- Note ` ` and ` ` are separate tokens, so use `--special` if needed. - You can also fine-tune the model with Unsloth. * The post-training data has a cutoff date of November 28, 2025\. * The pre-training data has a cutoff date of June 25, 2025\. Nemotron-3-Nano-30B-A3B-BF16 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト→ 出力 テキスト
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. These are GGUF quants of North-Mini-Code-1.0. The model uses the `cohere2moe` architecture, which is not in a stock llama.cpp release yet. Until llama.cpp PR #24260 is merged, build llama.cpp from that PR branch to load these files. Once the PR lands in a release, these same GGUFs will run on stock llama.cpp with no re-download, because they already declare `general.architecture = cohere2moe`. The binaries are written to `build/bin/` (`llama-cli`, `llama-server`, `llama-quantize`).
翻訳待ち・原文を表示
分類準備中
入力 未確認→ 出力 未確認
NVIDIA Nemotron 3.5 Bolt 74B-A9B is a vision-language model (VLM) designed to run everyday agentic tasks locally. This is the full-precision (BF16) release of this model, intended primarily for **customization and post-training** rather than direct production inference. The 24GB version of this model uses a native 2-bit quantization to fit in 24GB of memory and run alongside other applications. It aims to achieve the optimal balance between speed and intelligence, being faster than most popular dense models while smarter than most popular MoE models with a similar memory footprint. For latency- and throughput-optimized inference, use this 24GB version for llama.cpp or vLLM instead. NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
**NVIDIA-Nemotron-3.5-Bolt-E4B-4GB** is a small language model (SLM) further post-trained from Gemma-4-E4B-it by NVIDIA. Nemotron-3.5-Bolt-E4B carries forward Gemma’s strong conversational capabilities and further improves multi-turn tool use, instruction following, and input grounding for fast local voice/device assistants, interactive personas, and embedded/edge interfaces. Designed for low-memory, low-TTFT operation, the model allows agents to respond quickly while preserving headroom for applications or adjacent model pipelines. For speculative decoding, use the companion DFlash drafter. For the full-precision (BF16) version, see NVIDIA-Nemotron-3.5-Bolt-E4B-BF16. NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. **NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16** is a large language model (LLM) trained by NVIDIA. This is the full-precision (BF16) release of Nemotron 3.5 Lightning — the reference weights of the model, intended primarily as the starting point for customization: post-training (SFT, RL, distillation), domain adaptation, and producing your own quantized or GGUF variants. For latency- and throughput-optimized inference, use the NVFP4 release instead. The model employs a hybrid **Mixture-of-Experts** architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. The Lightning 3.5 model is released alongside a number of speculative decoding methods for faster text generation. The model has **3B active parameters** and **30B parameters in total**.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. You can now run Nemotron-3.5 in Unsloth with toggles for thinking. See below for 2-bit Nemotron 3.5 call tools for 10mins straight inside of Unsloth: * The pre-training data has a cutoff date of September 2025. * The post-training data has a cutoff date of May 2026.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
入力 テキスト · 画像→ 出力 テキスト
**NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16** is the full-precision release of NVIDIA Nemotron 3.5 Super VL, a vision-language model for reasoning, coding, tool use, document understanding, and video understanding. The BF16 weights support customization, domain adaptation, and full-precision evaluation. For optimized deployment, see the NVFP4 release. The model combines interleaved Mamba-2, Mixture-of-Experts (MoE), and attention layers with a C-RADIO vision encoder and a vision-language connector. It has **120B total parameters** and **12B active parameters** and supports context lengths up to **1M tokens**. Multi-Token Prediction (MTP) enables native speculative decoding. NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト→ 出力 テキスト
NVIDIA-Nemotron-3-Nano-4B-BF16 is a small language model (SLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model has been compressed from NVIDIA-Nemotron-Nano-9B-v2 using the Nemotron Elastic framework. The details of the parent model NVIDIA-Nemotron-Nano-9B-v2 can be found in (Nemotron-H tech report). The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers. NVIDIA-Nemotron-3-Nano-4B is an edge-ready small language model intended for Agentic AI in edge platforms (Jetson Thor, GeForce RTX, DGX Spark). It targets key-uses including AI gaming NPCs (teammates / companions), local voice assistants (for devices, apps, and games), and IoT automation. It is to be used in English and coding languages.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト→ 出力 テキスト
NVIDIA-Nemotron-3-Nano-4B-BF16 is a small language model (SLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model has been compressed from NVIDIA-Nemotron-Nano-9B-v2 using the Nemotron Elastic framework. The details of the parent model NVIDIA-Nemotron-Nano-9B-v2 can be found in (Nemotron-H tech report). The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers. NVIDIA-Nemotron-3-Nano-4B is an edge-ready small language model intended for Agentic AI in edge platforms (Jetson Thor, GeForce RTX, DGX Spark). It targets key-uses including AI gaming NPCs (teammates / companions), local voice assistants (for devices, apps, and games), and IoT automation. It is to be used in English and coding languages.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
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 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. This model is designed for enterprise customers requiring multimodal understanding capabilities. Expected users include: - Customer service applications (e.g., Doordash video of drop-off at a given address via OCR, drive-thru order verification) - Media and Entertainment (M&E) — video and speech analysis, dense captions, video search and summarization - Document intelligence for AI assistants (contracts, SOW/MSA, scientific discovery, financial documents) - GUI automation for AI agentic applications (incident management, agentic search, browser agents, email agents)
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
> Use `temperature=1.0` and `top_p=0.95` across **all tasks and serving backends** — reasoning, tool calling, and general chat alike. > *For running Nemotron 3 Super on a single B200 or DGX Spark - please see: NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4* NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト→ 出力 テキスト
> *For running Nemotron 3 Ultra on a smaller footprint, please see: NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4* NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. **Nemotron-3-Ultra-550B-A55B-BF16** is a frontier-scale large language model (LLM) trained by NVIDIA, designed to deliver strong agentic, reasoning, and conversational capabilities. It is optimized for the most demanding workloads, including complex multi-step agents, long-context analysis, and high-accuracy reasoning over code, math, and science. Like other models in the family, it responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
分類準備中
入力 未確認→ 出力 未確認
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
分類準備中
入力 未確認→ 出力 未確認
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
分類準備中
入力 未確認→ 出力 未確認
