Qwen3.5-2B-SpeedX
j-llm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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View details ↗DISCOVER & COMPARE
Explore AI models 84999
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View details ↗s1-gemma4-26b-decision
j-raghavan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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View details ↗FlashMap-Qwen3-4B
j-shen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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View details ↗FlashMap-Qwen3-8B
j-shen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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View details ↗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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View details ↗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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View details ↗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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View details ↗Qwen3.8-27B-q3mix-NInfer
jaakkomoller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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View details ↗This repository provides **GGUF** quantized formats of **`Jab1718/qwen3.8-flash-coder-85gb-bf16`**, a 160-expert coding subnet sliced from `Qwen3.8-Flash-Next` (335GB) using the **`moe-slice`** toolkit.
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View details ↗**`Qwen3.8-Flash-Coder-44GB-Selective-INT8`** is a high-performance, selective-quantized Mixture-of-Experts (MoE) coding model. Sliced down from the monolithic **`Qwen/Qwen3.8-Flash-Next` (335GB)** and quantized from the **`Qwen3.8-Flash-Coder-85GB-BF16`** parent checkpoint, this model reduces disk and VRAM footprint to exactly **44.29 GB** (a **44.2% VRAM reduction** and **86.8% reduction from base**), enabling full zero-offload deployment on **only 2x 32GB GPUs** (e.g. 2x NVIDIA RTX 5000 Ada, 2x RTX 4090/3090, or 1x A100/H100 80GB). Traditional MoE post-training quantization often quantizes all layers uniformly, which severely degrades the Router Gate and causes **Routing Collapse** (routing tokens to sub-optimal experts). This checkpoint introduces **Selective MoE Quantization**: 1. **Critical High-Precision Modules (Kept in 100% Native BF16):** - **Router Gates:** Retain 100% floating-point routing fidelity across all 48 layers. - **Multi-Head Self-Attention & Linear Attention:** `q_proj`, `k_proj`, `v_proj`, `o_proj`. - **Shared Expert, RMSNorms, Embeddings & LM Head:** Zero quantization loss in embedding projections. 2. **High-Capacity Sparse Experts (Quantized to Symmetric Per-Channel INT8):** - 160 MoE Experts across 48 layers (`gate_up_proj`, `down_proj`). - Symmetrically quantized per-channel with dynamic scaling vectors (`gate_up_proj_scale`, `down_proj_scale`).
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View details ↗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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View details ↗CS_Checkpoints_Dataset
JabaleNurAdnan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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View details ↗Laguna-S-2.1-Uncensored-GGUF
jabbatheduck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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View details ↗OpenMythos-GGUF
jabbatheduck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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View details ↗ASR-STT
Jacaranda-Health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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View details ↗UlizaLlama
JacarandaUlizaLlama is a 7B Parameters language model that builds upon the foundation of Jacaranda/kiswallama-pretrained. Jacaranda/kiswallama-pretrained is a large language model continually-pretrained with 321,530,045 swahili tokens and a customized tokenizer with a swahili vocabulary of 20,000 tokens to extend the capabilities of Meta/Llama2. It offers significant improvements in both encoding and decoding for Swahili text, surpassing the Swahili performance of Meta/Llama2. Moreover, Jacaranda/kiswallama-pretrained excels in providing accurate next-word completions in Swahili, a capability which Meta/Llama2 falls short of. - Origin: Adaptation of the Jacaranda/kiswallama-pretrained model which is continually pretrained from Meta/Llama2. - Data: Instructional dataset in Swahili and English consisting of prompt-response pairs. - Training: Alignment to standard methodologies, incorporation of task-centric heads, neural network weight optimization via backpropagation, and task-specific adjustments. - Fine-tuning: Utilized the LoRA approach, refining two matrices that mirror the main matrix from Jacaranda/kiswallama-pretrained. This Low Rank Adapter (LoRa) was vital for instruction-focused fine-tuning. Post-training, the developed LoRa was extracted, and Hugging Face's merge and unload() function facilitated the amalgamation of adapter weights with the base model. This fusion enables standalone inference with the merged model Meanwhile, Jacaranda/kiswallama-pretrained) offers versatility in: - Text Summarization - Autoregressive Text Completion - Content Generation - Text Rewording - Grammar Refinement and Editing - Further Research-The current UlizaLlama is available as a 7 Billion parameters model, further research can also explore availing bigger variants of UlizaLlama. ## Sample Evaluation Prompts The use of the developed Large Language Model (LLM) capabilities is for research,social good and internal use purposes only. For commercial use and distribution, organisations/individuals are encouraged to contactJacaranda Health). To ensure the ethical and responsible use of UlizaLlama, we have outlined a set of guidelines. These guidelines categorize activities and practice
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View details ↗Qwen3.8-27B-OBLITERATED
JACK-POTTSSON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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View details ↗Qwen3.8-27B-OBLITERATEDpintus
JACK-POTTSSON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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View details ↗SPAC-4B-toy4k
Jack04810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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View details ↗spac-qwen3-4b-spatial-pe-8ep
Jack04810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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View details ↗SecBERT
jackadumaThis is the pretrained model presented in SecBERT: A Pretrained Language Model for Cyber Security Text, which is a BERT model trained on cyber security text. The training corpus was papers taken from * APTnotes * Stucco-Data: Cyber security data sources * CASIE: Extracting Cybersecurity Event Information from Text * SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP). SecBERT has its own wordpiece vocabulary (secvocab) that's built to best match the training corpus.
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View details ↗SecRoBERTa
jackadumaThis is the pretrained model presented in SecBERT: A Pretrained Language Model for Cyber Security Text, which is a SecRoBERTa model trained on cyber security text. The training corpus was papers taken from * APTnotes * Stucco-Data: Cyber security data sources * CASIE: Extracting Cybersecurity Event Information from Text * SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP). SecRoBERTa has its own wordpiece vocabulary (secvocab) that's built to best match the training corpus.
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View details ↗Jack-3.8-27B-Coder-16GB-VRAM
JackAgentLead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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