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z-image-diffusers
Z-Image is the foundation model of the ⚡️-Image family, engineered for good quality, robust generative diversity, broad stylistic coverage, and precise prompt adherence. While Z-Image-Turbo is built for speed, Z-Image is a full-capacity, undistilled transformer designed to be the backbone for creators, researchers, and developers who require the highest level of creative freedom.

Repository: localaiLicense: apache-2.0

z-image-turbo-diffusers
🚀 Z-Image-Turbo – A distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers ⚡️sub-second inference latency⚡️ on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.

Repository: localaiLicense: apache-2.0

glm-4.7-flash-derestricted
This model is a quantized version of the original GLM-4.7-Flash-Derestricted model, derived from the base model `koute/GLM-4.7-Flash-Derestricted`. It is designed for restricted use, featuring tags like "derestricted," "uncensored," and "unlimited." The quantized versions (e.g., Q2_K, Q4_K_S, Q6_K) offer varying trade-offs between accuracy and efficiency, with the Q4_K_S and Q6_K variants being recommended for balanced performance. The model is optimized for fast inference and supports multiple quantization schemes, though some advanced quantization options (like IQ4_XS) are not available. It is intended for use in environments with specific constraints or restrictions.

Repository: localai

qwen3-tts-1.7b-custom-voice
Qwen3-TTS is a high-quality text-to-speech model supporting custom voice, voice design, and voice cloning.

Repository: localaiLicense: apache-2.0

qwen3-asr-1.7b
Qwen3-ASR is an automatic speech recognition model supporting multiple languages and batch inference.

Repository: localaiLicense: apache-2.0

qwen3-asr-0.6b
Qwen3-ASR is an automatic speech recognition model supporting multiple languages and batch inference.

Repository: localaiLicense: apache-2.0

huihui-glm-4.7-flash-abliterated-i1
The model is a quantized version of **huihui-ai/Huihui-GLM-4.7-Flash-abliterated**, optimized for efficiency and deployment. It uses GGUF files with various quantization levels (e.g., IQ1_M, IQ2_XXS, Q4_K_M) and is designed for tasks requiring low-resource deployment. Key features include: - **Base Model**: Huihui-GLM-4.7-Flash-abliterated (unmodified, original model). - **Quantization**: Supports IQ1_M to Q4_K_M, balancing accuracy and efficiency. - **Use Cases**: Suitable for applications needing lightweight inference, such as edge devices or resource-constrained environments. - **Downloads**: Available in GGUF format with varying quality and size (e.g., 0.2GB to 18.2GB). - **Tags**: Abliterated, uncensored, and optimized for specific tasks. This model is a modified version of the original GLM-4.7, tailored for deployment with quantized weights.

Repository: localai

mox-small-1-i1
The model, **vanta-research/mox-small-1**, is a small-scale text-generation model optimized for conversational AI tasks. It supports chat, persona research, and chatbot applications. The quantized versions (e.g., i1-Q4_K_M, i1-Q4_K_S) are available for efficient deployment, with the i1-Q4_K_S variant offering the best balance of size, speed, and quality. The model is designed for lightweight inference and is compatible with frameworks like HuggingFace Transformers.

Repository: localai

glm-4.7-flash
**GLM-4.7-Flash** is a 30B-A3B MoE (Model Organism Ensemble) model designed for efficient deployment. It outperforms competitors in benchmarks like AIME 25, GPQA, and τ²-Bench, offering strong accuracy while balancing performance and efficiency. Optimized for lightweight use cases, it supports inference via frameworks like vLLM and SGLang, with detailed deployment instructions in the official repository. Ideal for applications requiring high-quality text generation with minimal resource consumption.

Repository: localai

qwen3-vl-reranker-8b
**Model Name:** Qwen3-VL-Reranker-8B **Base Model:** Qwen/Qwen3-VL-Reranker-8B **Description:** A high-performance multimodal reranking model for state-of-the-art cross-modal search. It supports 30+ languages and handles text, images, screenshots, videos, and mixed modalities. With 8B parameters and a 32K context length, it refines retrieval results by combining embedding vectors with precise relevance scores. Optimized for efficiency, it supports quantized versions (e.g., Q8_0, Q4_K_M) and is ideal for applications requiring accurate multimodal content matching. **Key Features:** - **Multimodal**: Text, images, videos, and mixed content. - **Language Support**: 30+ languages. - **Quantization**: Available in Q8_0 (best quality), Q4_K_M (fast, recommended), and lower-precision options. - **Performance**: Outperforms base models in retrieval tasks (e.g., JinaVDR, ViDoRe v3). - **Use Case**: Enhances search pipelines by refining embeddings with precise relevance scores. **Downloads:** - [GGUF Files](https://huggingface.co/mradermacher/Qwen3-VL-Reranker-8B-GGUF) (e.g., `Qwen3-VL-Reranker-8B.Q8_0.gguf`). **Usage:** - Requires `transformers`, `qwen-vl-utils`, and `torch`. - Example: `from scripts.qwen3_vl_reranker import Qwen3VLReranker; model = Qwen3VLReranker(...)` **Citation:** @article{qwen3vlembedding, ...} This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.

Repository: localai

liquidai.lfm2-2.6b-transcript
This is a large language model (2.6B parameters) designed for text-generation tasks. It is a quantized version of the original model `LiquidAI/LFM2-2.6B-Transcript`, optimized for efficiency while retaining strong performance. The model is built on the foundation of the base model, with additional optimizations for deployment and use cases like transcription or language modeling. It is trained on large-scale text data and supports multiple languages.

Repository: localai

lfm2.5-1.2b-nova-function-calling
The **LFM2.5-1.2B-Nova-Function-Calling-GGUF** is a quantized version of the original model, optimized for efficiency with **Unsloth**. It supports text and multimodal tasks, using different quantization levels (e.g., Q2_K, Q3_K, Q4_K, etc.) to balance performance and memory usage. The model is designed for function calling and is faster than the original version, making it suitable for tasks like code generation, reasoning, and multi-modal input processing.

Repository: localai

mistral-nemo-instruct-2407-12b-thinking-m-claude-opus-high-reasoning-i1
The model described in this repository is the **Mistral-Nemo-Instruct-2407-12B** (12 billion parameters), a large language model optimized for instruction tuning and high-level reasoning tasks. It is a **quantized version** of the original model, compressed for efficiency while retaining key capabilities. The model is designed to generate human-like text, perform complex reasoning, and support multi-modal tasks, making it suitable for applications requiring strong language understanding and output.

Repository: localai

rwkv7-g1c-13.3b
The model is **RWKV7 g1c 13B**, a large language model optimized for efficiency. It is quantized using **Bartowski's calibrationv5 for imatrix** to reduce memory usage while maintaining performance. The base model is **BlinkDL/rwkv7-g1**, and this version is tailored for text-generation tasks. It balances accuracy and efficiency, making it suitable for deployment in various applications.

Repository: localai

iquest-coder-v1-40b-instruct-i1
The **IQuest-Coder-V1-40B-Instruct-i1-GGUF** is a quantized version of the original **IQuestLab/IQuest-Coder-V1-40B-Instruct** model, designed for efficient deployment. It is an **instruction-following large language model** with 40 billion parameters, optimized for tasks like code generation and reasoning. **Key Features:** - **Size:** 40B parameters (quantized for efficiency). - **Purpose:** Instruction-based coding and reasoning. - **Format:** GGUF (supports multi-part files). - **Quantization:** Uses advanced techniques (e.g., IQ3_M, Q4_K_M) for balance between performance and quality. **Available Quantizations:** - Optimized for speed and size: **i1-Q4_K_M** (recommended). - Lower-quality options for trade-off between size/quality. **Note:** This is a **quantized version** of the original model, but the base model (IQuestLab/IQuest-Coder-V1-40B-Instruct) is the official source. For full functionality, use the unquantized version or verify compatibility with your deployment tools.

Repository: localai

onerec-8b
The model `mradermacher/OneRec-8B-GGUF` is a quantized version of the base model `OpenOneRec/OneRec-8B`, a large language model designed for tasks like recommendations or content generation. It is optimized for efficiency with various quantization schemes (e.g., Q2_K, Q4_K, Q8_0) and available in multiple sizes (3.5–9.0 GB). The model uses the GGUF format and is licensed under Apache-2.0. Key features include: - **Base Model**: `OpenOneRec/OneRec-8B` (a pre-trained language model for recommendations). - **Quantization**: Supports multiple quantized variants (Q2_K, Q3_K, Q4_K, etc.), with the best quality for `Q4_K_S` and `Q8_0`. - **Sizes**: Available in sizes ranging from 3.5 GB (Q2_K) to 9.0 GB (Q8_0), with faster speeds for lower-bit quantized versions. - **Usage**: Compatible with GGUF files, suitable for deployment in applications requiring efficient model inference. - **Licence**: Apache-2.0, available at [https://huggingface.co/OpenOneRec/OneRec-8B/blob/main/LICENSE](https://huggingface.co/OpenOneRec/OneRec-8B/blob/main/LICENSE). For detailed specifications, refer to the [model page](https://hf.tst.eu/model#OneRec-8B-GGUF).

Repository: localai

minimax-m2.1-i1
The model **MiniMax-M2.1** (base model: *MiniMaxAI/MiniMax-M2.1*) is a large language model quantized for efficient deployment. It is optimized for speed and memory usage, with quantized versions available in various formats (e.g., GGUF) for different performance trade-offs. The quantization is done by the user, and the model is licensed under the *modified-mit* license. Key features: - **Quantized versions**: Includes low-precision (IQ1, IQ2, Q2_K, etc.) and high-precision (Q4_K_M, Q6_K) options. - **Usage**: Requires GGUF files; see [TheBloke's documentation](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for details on integration. - **License**: Modified MIT (see [license link](https://github.com/MiniMax-AI/MiniMax-M2.1/blob/main/LICENSE)). For gallery use, emphasize its quantized variants, performance trade-offs, and licensing.

Repository: localai

tildeopen-30b-instruct-lv-i1
The **TildeOpen-30B-Instruct-LV-i1-GGUF** is a quantized version of the base model **pazars/TildeOpen-30B-Instruct-LV**, optimized for deployment. It is an instruct-based language model trained on diverse datasets, supporting multiple languages (en, de, fr, pl, ru, it, pt, cs, nl, es, fi, tr, hu, bg, uk, bs, hr, da, et, lt, ro, sk, sl, sv, no, lv, sr, sq, mk, is, mt, ga). Licensed under CC-BY-4.0, it uses the Transformers library and is designed for efficient inference. The quantized version (with imatrix format) is tailored for deployment on devices with limited resources, while the base model remains the original, high-quality version.

Repository: localai

allenai_olmo-3.1-32b-think
The **Olmo-3.1-32B-Think** model is a large language model (LLM) optimized for efficient inference using quantized versions. It is a quantized version of the original **allenai/Olmo-3.1-32B-Think** model, developed by **bartowski** using the **imatrix** quantization method. ### Key Features: - **Base Model**: `allenai/Olmo-3.1-32B-Think` (unquantized version). - **Quantized Versions**: Available in multiple formats (e.g., `Q6_K_L`, `Q4_1`, `bf16`) with varying precision (e.g., Q8_0, Q6_K_L, Q5_K_M). These are derived from the original model using the **imatrix calibration dataset**. - **Performance**: Optimized for low-memory usage and efficient inference on GPUs/CPUs. Recommended quantization types include `Q6_K_L` (near-perfect quality) or `Q4_K_M` (default, balanced performance). - **Downloads**: Available via Hugging Face CLI. Split into multiple files if needed for large models. - **License**: Apache-2.0. ### Recommended Quantization: - Use `Q6_K_L` for highest quality (near-perfect performance). - Use `Q4_K_M` for balanced performance and size. - Avoid lower-quality options (e.g., `Q3_K_S`) unless specific hardware constraints apply. This model is ideal for deploying on GPUs/CPUs with limited memory, leveraging efficient quantization for practical use cases.

Repository: localai

huihui-glm-4.6v-flash-abliterated
**Huihui-GLM-4.6V-Flash (Abliterated)** A text-based large language model derived from the **zai-org/GLM-4.6V-Flash** base model, featuring reduced safety filters and uncensored capabilities. Designed for text generation, it supports conversational tasks but excludes image processing. **Key Features:** - **Base Model**: GLM-4.6V-Flash (original author: zai-org) - **Quantized Format**: GGUF (optimized for efficiency). - **No Image Support**: Only text-based interactions are enabled. - **Custom Training**: Abliterated to remove restrictive outputs, prioritizing openness over safety. **Important Notes:** - **Risk of Sensitive Content**: Reduced filtering may generate inappropriate or controversial outputs. - **Ethical Use**: Suitable for research or controlled environments; not recommended for public or commercial deployment without caution. - **Legal Responsibility**: Users must ensure compliance with local laws and ethical guidelines. **Use Cases:** - Experimental text generation. - Controlled research environments. - Testing safety filtering mechanisms. *Note: This model is not suitable for production or public-facing applications without thorough review.*

Repository: localai

qwen3-coder-30b-a3b-instruct-rtpurbo-i1
The model in question is a quantized version of the original **Qwen3-Coder** large language model, specifically tailored for code generation. The base model, **RTP-LLM/Qwen3-Coder-30B-A3B-Instruct-RTPurbo**, is a 30B-parameter variant optimized for instruction-following and code-related tasks. It employs the **A3B attention mechanism** and is trained on diverse data to excel in programming and logical reasoning. The current repository provides a quantized (compressed) version of this model, which is suitable for deployment on hardware with limited memory but loses some precision compared to the original. For a high-fidelity version, the unquantized base model is recommended.

Repository: localai

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