FERNET-C5
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gpt2-small-cs
fav-kky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 ↗modello-italia-9B-GGUF
fbaldassarri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 ↗claudius-latin-400m-chat
fbandov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 ↗A collection of optimized GGUF quantized models derived from BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored, providing various performance-quality tradeoffs.
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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 ↗Scoring 04-February-2024 #1 34B model, outperforming its original base model Smaug-34B-v0.1 with `77.41` 😎 Oh, btw.. this one went thru SFT so the abacus inside Smaug is back to normal.. so you can further train/dpo him .. RESET!.. ==== And people wonders.. why there is no UNA of Hermes or Smaug 70B? << i dont think is worth the time to spend on a model that is widely known for not being too useful, likely UNA can fix some of the internal mess.. for Hermes, we spoke chitchat quick a couple times but nothing solid, but we would like to make a reborn of excellent models using UNA, just liek we did with UNA-Dolphin where we saw relevant performance is short time. === Applied UNA only on the Attention, not on the MLP's * Is based on Smaug * SimpleMath dataset * It was trained on Axolotl
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View details ↗yolov5s-v7.0
fcakyon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-MLX-oQ4e-mtp
fcmeyer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 ↗Quinto-0.2-9b-GGUF
fcreait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 ↗Qwen-Image-Edit-mflux
fcreaitPre-quantized weights for Qwen/Qwen-Image-Edit-2511, ready to use with mflux on Apple Silicon. Full (unquantized) weights: ~54 GB from the original repo. - Quantized with mflux v0.17.4 using `mflux-save` - Source model: Qwen/Qwen-Image-Edit-2511 - Format: MLX safetensors with mflux metadata - Includes VisionTransformer weights (required for image editing)
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View details ↗t5-base-spell-correction-fr
fdemeloThis model corrects spelling and punctuation problems of the input text (in French). It was trained on a synthetic dataset based on a French news corpus (2023) provided by the University of Leipzig using Happy Transformer. The base model used for training is airKlizz/t5-base-multi-fr-wiki-news. The following article was used as reference full article.
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View details ↗antares-1b
fdtn-ai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 ↗Foundation-Sec-8B
fdtn-aiFoundation-Sec-8B (Llama-3.1-FoundationAI-SecurityLLM-base-8B) is an open-weight, 8-billion parameter base language model specialized for cybersecurity applications. It extends Llama-3.1-8B model through continued pretraining on a curated corpus of cybersecurity-specific text, including threat intelligence reports, vulnerability databases, incident response documentation, and security standards. It has been trained to understand security concepts, terminology, and practices across multiple security domains. The model is designed to serve as a domain-adapted base model for use in applications such as threat detection, vulnerability assessment, security automation, and attack simulation. Foundation-Sec-8B enables organizations to build AI-driven security tools that can be deployed locally, reducing dependency on cloud-based AI services while maintaining high performance on security-related tasks. Foundation-Sec-8B is designed for security practitioners, researchers, and developers building AI-powered security workflows and applications. Foundation-Sec-8B is optimized for three core use case categories: The model is intended for local deployment in environments prioritizing data security, regulatory compliance, and operational control.
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View details ↗Foundation-Sec-8B-Instruct
fdtn-aiLlama-3.1-FoundationAI-SecurityLLM-8B-Instruct (Foundation-Sec-8B-Instruct) is an open-weight, 8-billion parameter instruction-tuned language model specialized for cybersecurity applications. It extends the Foundation-Sec-8B base model with instruction-following capabilities. It leverages prior training to understand security concepts, terminology, and practices across multiple security domains. Further instruction-tuning allows the model to interact with human users in a chat-like interface. Foundation-Sec-8B-Instruct enables organizations to build AI-driven security tools that can be deployed locally, reducing dependency on cloud-based AI services while maintaining high performance on security-related tasks. Foundation-Sec-8B-Instruct is designed for security practitioners, researchers, and developers building AI-powered security workflows and applications. Foundation-Sec-8B-Instruct is optimized for three core use case categories: The model is intended for local deployment in environments prioritizing data security, regulatory compliance, and operational control.
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View details ↗Foundation-Sec-8B-Reasoning
fdtn-ai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 was quantized from fdtn-ai/Foundation-Sec-8B-Reasoning to a 4-bit (Q4_K_M) GGUF checkpoint using llama.cpp. It retains the cybersecurity specialization of the original 8-billion-parameter model while reducing the memory footprint from approximately 16GB (BF16) to around 4.92GB (Q4_K_M) for inference.** `fdtn-ai/Foundation-Sec-8B-Reasoning-Q4_K_M-GGUF` is a 4-bit quantized variant of **Foundation-Sec-8B-Reasoning** — an 8B-parameter LLaMA 3.1–based model that extends the **Foundation-Sec-8B** base model with instruction-following and reasoning capabilities. The base model was continued-pretrained on a curated corpus of cybersecurity-specific text (e.g., CVEs, threat intel reports, exploit write-ups, compliance guides). Foundation-Sec-8B-Reasoning is optimized for three core use case categories: Rather than re-uploading or replicating the entire training details, please refer to the original model card for foundational architecture, training data, evaluation results, and known limitations.
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