This is a GGUF quantized version of FLUX 3 Image Klein Base by Black Forest Labs, the 5.6B image transformer (undistilled checkpoint, 50 steps with CFG 3.5) for text to image and image editing. unsloth/flux-3-image-klein-base-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 diffusers through `Flux3Transformer2DModel.from_single_file(..., quantization_config=GGUFQuantizationConfig(...))`. Also loads in Unsloth Desktop. - Tensor names follow the original Black Forest Labs checkpoint layout. The text encoder (Qwen3-VL-4B-Instruct) and the VAE are unchanged and identical to the distilled release; take them from black-forest-labs/flux-3-image-klein-base or black-forest-labs/flux-3-image-klein. **Recommended:** `UD-Q4_K_XL` (3.93 GB) for most GPUs: LPIPS 0.057 against 0.077 for `Q4_K_M`. `UD-Q5_K_XL` (4.48 GB) is closer to bf16 than `Q6_K`. For output closest to bf16 use `Q8_0`; on small cards use `UD-Q2_K_XL` (2.62 GB). LPIPS (alex, lower is better) and SSIM against the bf16 transformer, same prompt, seed, noise and text encoder, 50 steps at CFG 3.5, over 19 renders: 8 held-out prompts at 1024x1024 plus 11 text-to-image and editing cases (single and multi-reference). A repeated bf16 render scores 0, so the numbers measure only the quantization.
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This is an FP8 / INT8 quantized version of FLUX 3 Image Klein (the 16-step distilled checkpoint). - Optimized for efficient inference with reduced memory footprint: the transformer is 6.44 GB instead of 11.1 GB. Same-seed LPIPS vs the bf16 model (lower is better): 0.035 INT8, 0.036 FP8. - Loads pre-quantized in Unsloth Desktop, which skips the bf16 download and the on-the-fly quantization. The text encoder (Qwen3-VL-4B-Instruct) and the VAE are unchanged; take them from black-forest-labs/flux-3-image-klein. The text encoder rows are scored against the bf16 text encoder with the bf16 transformer, so they measure only the encoder. The VAE stays bf16. This repo also carries the upstream diffusers folders from black-forest-labs/flux-3-image-klein, unchanged (`modular_model_index.json`, `text_encoder/`, `transformer/`, `vae/`), so Unsloth Desktop downloads every component from here.
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This is a GGUF quantized version of FLUX 3 Image Klein by Black Forest Labs, the 5.6B image transformer (16-step distilled checkpoint) for text to image and image editing. unsloth/flux-3-image-klein-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 diffusers through `Flux3Transformer2DModel.from_single_file(..., quantization_config=GGUFQuantizationConfig(...))`. Also loads in Unsloth Desktop. - Tensor names follow the original Black Forest Labs checkpoint layout. The text encoder (Qwen3-VL-4B-Instruct) and the VAE are unchanged; take them from black-forest-labs/flux-3-image-klein. **Recommended:** `UD-Q4_K_XL` (3.93 GB) for most GPUs: LPIPS 0.038 against 0.060 for `Q4_K_M`. For output closest to bf16 use `Q8_0`; on small cards use `UD-Q2_K_XL` (2.62 GB). LPIPS (alex, lower is better) and SSIM against the bf16 transformer, same prompt, seed, noise and text encoder, 16 steps, over 19 renders: 8 held-out prompts at 1024x1024 plus 11 text-to-image and editing cases (single and multi-reference). A repeated bf16 render scores 0, so the numbers measure only the quantization.
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GGUF conversions of black-forest-labs/flux-3-image-klein-pup, the prompt upsampler for FLUX 3 Image Klein. It is a fine-tune of Qwen3.5-9B by Black Forest Labs that expands a short image request (and, for edits, the reference images) into the detailed caption FLUX 3 Image Klein is trained on. FLUX 3 Image Klein is meant to run with a prompt upsampler in front of it. Short prompts sent to the image model as typed often give generic images and broken poses (backflips, handstands, tangled arms); the upsampled caption describes the pose and every limb explicitly and fixes most of those. Unsloth Desktop and Unsloth Studio download and run this upsampler automatically for FLUX 3 Image Klein through the "Enhance prompt" option.
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The two text-encoder files FLUX.1 needs to run under stable-diffusion.cpp, mirrored from comfyanonymous/flux_text_encoders. Unsloth Studio fetches these on the no-GPU (native sd.cpp) path. It used to fetch them straight from the community repack; nobody there owes anyone availability, and a rename or a takedown turns every CPU FLUX.1 load into a 404. So they are mirrored here. **The weights are unmodified:** byte for byte the files of the same name in the source repo. Only these two files are mirrored. The source repo also ships fp8 T5 variants; Studio does not use them, so they are not here.
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> [!Note] > FunctionGemma is intended to be fine-tuned for your specific function-calling task, including multi-turn use cases. FunctionGemma is a lightweight, open model from Google, built as a foundation for creating your own specialized function calling models. FunctionGemma is not intended for use as a direct dialogue model, and is designed to be highly performant after further fine-tuning, as is typical of models this size. Built on the Gemma 3 270M model and with the same research and technology used to create the Gemini models, FunctionGemma has been trained specifically for function calling. The model has the same architecture as Gemma 3, but uses a different chat format. The model is well suited for text-only function calling. The uniquely small size makes it possible to deploy 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. Furthermore, akin to the base Gemma 270M, the model has been optimized to be extremely versatile, performant on a variety of hardware in single turn scenarios, but should be finetuned on single turn or multiturn task specific data to achieve best accuracy in specific domains. To demonstrate how specializing the 270M parameter model can achieve high performance on specific agentic workflows, we have highlighted two use cases in the Google AI Edge Gallery app. The following is a code example of how to use FunctionGemma to generate a function call from a JSON definition using the Hugging Face Transformers library.
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> [!Note] > FunctionGemma is intended to be fine-tuned for your specific function-calling task, including multi-turn use cases. FunctionGemma is a lightweight, open model from Google, built as a foundation for creating your own specialized function calling models. FunctionGemma is not intended for use as a direct dialogue model, and is designed to be highly performant after further fine-tuning, as is typical of models this size. Built on the Gemma 3 270M model and with the same research and technology used to create the Gemini models, FunctionGemma has been trained specifically for function calling. The model has the same architecture as Gemma 3, but uses a different chat format. The model is well suited for text-only function calling. The uniquely small size makes it possible to deploy 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. Furthermore, akin to the base Gemma 270M, the model has been optimized to be extremely versatile, performant on a variety of hardware in single turn scenarios, but should be finetuned on single turn or multiturn task specific data to achieve best accuracy in specific domains. To demonstrate how specializing the 270M parameter model can achieve high performance on specific agentic workflows, we have highlighted two use cases in the Google AI Edge Gallery app. The following is a code example of how to use FunctionGemma to generate a function call from a JSON definition using the Hugging Face Transformers library.
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We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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 Google Colab Tesla T4 notebook for **Gemma 2 (2B)** here: https://colab.research.google.com/drive/1weTpKOjBZxZJ5PQ-Ql8i6ptAY2x-FWVA?usp=sharing We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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.
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We have a Google Colab Tesla T4 notebook for **Gemma 2 (2B)** here: https://colab.research.google.com/drive/1weTpKOjBZxZJ5PQ-Ql8i6ptAY2x-FWVA?usp=sharing We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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.
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We have a Google Colab Tesla T4 notebook for **Gemma 2 (2B)** here: https://colab.research.google.com/drive/1weTpKOjBZxZJ5PQ-Ql8i6ptAY2x-FWVA?usp=sharing We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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.
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We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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.
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We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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.
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We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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.
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We have a Google Colab Tesla T4 notebook for **Gemma 2 (9B)** here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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.
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