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View details ↗ModerationBERT-En-02
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View details ↗girlslike
ifmylove2011以下为上传至 Hugging Face 的图片示例，每张图展示对应的 LoRA 名。 个别lora有多个版本，但只要名字对得上就是同一个人。 小图上方各自有用于生成此图的模型名，少则2、3个，多则5、6个，都是兼容性较好的模型。 Below are image samples uploaded to Hugging Face, each showcasing a specific LoRA model. Some LoRAs have multiple versions, but as long as the names match, they refer to the same person. Above each image, you'll find the model names used to generate it — usually 2 to 3, sometimes up to 5 or 6 — all of which are highly compatible with the LoRA.
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View details ↗girlslike-krea2
ifmylove2011用于**测试**基于krea2_raw int8训练的人物 LoKR / LoRA，仅代表个人审美与训练效果。实际生成结果仅供测试，**请勿用于**冒充、欺骗或其他不当用途。 （*存疑，某些中文概念确实可以准确理解，但也不要因此对krea2的中文理解能力抱有过高的期待，目前只能说比之前的flux系列有较大进步，实际效果有待商榷，比如flux系列的老问题——krea2仍然会把中文的"桃子"大概率画成苹果*） krea2 turbo fp8； 8步； euler + beta|beta57； cfg=1； lora权重1.0； 分辨率960*1280； 中|英文提示词； 无其他lora参与。
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View details ↗girlslike-qweni
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ifmylove2011用于**测试**基于 Z-Image Base/Turbo BF16 训练的人物 LoKR / LoRA，仅代表个人审美与训练效果。实际生成结果仅供测试，**请勿用于**冒充、欺骗或其他不当用途。 后缀为**zi**代表基于z-image base训练，**zit**则代表基于turbo训练，zi在base与turbo模型中**均可**使用，zit则**只能**在turbo中使用。 z-image base训练尚无较为通用的可行方案，目前本zi系列选择的方案是全秩lokr，可在与turbo模型配合使用时以权重1.0出图，也能适当**增加**权重以增强相似性，最好不要超过1.5。
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View details ↗--- language: gl datasets: - OpenSLR 77 - mozilla-foundation common_voice_8_0 metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Galician wav2vec2-large-xlsr-galician results: - task: name: Speech Recognition type: automatic-speech-recognition dataset_1: name: OpenSLR type: openslr args: gl dataset_2: name: mozilla-foundation type: common voice args: gl metrics: - name: Test WER type: wer value: 7.12 --- Based on the facebook/wav2vec2-large-xlsr-53 self-supervised model Fine-tune with audio labelled from OpenSLR and Mozilla Common_Voice (both datasets previously refined)
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View details ↗This model has been quantized using llm-compressor v0.10.1.dev31+geb49917e (just after Qwen3.5 support was merged) and transformers v5.3.0. It is based on the official example with a few modifications (see next section). The sequence length has been increased from 4096 to 8192 and the number of samples from 256 to 1024. The 1024 samples come from 4 differents datasets: * 256 general conversation samples (UltraChat) * 256 math reasoning samples (GSM8K) * 256 code samples (CodeAlpaca) * 256 multilingual samples (Aya)
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