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View details ↗Fidel2.0-1B-GGUF
4E-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 ↗LocoOperator-4B-LiteRTLM
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View details ↗roberta-large-NER
51la51. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training 5. Evaluation 6. Environmental Impact 7. Technical Specifications 8. Citation 9. Model Card Authors 10. How To Get Started With the Model The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is XLM-RoBERTa-large fine-tuned with the conll2003 dataset in English. The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text.
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View details ↗Vintern-1B-v2
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View details ↗Vintern-1B-v3_5
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View details ↗Vintern-3B-beta
5CD-AI**What's new in Vintern-3B-beta!** - **We successfully reproduced the training process of InternVL from scratch.** - The model is the result of integrating Qwen/Qwen2.5-3B-Instruct and InternViT-300M-448px through an MLP layer. - Trained with more than 10 Milion Vietnamese QnAs, Descriptions, and 10% English, Chinese Data from OpenGVLab/InternVL-Chat-V1-2-SFT-Data. We surpassed GPT-4o and are approaching Gemini 1.5 Pro on the MTVQA dataset for Vietnamese. The benchmark result in MTVQA from open_vlm_leaderboard. The current results are at a quite good level, and we are expanding the training set in English and other languages to approach models within a comparable parameter range.
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View details ↗Vintern-3B-R-beta
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View details ↗GLM-5.3-Flash-GGUF
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View details ↗Hy4-preview-GGUF
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View details ↗Kimi-K3-GGUF
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View details ↗An **NVFP4 (4-bit) Qwen3-VL-32B text encoder** for MiniMax-H3, made with `convert-to-quant`. It is a drop-in replacement for the H3 conditioning text encoder (ComfyUI `CLIPLoader`, type `minimax`). The H3 video weights are unchanged — only the text encoder is quantized to 4-bit, to bring the full H3 stack onto consumer GPUs. This encoder deliberately keeps more of the network in bf16 than a minimum-size 4-bit encoder does. The `--qwen_vlm` filter protects — at bf16 — the token embeddings, the **first and last language layers**, and the **entire vision tower**, quantizing only the middle language-layer linears to NVFP4. The result is 16.64 GiB, about 2 GiB above the most aggressively quantized community NVFP4 encoders (≈14.6 GiB, e.g. the AWQ-quantized encoder in `Comfy-Org/MiniMax-H3`), in exchange for leaving the precision-sensitive layers (embeddings, boundary layers, vision) untouched. It is a fidelity-first tradeoff: spend ~2 GiB more VRAM to keep the layers that most affect conditioning at full precision. Everything still fits a 24 GB card with offload. Everything is held constant except encoder precision — the bf16 encoder vs this NVFP4 encoder, same prompts, same seeds, same DiT — over a 16-clip paired set.
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View details ↗Qwen3-14B-GGUF
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View details ↗Qwen3-30B-A3B-GGUF
6blockAll quants made using the imatrix option with a bilingual (English + Chinese) and code-heavy calibration dataset, so low-bit quants keep more of their Chinese and coding ability than a English-only calibration would. The `.imatrix` file used to produce these quants is included in this repo, so anyone can reproduce or extend the quant set with the exact same importance matrix. The first thing to figure out is how big a model you can run. For that you need to know how much RAM and/or VRAM you have.
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View details ↗Qwen3-32B-GGUF
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