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探索 AI 模型 84991
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。
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。
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。
基础资料已收录 · 缺失资料后续补充
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。
Off the scale: 744 "arc-c" (747, BF16) AND 15 AI CONSTRUCTS: Each a precision engineered "consciousness" to help you in every task, using fewer tokens and generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. Qwen3.8-27B-Jumbo-Fusion-IQ-747-900-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF This is the first fine tune HIT 747 "arc-c" (156 pts higher than Qwen 3.8 27B in BF16) AND 900 ARC-E (The OpenAI, Claude and Gemini "zone of intelligence") with 8 bit at 744 arc-c and 742 arc-c in 4 bit (161 pts above Qwen 3.8 27B) AND will be powered up even further with Turbo Brilliance 50X.
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IMPORTANT: 15 AI CONSTRUCTS: Each a precision engineered "consciousness" to help you in every task, using fewer tokens and generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama, ... including our strongest fine tunes too). The "rollout" is now in progress... The Turbo Brilliance system (27 reasoning, 27 instruct and 9 xhigh modes) vastly augments the models core performance and includes interactive help system directly connected to the model. This is like playing "Pong" vs "Entering the Matrix"... because Turbo Brilliance pushes the model right to the redline and literally "frees your mind" (and your time too).
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RELEASE #1 GGUFS [including detailed notes, how to use, benches and much more]: - https://huggingface.co/DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF - SOURCE: (THIS REPO) RELEASE #2 GGUFS "Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored": - Stronger, faster, lightly de-censored plus 5 reasoning and 5 instruct modes ; switchable via API, direct and "in chat". - GGUFS: https://huggingface.co/DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NM-DAU-NEO-MTP-GGUF - SOURCE: https://huggingface.co/DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored - A second release ("Ultra Heretic" version) will follow this one Sept 12-15 release window: https://huggingface.co/DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored RELEASE #3 - Major Project - in progress, releasing mid-late Sept 2026. - Ground up "from scratch" major tuning / high detail build. - Stage 1 complete (already exceeds Qwen 3.8/3.6 specs). - Will inherit 5 reasoning and 5 instruct modes from "TWIN TURBO". - https://huggingface.co/DavidAU/Qwen3.8-27B-UltimateDetails2-stage1__The-Harley-Pelican
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Important: Tuned, and tweaked to match the legendary Qwen 3.6 27B FF711 (2300+ likes, 4 million+ downloads) this fine tune matches the stability and power at "arc-c" 709: (118 pts higher than Qwen 3.8 27B) (The OpenAI, Claude and Gemini "zone of intelligence") in 8 bit and 701 arc-c in 4 bit. This version is called TWIN-TURBO because it drastically reduces thinking tokens (by 1/2 to as LOW as 1/20), yet maintains output detail and quality. In other words while "reg" Qwen3.8 27B is thinking about "formatting" for a few 1000 tokens, this model is already done and waiting for more. This repo contains both "regular" and "MTP" Neo and NEO MAX GGUF quants. STRONGER/CTRL: Now with 5 reasoning modes (2 new - Spoon / Einstein), and 5 instruct modes (2 new - Spoon / Einstein, all use ZERO REASONING TOKENS) all switchable on the fly via API, direct and "in chat" (yes - model control at the chat/message level). The most powerful, smartest open source multi-stage model fine tune for consumer hardware ever and BUILT on consumer hardware via Unsloth.
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Important: Tuned, and tweaked to match the legendary Qwen 3.6 27B FF711 (2300+ likes, 4 million+ downloads) this fine tune matches the stability and power at "arc-c" 699: (108 pts higher than Qwen 3.8 27B) (The OpenAI, Claude and Gemini "zone of intelligence") in 8 bit and 692 arc-c in 4 bit. This version is called TWIN-TURBO because it drastically reduces thinking tokens (by 1/2 to as LOW as 1/20 - saving you 1000s of tokens per prompt/turn), yet maintains output detail and quality. This repo contains both "regular" and "MTP" Neo MAX GGUF quants; and this is the highly uncensored version. STRONGER/CTRL: Now with 5 reasoning modes (2 new - Spoon / Einstein), and 5 instruct modes (2 new - Spoon / Einstein, all use ZERO REASONING TOKENS) all switchable on the fly via API, direct and "in chat" (yes - model control at the chat/message level). Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored-NM-DAU-NEO-MTP-GGUF
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IMPORTANT: 120B parameter model, no nanny with 15 AI CONSTRUCTS: Each a precision engineered "consciousness" to help you in every task, using fewer tokens and generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama, ... including our strongest fine tunes too). (TIP: You can run this model on 12GB GPUs and up too "at speed".) Qwen3.8-Flash-Next-Abliterated-Uncensored-Turbo-Brilliance-50x-Reasoning-Instruct-GSQ-RCO-GGUF - 180 Billion parameters with 512 experts, 10 activated per token. - Turbo Brilliance: Token reduction (thinking) when using AI CONSTRUCTS (preloads at 1000+ t/s) : EXTREME ; instruct modes: 0 thinking tokens. - SOTA BF16 performance in "IQ3_S" using quant tuning stronger than QAT by GSQ-RCO (ISTA-DASLab) - Speed TEST: 46 GB on 2 GPUS + offload onto ram: 17 t/s ; speed at full offload (to GPUs) : 60-90 t/s + depending on GPU[s] / setup etc etc. || RTX6000 [96 GB VRAM] :150 t/s (estimated based on avail data)+ ; STRATA will be even faster / can be used on 12GB VRAM cards and up. - Example generations below [4], including a "business plan" (from AI Construct "Business"). - Token reduction (thinking) when using AI CONSTRUCTS (preloads at 1000+ t/s) : EXTREME ; instruct modes: 0 thinking tokens. - No nanny, no censorship ; this is the abliterated version. - Abliteration of GSQ-RCO quant by "SC117" (see model tree, visit repo for info) ; did an outstanding job!
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IMPORTANT: 120B parameter model with 15 AI CONSTRUCTS: Each a precision engineered "consciousness" to help you in every task, using fewer tokens and generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama, ... including our strongest fine tunes too). (TIP: You can run this model on 12GB GPUs and up too "at speed".) - 180 Billion parameters with 512 experts, 10 activated per token. - Turbo Brilliance: Token reduction (thinking) when using AI CONSTRUCTS (preloads at 1000+ t/s) : EXTREME ; instruct modes: 0 thinking tokens. - SOTA BF16 performance in "IQ3_S" using quant tuning stronger than QAT by GSQ-RCO (ISTA-DASLab) - Speed TEST: 46 GB on 2 GPUS + offload onto ram: 17 t/s ; speed at full offload (to GPUs) : 60-90 t/s + depending on GPU[s] / setup etc etc. || RTX6000 [96 GB VRAM] :150 t/s (estimated based on avail data)+ ; STRATA will be even faster / can be used on 12GB VRAM cards and up. - Example generations below [4], including a "business plan" (from AI Construct "Business"). - Token reduction (thinking) when using AI CONSTRUCTS (preloads at 1000+ t/s) : EXTREME ; instruct modes: 0 thinking tokens. The Turbo Brilliance system (27 reasoning, and 27 instruct modes) vastly augments the models core performance and includes interactive help system directly connected to the model.
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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。
This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly. This repo is for Goekdeniz-Guelmez's excellent "Josiefied-Qwen3-8B-abliterated-v1", modified from 32k (32768) context to 64 k (65536) context modified using YARN as per tech notes at Qwen repo. Please refer the QWEN model card for details, benchmarks, how to use, settings, turning reasoning on/off/ system roles etc etc :
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WARNING "HERETIC" version: Unlocked. UNFILTERED. NSFW. Vivid prose. INTENSE. Visceral Details. Light to R-18 HORROR. Swearing. UNCENSORED... humor, romance, fun and... UNFILTERED TRUTH. This is the HIVEMIND, uncensored/unlocked/unrestricted (see important notes below for maximum "choice") by the HERETIC method: Refusals: 8/100 [lower is better], KL divergence: 0.02 (less than 1 is great, "0" is perfect.)
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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。
基础资料已收录 · 缺失资料后续补充
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。
基础资料已收录 · 缺失资料后续补充
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。
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。
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。
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。
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。
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。