📃 **MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**,(EMNLP 2024, GitHub Repo) The model is based on Flan-T5-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** The performance of these models is evaluated on our new collected benchmark (unseen by our models during training), LLM-AggreFact, from 11 recent human annotated datasets on fact-checking and grounding LLM generations. MiniCheck-Flan-T5-Large outperform all exisiting specialized fact-checkers with a similar scale by a large margin (4-10% absolute increase) and is on par with GPT-4, but 400x cheaper. See full results in our work.
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The model is based on RoBERTA-Large that predicts a binary label - 1 for supported and 0 for unsupported. The model is doing predictions on the *sentence-level*. It takes as input a document and a sentence and determine whether the sentence is supported by the document: **MiniCheck-Model(document, claim) -> {0, 1}** MiniCheck-RoBERTa-Large is fine tuned from the trained RoBERTA-Large model from AlignScore (Zha et al., 2023) on 14K synthetic data generated from scratch in a structed way (more details in the paper). We also have other three MiniCheck model variants: - bespokelabs/Bespoke-Minicheck-7B (Model Size: 7B) - lytang/MiniCheck-Flan-T5-Large (Model Size: 0.8B) - lytang/MiniCheck-DeBERTa-v3-Large (Model Size: 0.4B)
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**Lythri** is a family of on-device language models built for emotional companionship. Instead of chasing math and coding scores, Lythri is trained to understand how people feel and to hold natural, multi-turn conversations, while staying small enough to run locally on a laptop or phone. Zero-shot comparison between Lythri-4B-A2B and Gemma 4 instruction-tuned baselines on three emotion benchmarks. Despite having only 2.3B active parameters, **Lythri-4B-A2B(4.6B, base on Gemma4-E2B-PT) comparable to Gemma4-E4B-IT(8B) on all three EQ benchmarks.** All benchmarks are evaluated with their official standard settings and in generative mode with chat template applied, reflecting real-world inference conditions. Think-tag outputs from model are stripped before answer extraction.
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This repository contains GGUF quantizations of Lythri-7B-A4B for use with llama.cpp and compatible apps. For model details, evaluation results and limitations, see the original model card. > **Note:** Lythri models are highly sensitive to quantization. We recommend using the highest-precision quantization your hardware allows. Lower-bit quantizations still work but are not recommended. To use a specific quantization, append its type to the repository name, for example `Lythri/Lythri-7B-A4B-GGUF:Q8_0`.
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The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.
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The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.
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🤗 MERT-v2-FullSong Music representations with full-song context 24 kHz mono · 632M parameters · 24 layers · 1,024 dimensions · 25 Hz 🎵 YuE2 project · 🚀 Quick start · 📊 MARBLE · 🎛️ Layer guide · 📄 arXiv · PDF · 📚 Citation **MERT-v2-FullSong is a bidirectional music encoder adapted to complete songs lasting 30–360 seconds.** Extract general-purpose music representations at the frame or recording level. Load with standard Hugging Face Transformers, using the familiar MERT workflow.
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