omnivoice-thai
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探索 AI 模型 84999
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查看详情 ↗Qwen3.8-27B-abliterated
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查看详情 ↗GGUF builds of the **offensive-security / agentic tool-calling** merged model hotdogs/Qwen3.8-27B-abliterated-cyber-preview (the cyber LoRA merged into the abliterated base at **scale 1.0**, with the **MTP head preserved**). All files are imatrix-quantized and are ready to run in llama.cpp with native **self-speculative (MTP) decoding**. > For **authorized security research and red-teaming** only — this model will > not refuse and may emit exploit instructions. Use only on systems you own or > are explicitly authorized to test. Apache-2.0. The quantized files were produced with an **imatrix** (see `...-imatrix.dat`) computed from the bf16 master — required for the 4-bit IQ files and improving low-bit quality.
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查看详情 ↗GGUF quantization of **hotdogs/Qwen3.8-27B-abliterated** — the training-free abliterated (refusal-removed) build of `Qwen/Qwen3.8-27B` — converted for **llama.cpp** with the **MTP (Multi-Token Prediction) head preserved**. > **Status: testing / development.** This is an early GGUF upload for evaluation. It has **not yet been calibrated with an imatrix** — the low-bit `Q4_K_M` quality is expected to improve once an imatrix is added (see *Roadmap*). Feedback welcome. These `-mixed` quants use a **hybrid precision recipe** to get closer to the f16 reference: the token-embedding and all output tensors are kept at `q8_0` while the rest of the network is quantized to the base level, and they are built with an **imatrix** (CPU calibration) — `Q4_K_M-mixed` and `Q6_K-mixed`.
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查看详情 ↗Qwen35B-Agent-R2-Abliterated
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查看详情 ↗**BoldingBuilds' abliterated Bonsai 2 v2, with two SVD-LoRAs merged in at 0.4 each:** `hotdogs/swift_unc_qwen3.8-27B_lora` (abliteration + Swift-1.5 reasoning-efficiency) and `hotdogs/thinkingcap_qwen3.8-27B_svd_lora` (ThinkingCap token-efficient reasoning). Same 27B ternary runtime as the base — **PQ2_0 packing, Hadamard latent, MTP head, 262,144-token context**, ~7.6 GB on disk. Vision projector included. > ⚠️ **This file needs PrismML's llama.cpp fork.** PQ2_0 does **not** load in mainline llama.cpp, Ollama, LM Studio or llama.app. See Requirements.
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查看详情 ↗A DFlash2 draft model fine-tuned for one target: `DavidAU/Qwen3.8-Flash-Next-Abliterated-Uncensored-Turbo-Brilliance-50x-Reasoning-Instruct-GSQ-RCO-GGUF` (IQ3_S). It starts from `incoai/Qwen3.8-Flash-Next-DFlash2`, which was trained on the official Qwen3.8-Flash-Next. It is not a standalone language model: it drafts tokens that the target verifies, so the output is the target's own (greedy output is identical with or without it). **v2 (2026-10-22):** trained on 10x more of the target's own data, a wider draft vocabulary, and every llama.cpp quantization from F32 down to IQ4_XS, each measured. v1 (`t3000-q8-dv.gguf`) stays in place for existing configs. The drafter is small next to the target (tens of GB; this is about 1 GB), so the smaller files save little memory, and on Apple Silicon they were not faster: the drafter's time is spent on its many small GPU steps, not on reading weights.
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hotshotco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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houlette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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howey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 ↗Llama3.1-Aloe-Beta-8B
HPAI-BSCLlama3.1-Aloe-Beta-8B is an **open healthcare LLM** achieving **state-of-the-art performance** on several medical tasks. Aloe Beta is made available in four model sizes: 7B, 8B, 70B, and 72B. All models are trained using the same recipe, on top of two different families of models: Llama3.1 and Qwen2.5. Aloe is trained on 20 medical tasks, resulting in a robust and versatile healthcare model. Evaluations show Aloe models to be among the best in their class. When combined with a RAG system (also released) the 7B and 8B version gets close to the performance of closed models like MedPalm-2, GPT4. With the same RAG system, Llama3.1-Aloe-Beta-70B and Qwen2.5-Aloe-Beta-72B outperforms those private alternatives, producing state-of-the-art results. **Aloe-8B-Beta** is the latest iteration in the **Aloe family**, building and improving on the success of its predecessor, Aloe-8B-Alpha. Beta more than triples the training data used by Alpha, for a total of **1.8B tokens**, including a wider variety of medical tasks and instructions (e.g., text summarization, explanation, diagnosis, text classification, treatment recommendation, ...).
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HPAI-BSC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 ↗Colossal-LLaMA-2-7b-base
hpcai-tech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 ↗grok-1
hpcai-tech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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hpcai-techWe design and implement **Open-Sora**, an initiative dedicated to **efficiently** producing high-quality video. We hope to make the model, tools and all details accessible to all. By embracing **open-source** principles, Open-Sora not only democratizes access to advanced video generation techniques, but also offers a streamlined and user-friendly platform that simplifies the complexities of video generation. With Open-Sora, our goal is to foster innovation, creativity, and inclusivity within the field of content creation. 📍 Since Open-Sora is under active development, we remain different branchs for different versions. The latest version is main. Old versions include: v1.0, v1.1, v1.2, v1.3. Our 11B model supports 256px and 768px resolution. Both T2V and I2V are supported by one model. 🤗 Huggingface 🤖 ModelScope.
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查看详情 ↗OpenSora-STDiT-v3
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查看详情 ↗OpenSora-VAE-v1.2
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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。