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AI Models 449

• Huihui-gpt-oss-20b-BF16-abliterated↗

  huihui-ai

  This is an uncensored version of unsloth/gpt-oss-20b-BF16 created with abliteration (see remove-refusals-with-transformers to know more about it). You can use this model in your applications by loading it with Hugging Face's `transformers` library:

• Bee-8B-RL↗

  Open-Bee

  We introduce **Bee-8B**, a new state-of-the-art, fully open 8B Multimodal Large Language Model (MLLM) designed to close the performance gap with proprietary models by focusing on data quality. Bee-8B is trained on our new **Honey-Data-15M** corpus, a high-quality supervised fine-tuning (SFT) dataset of approximately 15 million samples. This dataset was meticulously created with our transparent, adaptable, and open-source data curation pipeline, **HoneyPipe**, which systematically cleans noisy data and enriches it with a novel dual-level (short and long) Chain-of-Thought (CoT) strategy. This dataset enables Bee-8B to achieve exceptional performance, particularly in complex reasoning, establishing a new standard for fully open MLLMs.

• Qwen2.5-7B-Instruct-abliterated-v2-GGUF↗

  QuantFactory

  library_name: transformers license: apache-2.0 license_link: https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2/blob/main/LICENSE language: - en pipeline_tag: text-generation base_model: Qwen/Qwen2.5-7B-Instruct tags: - chat - abliterated - uncensored This is quantized version of huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2 created using llama.cpp This is an uncensored version of Qwen/Qwen2.5-7B-Instruct created with abliteration (see this article to know more about it).

• Devstral-Small-2507-MLX-8bit↗

  lmstudio-community

  *👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator**: mistralai **Original model**: Devstral-Small-2507 **MLX quantization**: provided by LM Studio team using mlx_lm **LM Studio model page**: mistralai/devstral-small-2507 LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

• Qwen3.8-27B-Ternary-Bonsai-2-DFlash2-MLX↗

  nathansutton

  Prism ML's ternary `Ternary-Bonsai-2-27B` build of `Qwen/Qwen3.8-27B`, repacked for chad, a Claude-Code-style local coding agent for Apple Silicon, **with its speculative decoder bundled in**. This is chad's default model. Created using Bonsai by Prism ML. One download gets both halves: the ternary target model and the block drafter it decodes with, already quantized. Nothing is built on first run. Every projection of Qwen3.8-27B (a dense `qwen3_5` hybrid: 64 layers, 48 GatedDeltaNet + 16 full attention) is stored in a **Hadamard-rotated basis**: multiplied by a fixed sign vector and put through a blockwise Walsh-Hadamard transform offline, then quantized to 2-bit affine group-128 whose three levels reproduce the ternary set {−s, 0, +s}. The rotation costs no extra bits and no extra weight traffic, but the matching transform must be applied to the activations at runtime and inverted after the embedding lookup. **An ordinary MLX affine loader finds tensors of exactly the right shapes, skips both transforms, and produces plausible garbage without raising.** chad routes the pack on its `model_type` (`prism_hadamard_qwen35`) to a loader that does the rotation; mlx-lm alone will not run it correctly.

• WebSailor-32B↗

  Alibaba-NLP

  You can download the model then run the inference scipts in https://github.com/Alibaba-NLP/WebAgent. - **WebSailor** is a complete post-training methodology designed to teach LLM agents sophisticated reasoning for complex web navigation and information-seeking tasks. It addresses the challenge of extreme uncertainty in vast information landscapes, a capability where previous open-source models lagged behind proprietary systems. - We classify information-seeking tasks into three difficulty levels, where **Level 3** represents problems with both high uncertainty and a complex, non-linear path to a solution. To generate these challenging tasks, we introduce **SailorFog-QA**, a novel data synthesis pipeline that constructs intricate knowledge graphs and then applies information obfuscation. This process creates questions with high initial uncertainty that demand creative exploration and transcend simple, structured reasoning patterns.

• LFM2-1.2B↗

  LiquidAI

  LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. We're releasing the weights of four post-trained checkpoints with 350M, 700M, 1.2B, and 2.6B parameters. They provide the following key features to create AI-powered edge applications: * **Fast training & inference** – LFM2 achieves 3x faster training compared to its previous generation. It also benefits from 2x faster decode and prefill speed on CPU compared to Qwen3. * **Best performance** – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities. * **New architecture** – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions. * **Flexible deployment** – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles.

• Qwen3-1.6B-A0.9B↗

  inference-optimization

  The checkpoint is stored as a single `model.safetensors` file with individual expert weights matching the original Qwen3 structure. Each layer has 16 experts with separate `gate_proj`, `up_proj`, and `down_proj` weights per expert. The model was fine-tuned on a toy copypasta dataset and achieves: - **Perplexity**: 1.0 (on validation text) - **Generation**: Successfully generates coherent continuations 1. **Configuration**: Created with 10 layers and 16 experts (8 activated per token) to achieve ~1.6B total parameters with 50% MoE activation 2. **Initialization**: Randomly initialized weights using transformers `init_weights()` 3. **Fine-tuning**: Trained on famous internet copypastas until perplexity < 3.0 4. **Checkpoint Conversion**: Converted from batched expert format to individual expert format to match original Qwen3 checkpoint structure 5. **Validation**: Confirmed perplexity ~1.0 and successful text generation

• deepseek-vl-7b-chat↗

  deepseek-community

  This is the transformers version of Deepseek-VL-Hybrid, a foundation model for Visual Language Modeling. Deepseek-VL-Hybrid was introduced by the DeepSeek AI team. It is a vision-language model (VLM) designed to process both text and images for generating contextually relevant responses. The model leverages LLaMA as its text encoder, while SigLip is used for encoding low-resolution images and SAM (Segment Anything Model) is incorporated to handle high-resolution image encoding, enhancing the model’s ability to process fine-grained visual details. Deepseek-VL-Hybrid is a variant of Deepseek-VL that uses SAM (Segment Anything Model) to handle high-resolution image encoding. > We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in r

• Ingot-genesis-27B-Q2↗

  dendriteholdings

  The genesis model of Ingot's **27B-Q2** competition: the first king that every q2 submission is scored against. This is an unmodified copy of `Ternary-Bonsai-2-27B-PQ2_0.gguf` from prism-ml/Ternary-Bonsai-2-27B-gguf at revision `b072e1d3b35a0a630cece372c2127528e0994386`, renamed to `Ingot-genesis-27B-Q2.gguf`. Created using Bonsai by Prism ML. PQ2_0 needs the PrismML llama.cpp fork; stock llama.cpp does not load it. Ingot evaluates with the fork at tag `prism-b10777-e3de715`.