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This is a Cross Encoder model finetuned from jinaai/jina-reranker-v2-base-multilingual using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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We introduce Latxa 3.1 70B Instruct, an instructed version of Latxa. This new Latxa is based on Llama-3.1 (Instruct), which we trained on our Basque corpus (Etxaniz et al., 2024) comprising 4.3M documents and 4.2B tokens using language adaptation techniques (paper in preparation). Our preliminary experimentation shows that Latxa 3.1 70B Instruct outperforms Llama-3.1-Instruct by a large margin on Basque standard benchmarks, and particularly, on chat conversations. In addition, we organized a public arena-based evaluation, on which Latxat competed against other baselines and proprietary models such as GPT-4o and Claude Sonnet. The results showed that Latxa ranked 3rd, just behind Claude and GPT-4 and above all the other same-size competitors. The official paper is coming soon. Latxa is a family of Large Language Models (LLM) based on Meta’s LLaMA models. Current LLMs exhibit incredible performance for high-resource languages such as English, but, in the case of Basque and other low-resource languages, their performance is close to a random guesser. These limitations widen the gap between high- and low-resource languages when it comes to digital development. We present Latxa to overcome these limitations and promote the development of LLM-based technology and research for the Basque language. Latxa models follow the same architecture as their original counterparts and were further trained in Latxa Corpus v1.1, a high-quality Basque corpora.
We introduce Latxa 3.1 8B Instruct, an instructed version of Latxa. This new Latxa is based on Llama-3.1 (Instruct), which we trained on our Basque corpus (Etxaniz et al., 2024) comprising 4.3M documents and 4.2B tokens using language adaptation techniques (paper in preparation). > [!WARNING] > DISCLAIMER > > This model is still under development. > Further training details will be released with the corresponding research paper in the near future. Our preliminary experimentation shows that Latxa 3.1 8B Instruct outperforms Llama-3.1-Instruct by a large margin on Basque standard benchmarks, and particularly, on chat conversations. In addition, we organized a public arena-based evaluation, on which Latxat competed against other baselines and proprietary models such as GPT-4o and Claude Sonnet. The results showed that Latxa ranked 3rd, just behind Claude and GPT-4 and above all the other same-size competitors. The official paper is coming soon. Latxa is a family of Large Language Models (LLM) based on Meta’s LLaMA models. Current LLMs exhibit incredible performance for high-resource languages such as English, but, in the case of Basque and other low-resource languages, their performance is close to a random guesser. These limitations widen the gap between high- and low-resource languages when it comes to digital development. We present Latxa to overcome these limitations and promote the development of LLM-based technology and research for the Basque language. Latxa models follow the same architecture as their original counterparts and were further trained in Latxa Corpus v1.1, a high-quality Basque corpora.
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One encoder model that replaces your entire guardrail stack: safety classification, PII detection, adversarial attack detection, intent and tone analysis — all in a single forward pass. **307M params · GLiNER2 · uniencoder · multilingual deberta v3 · zero-shot classification, NER and more · no LLM required** GLiNER Guard Omni fine-tunes fastino/gliner2-multi-v1 on our guardrail taxonomy while preserving its multilingual zero-shot generalization. You get GLiNER Guard's safety understanding on top of the base model's ability to handle labels and domains beyond the training set — so you can define custom policies with nothing but natural language descriptions.
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Multilingual E5 Text Embeddings: A Technical Report. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model is initialized from xlm-roberta-base and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
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WaifuGemma4-26b-a4b-v1 is a role-play model: Gemma 4 26B-A4B (25.2B total, 3.8B active) post-trained with GRPO against a reward model learned from 1.2M blind votes cast by real users on the HiWaifu Arena. In blind head-to-head battles it splits votes evenly with GLM-5.1 and beats the untuned Gemma 4 26B-A4B-it 59.7% of the time. The full training story, arena results and response comparisons are on the model card. This repository holds the **weighted/imatrix** quantizations. Static quants and the vision projector (`mmproj`) are in WaifuGemma4-26b-a4b-v1-GGUF. Compared with the static quants, the imatrix versions cut the divergence from bf16 roughly in half at 4 bits: i1-Q4_K_M has a mean KLD of 0.069425 against 0.150663 for static Q4_K_M at the same 16.8 GB, and i1-IQ4_XS at 13.9 GB beats static Q4_K_M at 16.8 GB. Any recent llama.cpp works (the GGUFs were made with build `4fea119`, September 2026). The model was trained and evaluated in non-thinking mode; pass `--reasoning-budget 0` so the template inserts the empty thought channel the model expects.
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