BaronLLM-v2-OffensiveSecurityLLM-Q8
AlicanKiraz0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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View details ↗DISCOVER & COMPARE
Explore AI models 84853
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View details ↗Kizagan-TTS-v1.0
AlicanKiraz0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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View details ↗Mizan-27B-Turkish-Legal-LLM-mlx-4Bit
AlicanKiraz0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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View details ↗Qwen3-14B-BaronLLM-v2-Q4_0-GGUF
AlicanKiraz0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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View details ↗Seneca-Cybersecurity-LLM-Q4_K_M-GGUF
AlicanKiraz0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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View details ↗Titus-CybersecurityLLM-v1.0-mlx-4Bit
AlicanKiraz0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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View details ↗haru
alice-noa-chan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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View details ↗haru_1.1
alice-noa-chan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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View details ↗haru_2
alice-noa-chan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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View details ↗voice-gender-classifier-onnx-q8-v2
Alice-Sabrina-IvyONNX export with dynamic q8 (UInt8, weight-only, per-channel) quantization of JaesungHuh/voice-gender-classifier, prepared for Transformers.js inference in browser and mobile contexts. **v2 of voice-gender-classifier-onnx-q8.** Same upstream weights, same export, same file layout, same size class — one change to the quantization recipe that recovers full FP32 accuracy (see next section). v1 remains available for reproducibility of results published against it. Used in Syrinx, an open-source voice-training tool. **MIT licensed — anyone is welcome to use this for any purpose under MIT terms**, not Syrinx-specific.
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View details ↗GLM-5.3-Flash-UNCENSORED-GGUF
AliceThirty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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View details ↗MiMo-V2.6-Flash-RL-UNCENSORED-gguf
AliceThirty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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View details ↗Qwen3-30B-A3B-MXFP8
AliEdalati97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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View details ↗f5-ita-test
alien79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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View details ↗F5-TTS-italian
alien79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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View details ↗Gemma4-E2B-it-Deepseek-V4-8000x
Alienstro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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View details ↗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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View details ↗Turkish-Asistant-gguf
AlienTurk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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View details ↗Roberta-fa-en-ner
AliFartoutThis markdown presents a Robustly Optimized BERT Pretraining Approach (RoBERTa) model trained on a combination of two diverse datasets for two languages: English and Persian. The English dataset used is CoNLL 2003, while the Persian dataset is PEYMA-ARMAN-Mixed, a fusion of the "PEYAM" and "ARMAN" datasets, both popular for Named Entity Recognition (NER) tasks. Data Preparation: Cleaning, aligning, and mixing data from the two datasets. Data Loading: Loading the prepared data for subsequent processing. Tokenization: Utilizing tokenization to prepare the text data for model input. Token Splitting: Handling token splitting (e.g., "jack" becomes "_ja _ck") and using "-100" for optimization and ignoring certain tokens. Model Reconstruction: Adapting the RoBERTa model for token classification in NER tasks. Model Training: Training the reconstructed model on the combined dataset and evaluating its performance. The model's performance, as shown in the table below, demonstrates promising results: | Epoch | Training Loss | Validation Loss | F1 | Recall | Precision | Accuracy | |:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:| | 1 | 0.072600 | 0.038918 | 89.5% | 0.906680 | 0.883703 | 0.987799 | | 2 | 0.027600 | 0.030184 | 92.3% | 0.933840 | 0.915573 | 0.991334 | | 3 | 0.013500 | 0.030962 | 94% | 0.946840 | 0.933740 | 0.992702 | | 4 | 0.006600 | 0.029897 | 94.8% | 0.955207 | 0.941990 | 0.993574 | By following this approach, you can seamlessly access and incorporate the trained multilingual NER model into various Natural Language Processing tasks.
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View details ↗indobert_sentiment_news
aliffiaaliffia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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View details ↗qwen2.5-1.5b-legal-id-grpo
alifkha7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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View details ↗zephyr-7b-sft-full
alignment-handbook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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View details ↗Mistral-nemo-3b-unhealed
Alignment-Lab-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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View details ↗No matching models
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