Saved toolsSubmit a tool
FOXAI SEARCH

Search FoxAI

AI Models 449

  • wav2vec2-large-robust-24-ft-age-gender↗

    audeering

    The model expects a raw audio signal as input and outputs predictions for age in a range of approximately 0...1 (0...100 years) and gender expressing the probababilty for being child, female, or male. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on aGender, Mozilla Common Voice, Timit and Voxceleb 2. For this version of the model we trained all 24 transformer layers. An ONNX export of the model is available from doi:10.5281/zenodo.7761387. Further details are given in the associated paper and tutorial.

  • gemma-4-E2B-it-litert-lm↗

    litert-community

    Main Model Card: google/gemma-4-E2B-it This model card provides the Gemma 4 E2B model in a way that is ready for deployment on Android, iOS, Desktop, IoT and Web. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. This particular Gemma 4 model is small so it is ideal for on-device use cases. By running this model on device, users can have private access to Generative AI technology without even requiring an internet connection.

  • gpt-oss-20b-MLX-8bit↗

    lmstudio-community

    _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: openai **Original model**: gpt-oss-20b **MLX quantization**: provided by LM Studio team using mlx_lm 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.

  • Wan2.1_T2V_14B_FusionX_VACE-GGUF↗

    QuantStack

    All quantized versions were created from the base FP16 model using the conversion scripts provided by city96, available at the ComfyUI-GGUF GitHub repository. The model files can be used in ComfyUI with the ComfyUI-GGUF custom node. Place the required model(s) in the following folders:

  • Olmo-3-32B-Think-MLX-4bit↗

    lmstudio-community

    _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: allenai **Original model**: Olmo-3-32B-Think **MLX quantization**: provided by LM Studio team using mlx_lm 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.

  • Ming-Image_GGUFs↗

    realrebelai

    These are not simple one-pass quants. The diffusion-model files use a **mixed-precision HQ ladder** designed to keep the most sensitive Ming-Image tensors at higher precision while aggressively quantizing the larger bulk weights. 1. Convert the original BF16 diffusion weights into a **shape-preserving BF16 GGUF master**. 2. Quantize that BF16 GGUF directly with `llama-quantize` to create donor files: - `Q8_0` - `Q6_K` - `Q5_K_M` - `Q4_K_M` - `Q3_K_M` - `Q2_K` 3. Build the final `-HQ` models by selecting tensors from those already-quantized donor files. 4. The HQ assembly step copies the existing packed GGUF tensor data directly. It does **not** dequantize and requantize each selected tensor. The `general.architecture` transport metadata used during GGUF conversion is for quantizer/loader compatibility only. It should not be interpreted as Ming-Image being a Lumina2 model.

  • yolov8_animeface↗

    Fuyucchi

    Dataset was made created with 10 000 images manually annotated from safebooru. Split was train 70, val 20, test 10. This model is based on yolov8x6. It has been trained on the said dataset for 300 epoch at 1280px*1280px. It took ~110 hours to train on a RTX A4000. While it doesn't provide a huge mAP50-95, its predictions are always correct on the files I've tested. Confidence could be higher, but the model is very precise.

  • french-bge-m3↗

    antoinelouis

    --- pipeline_tag: sentence-similarity language: fr license: mit tags: - passage-retrieval - sentence-similarity - pruned library_name: sentence-transformers base_model: BAAI/bge-m3 base_model_relation: quantized --- This model is a 38.4% smaller version of BAAI/bge-m3 for the French language, created using the mtem-pruner space. This pruned model should perform similarly to the original model for French language tasks with a much smaller memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not commonly used in French were removed from the original multilingual model's vocabulary.

  • free-evo-qwen72b-v0.8-re↗

    freewheelin

    You need two models with the same architecture. - Choose one model and fine-tune it to create a gap between the original model and the fine-tuned one. It doesn't matter whether the evaluation score is higher or lower. - Merge the two models. - Evaluate the merged model. - Fine-tune a specific evaluation part of the model if you need to increase the score for that part. (It's unlikely to work as you think, but you can try it.) - Merge the models again. - Evaluate again. - Keep going until the average evaluation score is higher than the original one.

  • gemma-3-12b-it-GGUF↗

    ggml-org

    Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.