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Embeddings are the pooled last hidden state; the head is unused and left unquantised. Cosine similarity is scale-sensitive, so compare rankings rather than raw distances when validating this checkpoint. `WikipediaRetrievalMultilingual` from MTEB — retrieval over Wikipedia in 16 languages, scored by `ndcg_at_10`. Serving Qwen/Qwen3-Embedding-4B in bf16 leaves little room for KV cache on a small GPU: the weights take what the cache needs, and the context length has to be cut until it fits. Halving the weights gives that memory back — the same card serves a longer context without any other change.

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BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with Multilingual BERT as well as other (not BERT-based) models. All models use a vocabulary of about 31k BPE subwords constructed using SentencePiece and were trained for 2M steps. The following table shows some BETO results in the Spanish version of every task. We compare BETO (cased and uncased) with the Best Multilingual BERT results that we found in the literature (as of October 2019). The table also shows some alternative methods for the same tasks (not necessarily BERT-based methods). References for all methods can be found here.

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BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with Multilingual BERT as well as other (not BERT-based) models. All models use a vocabulary of about 31k BPE subwords constructed using SentencePiece and were trained for 2M steps. The following table shows some BETO results in the Spanish version of every task. We compare BETO (cased and uncased) with the Best Multilingual BERT results that we found in the literature (as of October 2019). The table also shows some alternative methods for the same tasks (not necessarily BERT-based methods). References for all methods can be found here.

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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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文本生成
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**On-device AAC language model optimized for Apple Watch.** Fine-tuned from SmolLM2-360M-Instruct using LoRA (rank=32) on a curated AAC clinical corpus. Runs entirely on-device via llama.cpp at ~570ms per response — no network required. Evaluated on a 300-case novel AAC evaluation suite covering 10 clinical categories. **Score: 100% (300/300)** at temperature=0.1. - **Symbol expansion**: Convert AAC symbol sequences to natural speech (70 test cases) - **Emergency communication**: Detect and express medical/safety emergencies (30 cases) - **Yes/No clarification**: Answer questions from symbol selections with appropriate polarity (40 cases) - **Intent classification**: SCERTS pragmatic function labeling — request, comment, protest, social, repair (25 cases) - **Multi-turn dialogue**: Contextual follow-up across conversation turns (30 cases) - **Clinical language rephrasing**: Person-first, trauma-informed terminology conversion (20 cases) - **Caregiver bridging**: Communication support between AAC user and care partners (20 cases) - **Vocabulary age-matching**: Developmentally appropriate word selection (25 cases) - **Predictive completion**: Symbol sequence prediction (30 cases) - **Emotion expression**: Nuanced emotional state communication (10 cases)

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dd999361

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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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。

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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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