google_gemma-4-E4B-it-GGUF
indiansatoshi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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Explore AI models 84999
bert-base-cantonese
indiejoseph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 ↗indobart-v2
indobenchmark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 ↗indobert-base-p1
indobenchmarkIndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
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View details ↗indobert-base-p2
indobenchmarkIndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
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View details ↗indobert-large-p1
indobenchmarkIndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. IndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
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View details ↗indobert-large-p2
indobenchmarkIndoBERT is a state-of-the-art language model for Indonesian based on the BERT model. The pretrained model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. IndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
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View details ↗indobert-lite-base-p1
indobenchmark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 ↗indobert-base-uncased
indolemWe trained the model for 2.4M steps (180 epochs) with the final perplexity over the development set being 3.97 (similar to English BERT-base). This IndoBERT was used to examine IndoLEM - an Indonesian benchmark that comprises of seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse. The paper is published at the 28th COLING 2020. Please refer to https://indolem.github.io for more details about the benchmarks.
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View details ↗indobertweet-base-uncased
indolemFajri Koto, Jey Han Lau, and Timothy Baldwin. _IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization_. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (**EMNLP 2021**), Dominican Republic (virtual). In this paper, we show that initializing domain-specific vocabulary with average-pooling of BERT subword embeddings is more efficient than pretraining from scratch, and more effective than initializing based on word2vec projections. We crawl Indonesian tweets over a 1-year period using the official Twitter API, from December 2019 to December 2020, with 60 keywords covering 4 main topics: economy, health, education, and government. We obtain in total of **409M word tokens**, two times larger than the training data used to pretrain IndoBERT. Due to Twitter policy, this pretraining data will not be released to public.
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View details ↗gpt2
indonesian-nlp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 ↗wav2vec2-indonesian-javanese-sundanese
indonesian-nlpThis is the model built for the project Multilingual Speech Recognition for Indonesian Languages. It is a fine-tuned facebook/wav2vec2-large-xlsr-53 model on the Indonesian Common Voice dataset, High-quality TTS data for Javanese - SLR41, and High-quality TTS data for Sundanese - SLR44 datasets. We also provide a live demo to test the model. The model can be evaluated as follows on the Indonesian test data of Common Voice.
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View details ↗wav2vec2-large-xlsr-indonesian
indonesian-nlpThis is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned facebook/wav2vec2-large-xlsr-53 model on the Indonesian Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be evaluated as follows on the Indonesian test data of Common Voice. The Common Voice `train`, `validation`, and synthetic voice datasets were used for training.
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View details ↗DeepSeek-V4.1-Flash-1017-XQ-Uncensored
IndrasMirror**A 552B-parameter MoE running on one gaming PC: RTX 4090 (24 GB) + 90 GB RAM + NVMe** Runs on **Xolotl** (formerly Strata-DSV4.1), an engine built for exactly this model and this class of machine, on top of Strata. **Builds:** **V4** (`v4/`, 163 GB, recommended) · V3 (`v3/`, 163 GB) · V2 (`v2/`, 152 GB)
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View details ↗DeepSeek-V4.1-Flash-GSQ-Uncensored
IndrasMirrorRuns on **Xolotl** (formerly Strata-DSV4.1), an engine built for exactly this model and this class of machine, on top of Strata. > **Update 2026-10-17:** the engine is now **Xolotl** (Strata-DSV4.1 renamed; > old links redirect) - one tree that also serves GLM-5.3-Flash and the Qwen3.8 models. Measured through the server > with thinking on (32K budget): **HumanEval 21/21, tool calls 10/10, agentic 3/3, decode 26.8 tok/s**. Agent turns got > cheaper: small-turn prefill -12% (bit-identical logits) and a **conversation-adaptive expert arena** (on by default) > that moves the conversation's NVMe experts into RAM between turns: -21..-30% NVMe reads, a +180-token turn on a 6K > context 8.5 -> 7.6 s. Build with `build-xolotl` (below). > **Update 2026-10-15:** the engine's defaults are now the **coding-safe route bias ×0.5** (HumanEval 20/21 and tool calls > 10/10, vs 16/21 at the old full bias) with DSpark speculative decoding **in the server** (exact speculative sampling), > a confidence-gated verify (prose 9.5 → 17.3, code 18.8 → 25.5, python 26.6 → 29.1 tok/s), a **bit-exact 4-bit KV cache** > (`--kv-fp4`, +1.1 GiB VRAM at 512K) and ~2× faster conversational turns (+1000-token turn 40 → 20 s at 512K). > Details: the repo README "Latest".
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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 ↗ponyRealism_v22MainVAE
Ine007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 ↗ponyRealism_V23ULTRA
Ine007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 ↗waiIllustriousSDXL_v160
Ine007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 ↗waiNSFWIllustrious_v100
Ine007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 ↗waiNSFWIllustrious_v140
Ine007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 ↗WhisperLv3-FT
inesc-id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 ↗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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