Chinese-Llama-2-7b
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linkup-sparseup-embed-v1
Linkup-Platform> **TL;DR:** SPARSEUP is a strong and efficient sparse retriever trained on the same backbone family and fine-tuning data as DenseOn / LateOn (149M, ModernBERT). Strongest public vocabulary-based sparse encoder we know of under 150M on BEIR-13 (56.4 nDCG@10). Apache 2.0. Use it! LightOn released open data and a recipe to train **DenseOn** (single-vector) and **LateOn** (late interaction). **SPARSEUP** fills the missing slot: a learned sparse embedding model, so the three architectures can be compared with backbone and data held fixed. The model is initialized from `LateOn-unsupervised` with ModernBERT’s MLM `head` / `decoder.bias` grafted on (`decoder.weight` stays tied to LateOn embeddings). Contrastive fine-tuning on LightOn’s mixture (7 hard negatives sampled from 50, plus in-batch). No cross-encoder distillation. Three extra knobs *vs* vanilla SPLADE-like model: 1. **`logit_shift=15`** — `log(1 + ReLU(x − 15))` so ReLU support starts sparse 2. **`position_top_k=12`** — each token keeps 12 vocab dims max before pooling (expansion budget) 3. **`vocab_fold=case_space`** — fold case / `Ġ` surface forms onto one id after pooling (avoid duplication of surface forms = improve efficiency). Voc goes from 50*k* to ~34*k*.
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View details ↗Chinese-LLaMA-2-7B-hf
Linly-AI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 ↗babylm-original-1234-gpt2-small
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View details ↗Flux2-Klein-Delight-LoRA
linoytsThese are linoyts/Flux2-Klein-Delight-LoRA DreamBooth LoRA weights for black-forest-labs/FLUX.2-klein-base-9B. For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
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View details ↗Krea2-emoji-LoRA
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View details ↗Qwen-Image-2.1-DMD2-2steps
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View details ↗Linq-Embed-Mistral
Linq-AI-ResearchLinq-Embed-Mistral has been developed by building upon the foundations of the E5-mistral-7b-instruct and Mistral-7B-v0.1 models. We focus on improving text retrieval using advanced data refinement methods, including sophisticated data crafting, data filtering, and negative mining guided by teacher models, which are highly tailored to each task, to improve the quality of the synthetic data generated by LLM. These methods are applied to both existing benchmark dataset and highly tailored synthetic dataset generated via LLMs. Our efforts primarily aim to create high-quality triplet datasets (query, positive example, negative example), significantly improving text retrieval performance. Linq-Embed-Mistral performs well in the MTEB benchmarks (as of May 29, 2024). The model excels in retrieval tasks, ranking **`1st`** among all models listed on the MTEB leaderboard with a performance score of **`60.2`** . This outstanding performance underscores its superior capability in enhancing search precision and reliability. The model achieves an average score of **`68.2`** across 56 datasets in the MTEB benchmarks, making it the highest-ranking publicly accessible model and third overall. (Please note that NV-Emb-v1 and voyage-large-2-instruct, ranked 1st and 2nd on the leaderboard as of May 29, reported their performance without releasing their models.) This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:
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View details ↗Scarlett-Opus
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View details ↗Scarlett-Opus-oQ8e-MLX
Linux4life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 ↗G9v3-39A5B-GGUF
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View details ↗UniMate
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View details ↗ALoDLM-8B-GGUF
liodon-ai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 ↗ALoDLM-8B-imatrix-GGUF
liodon-ai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 ↗ALoDLM-8B-ONNX
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View details ↗anon-1b-ONNX
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View details ↗AstaBrief_8B-ONNX
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View details ↗baguettotron-600m-ONNX
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View details ↗bloom-560m-FP8
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View details ↗colgrep-agent-2B-imatrix-GGUF
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View details ↗DeepHat-V1-7B-FP8
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