Gemma-3-27B-it-Uncensored-GGUF
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Gemma-3-4B-it-Uncensored-GGUF
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View details ↗Gemma-4-12B-uncensored-8bit-mlx
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View details ↗Gemma-4-12B-uncensored-mxfp4-mlx
lemuralabs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 ↗lemura-arabic-asr-qwen3
lemuralabs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 ↗Llama-3.2-3B-Uncensored-GGUF
lemuralabsAt Lemura Labs, we believe in pushing the boundaries of innovation by providing advanced and unrestricted AI models for every application. Dive into our world of possibilities and experience the freedom of **Lemura Labs-Llama-3.2-3B-Uncensored**, tailored to meet diverse needs with exceptional performance. 1. **Uncensored Responses**: Capable of addressing any query without content restrictions, offering detailed and uninhibited answers. 2. **Versatility**: Excels in diverse use cases, from complex technical queries to engaging casual conversations. 3. **Advanced Contextual Understanding**: Draws from an expansive knowledge base for accurate and context-aware outputs. 4. **Extended Context Handling**: Optimized for handling long-context interactions for improved continuity and depth. 5. **Customizability**: Adaptable to specific tasks and user preferences through fine-tuning. After fine-tuning with **uncensored data**, **Lemura Labs-Llama-3.2-3B** demonstrates **superior performance compared to the original LLaMA model**, particularly in accuracy and handling diverse, unrestricted scenarios.
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View details ↗Qwen3.6-27B-Coder-uncensored-MXFP4
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View details ↗Qwen3.6-27B-Coder-uncensored-MXFP8
lemuralabs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 ↗Qwen3.6-27B-Fable-Agentic
lemuralabs**Qwen3.6-27B-Fable-Agentic** is a powerful 27B agentic model that reasons step-by-step and operates tools across multi-turn sessions, while delivering top-tier general knowledge. Fine-tuned from Jackrong/Qwopus3.6-27B-v2. Evaluated head-to-head against the base model on identical questions, with an 8192-token budget so step-by-step reasoning is never truncated. > **Knowledge improved, hard reasoning preserved:** +1.43 over base on MMLU-Pro, matching it on GSM8K math and on GPQA-Diamond (graduate-level science, ~frontier-class for a 27B) - with agentic tool-calling added on top.
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View details ↗tamil-asr-qwen3
lemuralabs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 ↗content-vec-best
lengyue233Official Repo: ContentVec This repo brings fairseq ContentVec model to HuggingFace Transformers. You need to download the ContentVec_legacy model from the official repo, and then run
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View details ↗pflm1
lennartcb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 ↗paligemma-3b-pt-224
leo009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 model is a fine-tuned version of monologg/koelectra-small-v3-discriminator on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1431 - Precision: 0.8232 - Recall: 0.8449 - F1: 0.8339 - Accuracy: 0.9628 태깅 시스템 : BIO 시스템 - B-(begin) : 개체명이 시작할 때 - I-(inside) : 토큰이 개체명 중간에 있을 때 - O(outside) : 토큰이 개체명이 아닐 경우 개체명 인식(NER) 모델 학습 데이터 셋 - 문화체육관광부 > 국립국어원 > 모두의 말뭉치 > 개체명 분석 말뭉치 2021 - https://corpus.korean.go.kr/request/reausetMain.do
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View details ↗mt5-small-cc-news-es-titles
LeoCordoba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 ↗PIGuard
leolee99- Website: https://injecguard.github.io/ - Paper: https://aclanthology.org/2025.acl-long.1468.pdf - Code Repo: https://github.com/leolee99/PIGuard Due to some licensing issues, the model name has been changed from **InjecGuard** to **PIGuard**. We apologize for any inconvenience this may have caused. Prompt injection attacks pose a critical threat to large language models (LLMs), enabling goal hijacking and data leakage. Prompt guard models, though effective in defense, suffer from over-defense—falsely flagging benign inputs as malicious due to trigger word bias. To address this issue, we introduce ***NotInject***, an evaluation dataset that systematically measures over-defense across various prompt guard models. NotInject contains 339 benign samples enriched with trigger words common in prompt injection attacks, enabling fine-grained evaluation. Our results show that state-of-the-art models suffer from over-defense issues, with accuracy dropping close to random guessing levels (60\%). To mitigate this, we propose ***PIGuard***, a novel prompt guard model that incorporates a new training strategy, *Mitigating Over-defense for Free* (MOF), which significantly reduces the bias on trigger words. InjecGuard demonstrates state-of-the-art performance on diverse benchmarks including NotInject, surpassing the existing best model by 30.8\%, offering a robust and open-source solution for detecting prompt injection attacks.
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View details ↗leo-hessianai-7b
LeoLM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 ↗gemma-3-27b-it-FP8-Dynamic
leon-se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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