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This is ruRoberta-large model finetuned on ru_go_emotions dataset for multilabel classification. Model can be used to extract all emotions from text or detect certain emotions. Thresholds are selected on validation set by maximizing f1 macro over all labels. The quality of the model varies greatly across all classes (look at the table with metrics below). There are classes like amusement, gratitude, where the model shows high recognition quality, and classes that pose difficulties for the model - grief, relief, that do have much fewer examples in the training data. Also ONNX version of model and INT8 quantized model is available. information about them is posted in the last sections.

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Format repackaging of huihui-ai's abliterated Swift-1.5 GSQ-RCO GGUF series into single-file **`.ninfer`** containers for the NInfer engine. **The abliteration is not ours.** The refusal-direction ablation was performed by huihui-ai on the Swift-1.5 tiers of that GGUF repository; this repository only repackages their GGUF blocks into the NInfer container format. See Attribution and the usage warnings carried over below. Tiers are published one at a time; this repository grows as each conversion completes. **All four tiers are now published.**

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Format repackaging of huihui-ai's abliterated Ternary-Bonsai GGUF into a single-file **`.ninfer`** container for the NInfer engine. **The abliteration is not ours.** huihui-ai ablated layers 22–52 (0-based) of the PrismML Ternary-Bonsai-2-27B weights; this repository only repackages them. See Attribution and the usage warnings carried over below. huihui-ai's published `Ternary-Bonsai-PQ2_0.gguf` has 54 tensors that lost their ternary encoding (`ffn_down.weight` ×31 and `ssm_out.weight` ×23 became `Q3_K`/`Q2_K`) — they had to de-quantize those tensors to ablate them. A mixed ternary/k-quant file cannot enter the `.ninfer` container, whose runtime only reads `t2_g128_fp16`.

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Swift-1.5 Qwen3.8-27B is UkisAI's Swift 1.5 fine-tune of Qwen3.8-27B, quantized by UkisAI as the **GSQ-RCO** GGUF series (mixed-precision per-tensor allocation, optional MTP-head package). This repository repackages that GGUF series into single-file **`.ninfer`** containers for the NInfer engine. Four tiers are published, all built from the `-mtp` GGUF packages: Pick by weight budget: **IQ2_XS** when VRAM is tightest, **IQ3_S** when fidelity matters most. Note the IQ3_S tier is ~11.9 GiB of weights plus KV cache, so it needs a 16 GB+ card or offloading — the three smaller tiers fit smaller cards. All four carry the identical component set — only the source GGUF's per-tensor quantization allocation differs.

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Swift-Bonsai-2 is UkisAI's reasoning-efficient derivative of Prism ML's Ternary Bonsai 2 27B, packaged here for the NInfer engine as `PQ2_0_G128` and `PTQ1_0_G128` artifacts. The ternary codes were moved **byte-for-byte** out of the source GGUF into the NInfer container — nothing was dequantized and requantized, so there is no second quantization loss. Both files are in the root of this repository — download them from the **Files** tab. Each file is a complete container — text tower, vision tower, MTP head, proposal head, tokenizer, chat template and media-processor resources. No adapter file, patch, or extra flag is needed. **No DFlash2 adapter.**

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UniverSat

g-astruc

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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gwen-tts-0.6B

g-group-ai-lab

**Key highlights:** - Clone any voice with just a few seconds of reference audio - Natural and expressive Vietnamese voice cloning - Finetuned from Qwen3-TTS-0.6B on ~1,000 hours of Vietnamese audio data crawled from TikTok > **Note:** For best quality, proactively apply **TTS text normalization** (numbers, symbols, abbreviations, etc.) and **split input into chunks** before passing to the model. Each speaker shows the **reference audio** (voice input) and the **generated inference audio** (output).

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语音合成
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d_p7

g0001

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

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COST EXPLORER

好模型，也要算好成本。

费用分析

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

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

选型与常见问题
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。

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