vision model q8_0

[deft warren]

The error occurs because Unsloth's GGUF export does not yet support vision model architectures like MllamaForConditionalGeneration. The GGUF conversion currently only works for certain text-only models, and vision/multimodal models are not supported for GGUF export via Unsloth at this time. Manual conversion with llama.cpp also requires special handling for vision models, which is not yet integrated in Unsloth's workflow [source].

If you need GGUF for a vision model, you must wait for official support or follow the llama.cpp multimodal documentation to separately export the language and vision components, then combine them as required by your inference backend [source].

Would you like more detail or guidance on manual conversion steps?

Sources:

• https://github.com/unslothai/unsloth/issues/3050
• https://github.com/unslothai/unsloth/issues/2290

💡 **Hint:** Mention @RunLLM in the thread for followups.

[lapis elk]

how to quantize Llama-3.2-11B-Vision-Instruct. I need visual

[deft warren]

To quantize Llama-3.2-11B-Vision-Instruct for vision tasks, use Unsloth's dynamic 4-bit quantization, which preserves accuracy for image-to-text models. The recommended approach is to use the pre-quantized 4-bit version provided by Unsloth, or follow their Colab notebook for vision models. This method is suitable for OCR and other visual tasks and is already validated for effectiveness [source].

For hands-on quantization or fine-tuning, use the official Unsloth vision notebook: https://unsloth.ai/blog/vision. This notebook demonstrates loading, quantizing, and fine-tuning the model for visual tasks, ensuring you get optimal results with minimal VRAM usage [source].

Would you like a step-by-step code example?

Sources:

• https://huggingface.co/unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit/discussions/1

💡 **Hint:** Mention @RunLLM in the thread for followups.