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View details ↗DISCOVER & COMPARE
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View details ↗GLM-5.3-Flash-UD-Q4_K_XL-layers
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View details ↗Kimi-K3-UD-Q4_K_XL-layers
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View details ↗LLaMA-7B converted to work with Transformers/HuggingFace. This is under a special license, please see the LICENSE file for details. **Model type** LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters. **Paper or resources for more information** More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/.
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View details ↗gpt2-ptmap
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BAGEL-7B-MoT
字节跳动> We present **BAGEL**, an open‑source multimodal foundation model with 7B active parameters (14B total) trained on large‑scale interleaved multimodal data. BAGEL outperforms the current top‑tier open‑source VLMs like Qwen2.5-VL and InternVL-2.5 on standard multimodal understanding leaderboards, and delivers text‑to‑image quality that is competitive with strong specialist generators such as SD3. Moreover, BAGEL demonstrates superior qualitative results in classical image‑editing scenarios than the leading open-source models. More importantly, it extends to free-form visual manipulation, multiview synthesis, and world navigation, capabilities that constitute "world-modeling" tasks beyond the scope of previous image-editing models. This repository hosts the model weights for **BAGEL**. For installation, usage instructions, and further documentation, please visit our GitHub repository. BAGEL adopts a Mixture-of-Transformer-Experts (MoT) architecture to maximize the model’s capacity to learn from richly diverse multimodal information. Following the same principle of capacity maximization, it utilizes two separate encoders to capture pixel-level and semantic-level features of an image. The overall framework follows a Next Group of Token Prediction paradigm, where the model is trained to predict the next group of language or visual tokens as a compression target.
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View details ↗gemma-4-31B-it-assistant-fp8
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View details ↗gemma-4-31B-it-text-fp8
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View details ↗MiniMax-H3
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View details ↗Qwen3.6-35B-A3B-abliterated-v4
BahushruthUncensored version of Qwen/Qwen3.6-35B-A3B with refusal behavior removed via **abliteration** (norm-preserving orthogonalization). > **GGUF quantizations:** Bahushruth/Qwen3.6-35B-A3B-abliterated-v4-GGUF > > **Blog post:** Abliteration: Uncensoring LLMs via Weight Surgery Abliteration identifies the "refusal direction" in the model's residual stream — the linear direction that activates when the model decides to refuse — and surgically removes it from all output projection weights using norm-preserving orthogonalization.
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View details ↗Qwen3.6-35B-A3B-abliterated-v4-GGUF
BahushruthGGUF quantizations of Bahushruth/Qwen3.6-35B-A3B-abliterated-v4 for llama.cpp, Ollama, LM Studio, KoboldCPP, and other GGUF-compatible runtimes. Uncensored model — refusal behavior removed via norm-preserving abliteration (0% refusal, full capability preservation). See the base model card for method details. > **Blog post:** Abliteration: Uncensoring LLMs via Weight Surgery
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View details ↗Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS
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View details ↗Baichuan-13B-Base
baichuan-inc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 ↗Baichuan-13B-Chat
baichuan-incBaichuan-13B 是由百川智能继 Baichuan-7B 之后开发的包含 130 亿参数的开源可商用的大规模语言模型，在权威的中文和英文 benchmark 上均取得同尺寸最好的效果。本次发布包含有预训练 (Baichuan-13B-Base) 和对齐 (Baichuan-13B-Chat) 两个版本。Baichuan-13B 有如下几个特点： 1. **更大尺寸、更多数据**：Baichuan-13B 在 Baichuan-7B 的基础上进一步扩大参数量到 130 亿，并且在高质量的语料上训练了 1.4 万亿 tokens，超过 LLaMA-13B 40%，是当前开源 13B 尺寸下训练数据量最多的模型。支持中英双语，使用 ALiBi 位置编码，上下文窗口长度为 4096。 2. **同时开源预训练和对齐模型**：预训练模型是适用开发者的“基座”，而广大普通用户对有对话功能的对齐模型具有更强的需求。因此本次开源我们同时发布了对齐模型（Baichuan-13B-Chat），具有很强的对话能力，开箱即用，几行代码即可简单的部署。 3. **更高效的推理**：为了支持更广大用户的使用，我们本次同时开源了 int8 和 int4 的量化版本，相对非量化版本在几乎没有效果损失的情况下大大降低了部署的机器资源门槛，可以部署在如 Nvidia 3090 这样的消费级显卡上。 4. **开源免费可商用**：Baichuan-13B 不仅对学术研究完全开放，开发者也仅需邮件申请并获得官方商用许可后，即可以免费商用。 Baichuan-13B-Chat is the aligned version in the Baichuan-13B series of models, and the pre-trained model can be found at Baichuan-13B-Base.
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View details ↗Baichuan-7B
baichuan-incBaichuan-7B是由百川智能开发的一个开源的大规模预训练模型。基于Transformer结构，在大约1.2万亿tokens上训练的70亿参数模型，支持中英双语，上下文窗口长度为4096。在标准的中文和英文权威benchmark（C-EVAL/MMLU）上均取得同尺寸最好的效果。 如果希望使用Baichuan-7B（如进行推理、Finetune等），我们推荐使用配套代码库Baichuan-7B。 Baichuan-7B is an open-source large-scale pre-trained model developed by Baichuan Intelligent Technology. Based on the Transformer architecture, it is a model with 7 billion parameters trained on approximately 1.2 trillion tokens. It supports both Chinese and English, with a context window length of 4096. It achieves the best performance of its size on standard Chinese and English authoritative benchmarks (C-EVAL/MMLU).
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View details ↗Baichuan-M1-14B-Instruct
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View details ↗Baichuan-M2-32B
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