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nemotron-3-nano-omni-30b-a3b-reasoning-apex
# Model Overview ### Description: NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding to support enterprise-grade Q&A, summarization, transcription, and document intelligence workflows. It extends the Nemotron Nano family with integrated video+speech comprehension, Graphical User Interface (GUI), Optical Character Recognition (OCR), and speech transcription capabilities, enabling end-to-end processing of rich enterprise content such as meeting recordings, M&E assets, training videos, and complex business documents. NVIDIA Nemotron 3 Nano Omni was developed by NVIDIA as part of the Nemotron model family. This model is available for commercial use. This model was improved using Qwen3-VL-30B-A3B-Instruct, Qwen3.5-122B-A10B, Qwen3.5-397B-A17B, Qwen2.5-VL-72B-Instruct, and gpt-oss-120b. For more information, please see the Training Dataset section below. ### License/Terms of Use Governing Terms: Use of this model is governed by the NVIDIA Open Model Agreement ### Deployment Geography: Global ...

Repository: localaiLicense: other

gemma-4-26b-a4b-it
Google Gemma 4 26B-A4B-IT is an open-source multimodal Mixture-of-Experts model with 26B total parameters and 4B active parameters. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. The MoE architecture provides strong performance with efficient inference. Well-suited for question answering, summarization, reasoning, and image understanding tasks.

Repository: localaiLicense: apache-2.0

gemma-4-31b-it
Google Gemma 4 31B-IT is the largest dense model in the Gemma 4 family with 31B parameters. It handles text and image input, generating text output, with a 256K context window and support for 140+ languages. Provides the highest quality outputs in the Gemma 4 lineup, well-suited for complex reasoning, summarization, and image understanding tasks.

Repository: localaiLicense: apache-2.0

gemma-3-27b-it
Google/gemma-3-27b-it is an open-source, state-of-the-art vision-language model built from the same research and technology used to create the Gemini models. It is multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 models have a large, 128K context window, multilingual support in over 140 languages, and are available in more sizes than previous versions. They 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.

Repository: localaiLicense: gemma

gemma-3-12b-it
google/gemma-3-12b-it is an open-source, state-of-the-art, lightweight, multimodal model built from the same research and technology used to create the Gemini models. It is capable of handling text and image input and generating text output. It has a large context window of 128K tokens and supports over 140 languages. The 12B variant has been fine-tuned using the instruction-tuning approach. Gemma 3 models are suitable for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes them deployable in environments with limited resources such as laptops, desktops, or your own cloud infrastructure.

Repository: localaiLicense: gemma

gemma-3-4b-it
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. Gemma-3-4b-it is a 4 billion parameter model.

Repository: localaiLicense: gemma

gemma-3-1b-it
google/gemma-3-1b-it is a large language model with 1 billion parameters. It is part of the Gemma family of open, state-of-the-art 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. These models have multilingual support in over 140 languages, and are available in more sizes than previous versions. They 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.

Repository: localaiLicense: gemma

gemma-3-270m-it-qat
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. This model is a QAT (Quantization Aware Training) version of the Gemma 3 270M model. It is quantized to 4-bit precision, which means that it uses 4-bit floating point numbers to represent the weights and activations of the model. This reduces the memory footprint of the model and makes it faster to run on GPUs.

Repository: localaiLicense: gemma

llama-3.2-1b-instruct:q4_k_m
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.2

llama-3.2-3b-instruct:q4_k_m
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.2

llama-3.2-3b-instruct:q8_0
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.2

llama-3.2-1b-instruct:q8_0
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Repository: localaiLicense: llama3.2

llama-3.2-3b-agent007-coder
The Llama-3.2-3B-Agent007-Coder-GGUF is a quantized version of the EpistemeAI/Llama-3.2-3B-Agent007-Coder model, which is a fine-tuned version of the unsloth/llama-3.2-3b-instruct-bnb-4bit model. It is created using llama.cpp and trained with additional datasets such as the Agent dataset, Code Alpaca 20K, and magpie ultra 0.1. This model is optimized for multilingual dialogue use cases and agentic retrieval and summarization tasks. The model is available for commercial and research use in multiple languages and is best used with the transformers library.

Repository: localaiLicense: apache-2.0

llama-chat-summary-3.2-3b
Llama-Chat-Summary-3.2-3B is a fine-tuned model designed for generating context-aware summaries of long conversational or text-based inputs. Built on the meta-llama/Llama-3.2-3B-Instruct foundation, this model is optimized to process structured and unstructured conversational data for summarization tasks.

Repository: localaiLicense: creativeml-openrail-m

llama3.1-70b-chinese-chat
"Llama3.1-70B-Chinese-Chat" is a 70-billion parameter large language model pre-trained on a large corpus of Chinese text data. It is designed for chat and dialog applications, and can generate human-like responses to various prompts and inputs. The model is based on the Llama3.1 architecture and has been fine-tuned for Chinese language understanding and generation. It can be used for a wide range of natural language processing tasks, including language translation, text summarization, question answering, and more.

Repository: localaiLicense: llama3.1

dans-personalityengine-v1.0.0-8b
This model is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline. It has been trained on a wide array of one shot instructions, multi turn instructions, role playing scenarios, text adventure games, co-writing, and much more. The full dataset is publicly available and can be found in the datasets section of the model page. There has not been any form of harmfulness alignment done on this model, please take the appropriate precautions when using it in a production environment.

Repository: localaiLicense: apache-2.0

dans-personalityengine-v1.1.0-12b
This model series is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline. It has been trained on a wide array of one shot instructions, multi turn instructions, tool use, role playing scenarios, text adventure games, co-writing, and much more.

Repository: localaiLicense: apache-2.0

pocketdoc_dans-personalityengine-v1.2.0-24b
This model series is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline. It has been trained on a wide array of one shot instructions, multi turn instructions, tool use, role playing scenarios, text adventure games, co-writing, and much more.

Repository: localaiLicense: apache-2.0

command-r-v01:q1_s
C4AI Command-R is a research release of a 35 billion parameter highly performant generative model. Command-R is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities.

Repository: localaiLicense: cc-by-nc-4.0

ibm-granite.granite-4.0-1b
### **Granite-4.0-1B** *By IBM | Apache 2.0 License* **Overview:** Granite-4.0-1B is a lightweight, instruction-tuned language model designed for efficient on-device and research use. Built on a decoder-only dense transformer architecture, it delivers strong performance in instruction following, code generation, tool calling, and multilingual tasks—making it ideal for applications requiring low latency and minimal resource usage. **Key Features:** - **Size:** 1.6 billion parameters (1B Dense), optimized for efficiency. - **Capabilities:** - Text generation, summarization, question answering - Code completion and function calling (e.g., API integration) - Multilingual support (English, Spanish, French, German, Japanese, Chinese, Arabic, Korean, Portuguese, Italian, Dutch, Czech) - Robust safety and alignment via instruction tuning and reinforcement learning - **Architecture:** Uses GQA (Grouped Query Attention), SwiGLU activation, RMSNorm, shared input/output embeddings, and RoPE position embeddings. - **Context Length:** Up to 128K tokens — suitable for long-form content and complex reasoning. - **Training:** Finetuned from *Granite-4.0-1B-Base* using open-source datasets, synthetic data, and human-curated instruction pairs. **Performance Highlights (1B Dense):** - **MMLU (5-shot):** 59.39 - **HumanEval (pass@1):** 74 - **IFEval (Alignment):** 80.82 - **GSM8K (8-shot):** 76.35 - **SALAD-Bench (Safety):** 93.44 **Use Cases:** - On-device AI applications - Research and prototyping - Fine-tuning for domain-specific tasks - Low-resource environments with high performance expectations **Resources:** - [Hugging Face Model](https://huggingface.co/ibm-granite/granite-4.0-1b) - [Granite Docs](https://www.ibm.com/granite/docs/) - [GitHub Repository](https://github.com/ibm-granite/granite-4.0-nano-language-models) > *“Make knowledge free for everyone.” – IBM Granite Team*

Repository: localaiLicense: apache-2.0

qwen3-vlto-32b-instruct-i1
**Model Name:** Qwen3-VL-32B-Instruct (Text-Only Variant: Qwen3-VLTO-32B-Instruct) **Base Model:** Qwen/Qwen3-VL-32B-Instruct **Repository:** [mradermacher/Qwen3-VLTO-32B-Instruct-i1-GGUF](https://huggingface.co/mradermacher/Qwen3-VLTO-32B-Instruct-i1-GGUF) **Type:** Large Language Model (LLM) – Text-Only (Vision-Language model stripped of vision components) **Architecture:** Qwen3-VL, adapted for pure text generation **Size:** 32 billion parameters **License:** Apache 2.0 **Framework:** Hugging Face Transformers --- ### 🔍 **Description** This is a **text-only variant** of the powerful **Qwen3-VL-32B-Instruct** multimodal model, stripped of its vision components to function as a high-performance pure language model. The model retains the full text understanding and generation capabilities of its parent — including strong reasoning, long-context handling (up to 32K+ tokens), and advanced multimodal training-derived coherence — while being optimized for text-only tasks. It was created by loading the weights from the full Qwen3-VL-32B-Instruct model into a text-only Qwen3 architecture, preserving all linguistic and reasoning strengths without the need for image input. Perfect for applications requiring deep reasoning, long-form content generation, code synthesis, and dialogue — with all the benefits of the Qwen3 series, now in a lightweight, text-focused form. --- ### 📌 Key Features - ✅ **High-Performance Text Generation** – Built on top of the state-of-the-art Qwen3-VL architecture - ✅ **Extended Context Length** – Supports up to 32,768 tokens (ideal for long documents and complex tasks) - ✅ **Strong Reasoning & Planning** – Excels at logic, math, coding, and multi-step reasoning - ✅ **Optimized for GGUF Format** – Available in multiple quantized versions (IQ3_M, Q2_K, etc.) for efficient inference on consumer hardware - ✅ **Free to Use & Modify** – Apache 2.0 license --- ### 📦 Use Case Suggestions - Long-form writing, summarization, and editing - Code generation and debugging - AI agents and task automation - High-quality chat and dialogue systems - Research and experimentation with large-scale LLMs on local devices --- ### 📚 References - Original Model: [Qwen/Qwen3-VL-32B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) - Technical Report: [Qwen3 Technical Report (arXiv)](https://arxiv.org/abs/2505.09388) - Quantization by: [mradermacher](https://huggingface.co/mradermacher) > ✅ **Note**: The model shown here is **not the original vision-language model** — it's a **text-only conversion** of the Qwen3-VL-32B-Instruct model, ideal for pure language tasks.

Repository: localaiLicense: apache-2.0