MiniMax-H3
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View details ↗DISCOVER & COMPARE
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SeedVR2-GGUF
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View details ↗tiny-agent-gguf
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View details ↗PolyOpus
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View details ↗- **Description**: EasyRec is a series of language models designed for recommendations, trained to match the textual profiles of users and items with collaborative signals. - **Usage**: You can use EasyRec to encode user and item text embeddings based on the textual profiles that reflect their preferences for various recommendation scenarios. - **Evaluation**: We evaluate the performance of EasyRec in: (i) Text-based zero-shot recommendation and (ii) Text-enhanced collaborative filtering. - **Finetuned from model:** EasyRec is finetuned from RoBERTa within English. For details please refer to our 💻[GitHub Code] and 📖[Paper]. Here is an example code snippet to utilize EasyRec for encoding **text embeddings** based on user and item profiles for recommendations.
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View details ↗instructor-base
hkunlpWe introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! * 01/21: We released a new checkpoint trained with hard negatives, which gives better performance. * 12/21: We released our paper, code, checkpoint and project page! Check them out! Then you can use the model like this to calculate domain-specific and task-aware embeddings:
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View details ↗instructor-large
hkunlpWe introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks (MTEB leaderboard)! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! * 12/28: We released a new checkpoint trained with hard negatives, which gives better performance. * 12/21: We released our paper, code, checkpoint and project page! Check them out! Then you can use the model like this to calculate domain-specific and task-aware embeddings:
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View details ↗instructor-xl
hkunlpWe introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
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View details ↗EdgeRock-4B-GGUF
hkust-aaron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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View details ↗EdgeRock-4B-MLX-6bit-g64
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View details ↗EdgeRock-4B-ONNX-Q4-CPU-g32
hkust-aaron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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View details ↗EdgeRock-4B-ONNX-Q4-g32
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View details ↗EdgeRock-4B-ONNX-Q8
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View details ↗drkernel-8b
hkust-nlp`hkust-nlp/drkernel-8b` is a Qwen3-8B-based model specialized for GPU kernel generation and optimization (especially Triton) in the DR.Kernel framework. It is trained for iterative optimization with execution feedback from KernelGYM, rather than single-shot code generation only. 1. Cold-start SFT - Dataset: `hkust-nlp/drkernel-coldstart-8k` - Multi-turn trajectory warm-up for kernel generation/refinement 2. Multi-turn RL - Train dataset: `hkust-nlp/drkernel-rl-data` - Validation dataset: `hkust-nlp/drkernel-validation-data` (KernelBench Level 2 validation split) - Core methods: TRLOO + MRS + PR + PRS - Execution environment: KernelGYM with compilation/correctness/performance/profiling feedback
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View details ↗drkernel-8b-coldstart
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View details ↗Qwen-2.5-1.5B-SimpleRL-Zoo
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View details ↗Qwen-2.5-7B-SimpleRL-Zoo
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View details ↗WebExplorer-8B
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View details ↗Llasa-1B
HKUSTAudio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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View details ↗Llasa-1B-Multilingual
HKUSTAudio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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View details ↗Llasa-8B
HKUSTAudio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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View details ↗VidMuse
HKUSTAudio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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View details ↗xcodec2
HKUSTAudio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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View details ↗xcodec2-hf
HKUSTAudioThe X-Codec2 model was proposed in Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis. X-Codec2 is a neural audio codec designed to improve speech synthesis and general audio generation for large language model (LLM) pipelines. It extends the original X-Codec by refining how semantic and acoustic information is integrated and tokenized, enabling efficient and high-fidelity audio representation. About its architecture: - **Unified Semantic-Acoustic Tokenization**: X-Codec2 fuses outputs from a semantic encoder (e.g., Wav2Vec2-BERT) and an acoustic encoder into a single embedding, capturing both high-level meaning (e.g., text content, emotion) and low-level audio details (e.g., timbre). - **Single-Stage Feature Scalar Quantization (FSQ)**: Unlike the multi-layer residual VQ in most approaches (e.g., DAC, EnCodec, X-Codec, Mimi), X-Codec2 uses a single-layer of Feature Scalar Quantization (FSQ) for stability and compatibility with causal, autoregressive LLMs. - **Transformer-Friendly Design**: The 1D token structure of X-Codec2 naturally aligns with the autoregressive modeling in LLMs like LLaMA, improving training efficiency and downstream compatibility.
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