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AI Models 2119
- Cosmos-Predict2-14B-Sample-GR00T-Dreams-DROID↗
NVIDIA
- TinyLlama_v1.1↗
TinyLlama
- **Codebase:** github.com/jzhang38/TinyLlama - **Technical Report:** arxiv.org/pdf/2401.02385 We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. In this project, rather than only training a single TinyLlama model, we first train TinyLlama on a corpus of 1.5 trillion tokens to obtain foundational language capabilities. Subsequently, we take this model and turn it into three different models by continual pre-training with three distinct data sampling. For a visual representation of this process, please refer to the figure below.
- salamandra-7b-instruct-2606↗
BSC-LT
> [!NOTE] > **WARNING:** Although this model has undergone safety and value alignment, it may still occasionally generate unintended or undesired outputs. > > **Sampling Parameters:** For optimal performance, we recommend using temperatures close to zero (0 - 0.2). Additionally, we advise against using any type of repetition penalty, as from our experience, it negatively impacts instructed model's responses. The Salamandra-7b-instruct model is an instructed variant of a context-extended base Salamandra-7b model, which was pre-trained from scratch on 9.83 trillion tokens of carefully curated data spanning 35 European languages (including code). This instructed version is optimized to follow user prompts and engage in dialogue. It supports a broad range of languages (e.g. Spanish, Catalan, Basque, English, etc.) and is capable of text generation, translation, summarization, and question-answering in these languages. This version has also gone through a preliminary alignment phase for helpfulness and safety with synthetically generated preference pairs. The model has been made openly available under the Apache 2.0 license for the benefit of society. Through this open release, ALIA seeks to accelerate scientific progress and innovation by enabling the scientific community and society at large to use, study, evaluate, adapt, further develop, and build upon the model. This open approach promotes collaboration, transparency, and the responsible development of artificial intelligence, helping to advance knowledge, drive technological innovation, and contribute to the public interest. The post-training datasets are publicly available in ALIA-SFT, ALIA-SFT-tools, ALIA-DPO-helpulness, and ALIA-DPO-safety.
- wav2vec2-conformer-rope-large-960h-ft↗
facebook
Wav2Vec2 Conformer with rotary position embeddings, pretrained and **fine-tuned on 960 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Paper**: fairseq S2T: Fast Speech-to-Text Modeling with fairseq **Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino
- Samantha-PL-AG-Mistral-7B-v0.2-GGUF↗
mradermacher
- Qwen3.5-2B-Opus46-Reasoning-GGUF↗
supersamdev
- SauerkrautLM-Reason-EuroColBERT↗
VAGOsolutions
**Knowledge Distillation** from strong synthetic data (200k samples generated with Qwen/Qwen3-32B-AWQ and scored by a high-performing reranker). **Robust 210M parameter architecture** optimized for multilingual reasoning-focused retrieval without compression trade-offs. With **210 million parameters** – that's **less than 1/33rd the size** of some competing models – SauerkrautLM-Reason-EuroColBERT achieves or exceeds the performance of: - Models with **over 7 billion parameters** (33× larger than ours) - Proprietary API-based solutions from major tech companies - Specialized reasoning models like ReasonIR-8B (38× larger) This balanced architecture provides exceptional performance while remaining deployable on standard infrastructure.
- qwen2.5-vl-7b-instruct↗
阿里巴巴 · Qwen
--- license: apache-2.0 language: - en pipeline_tag: image-text-to-text tags: - multimodal library_name: transformers --- In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments.
- GLiNER2.5-multi-Decide↗
fastino
**The 287M multilingual classification model in the GLiNER2.5 family.** Pass any label set at call time: intent, routing, sentiment, priority, policy, and multi-label tags, in a single forward pass. No prompt template. No generated tokens. Load it with `AutoExtractor` and ship it locally. Use it when the text is not English. A single call can score several heads at once. Single-label tasks return one string. Multi-label tasks return every label above the threshold. Exact-match accuracy on `fastino/fast-decisions`: 17 domains, 300 held-out examples each, with the same text and candidate labels for every model.
- Splade_PP_en_v2↗
prithivida
This work stands on the shoulders of 2 robust researches: Naver's From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective paper and Google's SparseEmbed. Props to both the teams for such a robust work. **Beginner ?** expand this. **Expert in Sparse & Dense representations ?** feel free skip to next section 2, Lexical search with BOW based sparse vectors are strong baselines, but they famously suffer from vocabulary mismatch problem, as they can only do exact term matching. Here are the pros and cons: