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AI 模型 449

• Theia-Llama-3.1-8B-v1.1-GGUF↗

  Chainbase-Labs

  **Theia-Llama-3.1-8B is an open-source crypto LLM, trained with carefully-designed dataset from the crypto field.** The training dataset is curated from two primary sources to create a comprehensive representation of blockchain projects. The first source is data collected from **CoinMarketCap**, focusing on the top **2000 projects** ranked by market capitalization. This includes a wide range of project-specific documents such as whitepapers, official blog posts, and news articles. The second core component of the dataset comprises detailed research reports on these projects gathered from various credible sources on the internet, providing in-depth insights into project fundamentals, development progress, and market impact. After constructing the dataset, both manual and algorithmic filtering are applied to ensure data accuracy and eliminate redundancy. The Theia-Llama-3.1-8B is fine-tuned from the base model (Llama-3.1-8B), specifically tailored for the cryptocurrency domain. We employed LoRA (Low-Rank Adaptation) to fine-tune the model effectively, leveraging its ability to adapt large pre-trained models to specific tasks with a smaller computational footprint. Our training methodology is further enhanced through the use of LLaMA Factory, an open-source training framework. We integrate **DeepSpeed**, Microsoft's distributed training engine, to optimize resource utilization and training efficiency. Techniques such as ZeRO (Zero Redundancy Optimizer), offload, sparse attention, 1-bit Adam, and pipeline parallelism are employed to accelerate the training process and reduce memory consumption. A fine-tuned model is also built using the novel D-DoRA, a decentralized training scheme, by our Chainbase Labs. Since the LoRA version is much easier to deploy and play with for developers, we release the LoRA version first for the Crypto AI community.

• Qwen3.5-4B-MLX-8bit↗

  lmstudio-community

  _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: Qwen **Original model**: Qwen3.5-4B **MLX quantization**: provided by LM Studio team using mlx_vlm 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.

• QwenEdit2509-FlatLogColor↗

  tlennon-ie

  QwenEdit2509-FlatLogColor is a specialized LoRA designed to bridge the gap between AI image generation and professional creative workflows. A common challenge for professionals using AI-generated content is that most images are created as "finished" products. They come with fully applied color, contrast, and saturation, much like a JPEG straight out of a consumer camera. This leaves little room for creative manipulation, such as color grading, which is a crucial step in film, photography, and multimedia production. This model solves that problem. It takes a standard AI-generated image and converts it into a **FLAT** or **LOG** color profile.

• AI-vs-Deepfake-vs-Real-Siglip2↗

  prithivMLmods

  **AI-vs-Deepfake-vs-Real-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to distinguish AI-generated images, deepfake images, and real images using the SiglipForImageClassification architecture. The model categorizes images into three classes: - **Class 0:** "AI" – The image is fully AI-generated, created by machine learning models. - **Class 1:** "Deepfake" – The image is a manipulated deepfake, where real content has been altered. - **Class 2:** "Real" – The image is an authentic, unaltered photograph. The **AI-vs-Deepfake-vs-Real-Siglip2** model is designed to classify images into three categories: **AI-generated, deepfake, or real**. It helps in identifying whether an image is fully synthetic, altered through deepfake techniques, or an unaltered real image.

• Huihui-Qwen3.8-Flash-Next-abliterated-v2-GGUF↗

  huihui-ai

  This is an uncensored version of Qwen/Qwen3.8-Flash-Next created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. We used calibration3.txt to generate imatrix_micro3.gguf, and then based on that produced unsloth-UD-Q4_K_XL-V2, with the PPL value decreasing slightly. The llama.cpp version used for testing is llama.cpp-b11540.

• wav2vec2-large-xlsr-53-faroese-100h↗

  carlosdanielhernandezmena

  **Paper:** ASR Language Resources for Faroese The "wav2vec2-large-xlsr-53-faroese-100h" is an acoustic model suitable for Automatic Speech Recognition in Faroese. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" with 100 hours of Faroese data released by the Ravnur Project (https://maltokni.fo/en/) from the Faroe Islands. The specific dataset used to create the model is called "Ravnursson Faroese Speech and Transcripts" and it is available at http://hdl.handle.net/20.500.12537/276.

• LTX_2.3_Wild_West_Style_LoRa↗

  vrgamedevgirl84

  This LoRA creates a **cinematic realistic Wild West video** look. It is a grounded frontier-western style for **text-to-video generation**, not actual frames or footage from any movies, TV shows, games, or existing franchises. The workflow used to create the sample videos can be found HERE. Just swap out the LoRA and prompt. All sample videos are **text to video**. **NOT image to video.** Characters, environments, and motion read as grounded Wild West frontier footage. Scenes feature **weathered cowboys**, **outlaws**, **sheriffs**, **ranchers**, and **settlers**, with **period-authentic western clothing**, **worn leather gear**, **cowboy hats**, **dust-covered boots**, **wooden saloons**, **frontier towns**, **desert plains**, **barns**, **stables**, and **rugged natural landscapes**. The atmosphere is **dusty**, **sunbaked**, **dramatic**, and **cinematic**, with realistic textures and strong environmental storytelling. The result feels like a scene from a modern high-end western film with immersive frontier realism and grounded human tension.

• qwen3-4b-heretic↗

  DreamFast

  An abliterated version of Qwen 3 4B created using Heretic v1.2.0. This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for image generation models like Z-Image and FLUX.2 Klein 4B. Available in five ComfyUI-native quantized formats (FP8, INT8, INT4, NVFP4, MXFP8), all produced with SVD-guided learned rounding for maximum fidelity. > **Quality:** All quantized variants use SVD-guided learned rounding (AdaRound via convert_to_quant), which optimizes each weight's rounding direction to minimize output reconstruction error — noticeably higher fidelity than naive round-to-nearest quantization. All variants load natively in ComfyUI 0.30.0+ (no plugins) via the `comfy_quant` metadata embedded in each file.

• wav2vec2-conformer-rel-pos-large↗

  facebook

  Wav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.

• Huihui-Qwen3.8-27B-abliterated-GGUF↗

  Kaktus4048

  This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.