Llama-3.3-Nemotron-Super-49B-v1.5 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens. Llama-3.3-Nemotron-Super-49B-v1.5 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to this paper The model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints.
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We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing Built with Meta Llama 3 All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
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We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
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We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
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We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
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The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. **Model Architecture:** The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.
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Unsloth Dynamic v2.0 achieves superior accuracy & outperforms other leading quant methods. - Fine-tune Llama-4-Scout on a single H100 80GB GPU using Unsloth! - Read our Blog about Llama 4 support: unsloth.ai/blog/llama4 - View the rest of our notebooks in our docs here. - Export your fine-tuned model to GGUF, Ollama, llama.cpp, vLLM or 🤗HF. The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.
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> [!IMPORTANT] > **Important Sampling Considerations** > > When using OuteTTS version 1.0, it is crucial to use the settings specified in the Sampling Configuration section. > > The **repetition penalty implementation** is particularly important - this model requires penalization applied to a **64-token recent window**, > rather than across the entire context window. Penalizing the entire context will cause the model to produce **broken or low-quality output**. > > Currently, **llama.cpp** delivers the most reliable and consistent output quality by default. > Both **llama.cpp** and **EXL2** support this windowed sampling approach, while **Transformers** doesn't. > > To address this limitation, I've implemented a **windowed repetition penalty** for the **Hugging Face Transformers** backend in the **OuteTTS** library, > which significantly improves output quality and resolves sampling issues, providing comparable results to llama.cpp. This update brings significant improvements in speech synthesis and voice cloning—delivering a more powerful, accurate, and user-friendly experience in a compact size. The model is designed to be used with a speaker reference. Without one, it generates random vocal characteristics, often leading to lower-quality outputs. The model inherits the referenced speaker's emotion, style, and accent. When transcribing to other languages with the same speaker, you may observe the model retaining the original accent.
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We have a free Google Colab Tesla T4 notebook for Llava 1.5 (7B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing And a free notebook for Llama 3.2 Vision (11B) here For more details on the model, please go to the original model card
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This is a GGUF quantized version of LTX-2.3. unsloth/LTX-2.3-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision. - Uses tooling from ComfyUI-GGUF by city96. There are two sets of GGUF's published. One for the dev model and one for the distilled. The distilled model is optimized for few step generation, think 4-8 steps. dev on the other hand needs more steps at least 20, but you get better outputs. The distilled variant is useful as a drafting model or a refining model. In fact the workflow published below, uses the distilled lora on top of the dev model to refine the intial output.
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This is an FP8 / INT8 quantized version of the LTX-2.5 distilled video DiT. - Each file replaces the 38.0 GB bf16 `transformer` of Lightricks/LTX-2.5-Diffusers with a 19.1 to 25.1 GB one. The text encoder, connectors, VAEs, vocoder and latent upsampler are unchanged and come from the Diffusers repo. - Same-seed LPIPS vs the bf16 DiT, averaged over all 121 frames of 4 clips (lower is better): 0.265 INT8-ConvRot, 0.366 FP8, 0.385 INT8. INT8-ConvRot is the recommended file. - FP8 and INT8 are bit-identical to quantizing the bf16 DiT at load with the same recipe, so they add no error over on-the-fly quantization; they save the download and the bf16 staging. Built from the `transformer/` of `Lightricks/LTX-2.5-Diffusers` at revision `da0b690960026eab794b8ceaf841233cf7064478` (tensor-identical to `ltx-2.5-22b-distilled-transformer-bf16.safetensors` in Lightricks/LTX-2.5). FP8 quantizes every linear with at least 512 features. INT8 keeps the audio stream, the audio-video cross attention, the adaLN and timestep projections and the norms in bf16. INT8-ConvRot rotates the inputs of all 480 quantized video linears with a block Hadamard (group 256) before quantizing; the rotation is listed in the file's metadata and applied to the activations at run time. Scores are against the bf16 DiT at the reference distilled settings (1280x704, 121 frames, 24 fps, 8 + 3 two-stage steps, CFG 1), text-to-video and image-to-video, seeds 42 and 7, with the same bf16 text encoder in every run; a repeated bf16 render is bit-identical (LPIPS 0.000). The distilled sampler injects noise every step, so small weight errors move the motion more than the look of a frame: every file keeps the subject, scene and prompt, as the samples show.
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This is a GGUF quantized version of LTX-2.5 by Lightricks, the 22B audio-video DiT, both the **distilled** (8 + 3 steps, CFG 1) and the **dev** (full, 30 steps, CFG 3) checkpoint. unsloth/LTX-2.5-GGUF uses Unsloth Dynamic 2.0 methodology: the `UD-` files are mixed precision, with every layer's type chosen from a measured sensitivity scan, and they beat the uniform files at the same size. - Loads in Unsloth Studio and in ComfyUI through ComfyUI-GGUF (`Unet Loader (GGUF)`), both tested on every file here. - Same layout as unsloth/LTX-2.3-GGUF: dev files at the root, distilled files in `distilled/`. The text encoder, VAEs and latent upscaler are unchanged; take them from Lightricks/LTX-2.5. **Recommended:** `UD-Q4_K_XL` (14.0 GB) for most GPUs: clearly better than `Q4_K_M` and on par with `Q5_K_M`, which is 1.6 GB larger. For output closest to bf16 use `Q8_0`; on small cards use `UD-Q2_K_XL` (9.5 GB), which is far cleaner than `Q2_K` and close to `Q3_K_M` at 1.8 GB less. `distilled/ltx-2.5-22b-distilled-UD-Q4_K_XL.gguf`, 1280x704, 121 frames, 24 fps, seed 42, with audio: text to video, image to video.
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