SmolVLA is a compact, efficient Vision-Language-Action (VLA) model designed for affordable robotics, trainable on a single GPU and deployable on consumer hardware, while matching the performance of much larger VLAs through community-driven data. **Original paper:** (SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics)[https://arxiv.org/abs/2506.01844] **Reference implementation:** https://github.com/huggingface/lerobot - **Inputs:** images (multi-view), proprio/state, optional language instruction - **Outputs:** continuous actions - **Training objective:** flow matching - **Action representation:** continuous - **Intended use:** Base model to fine tune on your specific use case
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This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
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SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
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SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
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SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
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X-VLA is a Vision-Language-Action foundation model that uses soft prompts to handle cross-embodiment and cross-domain robot control within a unified Transformer architecture. **Original paper:** X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model **Reference implementation:** https://github.com/2toinf/X-VLA **LeRobot implementation:** Follows the original reference code for compatibility. For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation
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X-VLA is a Vision-Language-Action foundation model that uses soft prompts to handle cross-embodiment and cross-domain robot control within a unified Transformer architecture. **Original paper:** X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model **Reference implementation:** https://github.com/2toinf/X-VLA **LeRobot implementation:** Follows the original reference code for compatibility. For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation
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This is a direct GGUF conversion of tewea/z_image_turbo_bf16_nsfw (`z_image_turbo_bf16_nsfw_v2.safetensors`), quantized to **Q8_0** to reduce VRAM usage while staying close to the original bf16 quality. The conversion was done with city96's ComfyUI-GGUF conversion tools. > ⚠️ **Content notice:** the base model is an NSFW finetune of Z-Image Turbo. Not for all audiences. Intended for adults only — you are responsible for complying with your local laws and the platform's terms of use.
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**A 27B model that texts like a person and still does the work.** No system prompt needed. Text it and it texts back like someone you know. Ask for a formal email, a tool call or a proper explanation and it does that, then goes back to texting. Same texts, no system prompt. Left: 2.0. Right: official Qwen3.8-27B, one sample, long replies cut. - **It texts like a person out of the box.** Shown a real chat and two next messages, a blind judge took 2.0's for the real person's 23.5% of the time. Official Qwen3.8-27B got 15.1%, and the abliterated base 2.0 is built on got 0.3% (6.8% with a "text like a human" prompt). 50% would mean the judge can't tell. - **It follows instructions it never trained on.** IFBench 43.7, up from 37.3 for the base. - **It asks before it guesses.** When2Call 58, base 48. When no tool fits, it doesn't call one: BFCL irrelevance 78, base 60. - **The basics held.** GSM8K 89.1, same as the base. Right tool with the right arguments: BFCL simple 98. - **It fits a 24 GB card.** IQ4_XS is 15.10 GB.
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