FERNET-C5
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84997
gpt2-small-cs
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗antares-1b
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗**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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。