-encoded words, without punctuation. If you don't read the phonetic alphabet fluently, you can use this excellent IPA reader website to convert the transcript back to audio synthetic speech in order to check the quality of the phonetic transcription. The model has been finetuned on Commonvoice-v13 (FR) for 14 epochs on a 4x2080 Ti GPUs at Cnam/LMSSC using a ddp strategy and gradient-accumulation procedure (256 audios per update, corresponding roughly to 25 minutes of speech per update -> 2k updates per epoch)
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詳細を見る ↗wav2vec2-french-phonemizer-v2
Cnam-LMSSCThis version fixes the absence of the voiced labial-palatal approximant **ɥ**. The original Cnam-LMSSC/wav2vec2-french-phonemizer never transcribes the pronunciation of the voiced labial–palatal approximant with /ɥ/ and instead incorrectly replaces it with the sequence /yi/. This created issues for phonetic accuracy and linguistic analysis. Words like "nuit" or "huit" are pronounced with \ɥ\ and not with \yi\ For Text-to-Speech (TTS) task, substituting \ɥ\ with \yi\ results in unnatural prosody. Fine-tuned facebook/wav2vec2-base-fr-voxpopuli-v2 for **French speech-to-phoneme** (without language model) using the train and validation splits of Common Voice v13. As this model is specifically trained for a speech-to-phoneme task, the output is sequence of IPA-encoded words, without punctuation. If you don't read the phonetic alphabet fluently, you can use this excellent IPA reader website to convert the transcript back to audio synthetic speech in order to check the quality of the phonetic transcription.
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詳細を見る ↗CrPO-sft-llama-3.1-8b-instruct
CNCL-Penn-State基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗t5-small-booksum
cnicu基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗This Model2Vec model was created by using Tokenlearn, with nomic-embed-text-v2-moe as a base. The evaluation in the model card, was executed using this model (distilled), not the original. The process to create this one, was not a simple model2vec distill, this involved generating embeddings for 23M triplets (msmarco) with the original model, then training the tokenlearn model on it, with the nomic model as a base.
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗tiny-image-captioning
cnmoro基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗biolinkbert-mednli
cnut1648基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗LLaMA2-7B-fingerprinted-SFT
cnut1648基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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