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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.

Embeddings & similarity
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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.

Fill-mask
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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.

Fill-mask
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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)

Text generation
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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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Image generation
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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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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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