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AI 모델 450
- Ornith-1.0-9B-MLX-8bit↗
lmstudio-community
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: deepreinforce-ai **Original model**: Ornith-1.0-9B **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.
- Magistral-Small-2506-MLX-4bit↗
lmstudio-community
*👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** mistral-ai **Original model**: magistral-small **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.
- LLaVA-Bielik-11b-v2.6-instruct↗
NASK-PIB
This model is the first Polish-focused Vision-Language Model (VLM), created by extending the open-source LLaVA architecture with the Bielik language model. Our pipeline integrates high-quality multimodal instruction tuning with Bielik’s strong Polish linguistic abilities, resulting in a VLM that demonstrates significantly improved understanding of Polish language, culture, and context-specific visual reasoning. The model is intended for research and development purposes, specifically focusing on multimodal tasks requiring the Polish language and cultural context. It can be used directly for: This model can be fine-tuned or integrated into larger applications to support specific use cases, such as: * **Accessibility Tools:** Creating applications that describe surroundings or digital content to visually impaired Polish speakers. * **E-commerce:** Generating automated product descriptions based on images for Polish marketplaces. * **Educational Assistants:** Developing tutoring systems that can explain visual content (diagrams, historical photos) to students in Polish. * **Specialized Fine-tuning:** The model can be further fine-tuned on domain-specific datasets (e.g., Polish medical imaging reports or legal document analysis) to improve performance in niche sectors.
- Anima-Quantized↗
silveroxides
Anima is a 2 billion parameter text-to-image model created via a collaboration between CircleStone Labs and Comfy Org. It is focused mainly on anime concepts, characters, and styles, but is also capable of generating a wide variety of other non-photorealistic content. The model is designed for making illustrations and artistic images, and will not work well at realism. It is trained on several million anime images and about 800k non-anime artistic images. No synthetic data was used for training. The knowledge cut-off date for the anime training data is September 2025. This preview version is an intermediate model checkpoint. The model is still training and the final version will improve, especially for fine details and overall aesthetics.
- Huihui-Laguna-S-2.1-abliterated-GGUF↗
huihui-ai
This is an uncensored version of poolside/Laguna-S-2.1 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. **Note** This is an English language model, and support for other languages is not very good. - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
- gpt-neo-125m↗
EleutherAI
GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model. GPT-Neo 125M was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. This model was trained on the Pile for 300 billion tokens over 572,300 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
- wav2vec2-deepfake-voice-detector↗
garystafford
Fine-tuned Wav2Vec2 model for detecting AI-generated speech. Determines if audio was spoken by a human or created by AI text-to-speech/voice cloning software. Fine-tuned Wav2Vec2 transformer for binary audio classification (real vs AI-generated speech). Trained to distinguish authentic human speech from synthetic audio generated by AI text-to-speech and voice cloning services including: **Note:** This model uses transfer learning from a base model already trained for deepfake detection. Fast convergence is expected due to task similarity and TTS engine overlap with the base model's training data.
- Smooth_Booster_v3↗
LyliaEngine
Skin Details: created and merged Loras that add a little more details to skin. Should be more noticeable on realistic Checkpoints. Armor and Weapons Details:: created and merged Loras that adds detailed engraving and patterns. Better material quality. A detail tweaker/booster inspired by the work of many awesome creators like Shed_The_Skin's Detail Slider LoRA, w4r10ck's Detail Tweaker XL, and many, MANY, others.
- Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imat-plus2↗
DavidAU
This is a Floating Point 32 upscale, where all components and merges were remastered to floating point 32. This includes all the merges (recreated with master files), and where possible subbing full FP32 models. This repo contains the new Imatrix Plus 2 quants using a new inhouse dataset merged with a master dataset to push performance of the Ultra Quality remaster even higher. At Q2K an impressive drop of 533 points in perplexity. (lower is better) (VS: Q2K original base model: PPL = 9.8077 +/- 0.06821 )
- MegaBeam-Mistral-7B-300k↗
aws-prototyping
MegaBeam-Mistral-7B-300k is a fine-tuned Mistral-7B-Instruct-v0.2 language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as vLLM, Sagemaker DJL endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and Mistral-7B-Instruct-v0.2 are summarized below: _InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, Mistral-7B-Instruct-v0.2, Llama-3-8B-Instruct-262k, and Llama3-70B-1M on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below. The 12 evaluation tasks are summarized below (as per InfiniteBench)) | Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description | | -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- | | En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. | | En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. | | En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. | | En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. | | Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. | | Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). | | Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. | | Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. | | Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. | | Retrieve.PassKey | Syntheti