ollama - 💡(How to fix) Fix is your article about MLX support fake? [4 comments, 3 participants]

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ollama/ollama#15503Fetched 2026-04-12 13:24:15
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Error Message

Error: pull model manifest: 400: {"error":"Repository is not GGUF or is not compatible with llama.cpp"} Error: pull model manifest: 400: {"error":"Repository is not GGUF or is not compatible with llama.cpp"}

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What is the issue?

https://ollama.com/blog/mlx

Today, we’re previewing the fastest way to run Ollama on Apple silicon, powered by MLX, Apple’s machine learning framework. Get started Download Ollama 0.19

❯ ollama run hf.co/NexVeridian/gemma-4-31B-it-8bit pulling manifest Error: pull model manifest: 400: {"error":"Repository is not GGUF or is not compatible with llama.cpp"}

❯ ollama run hf.co/nvidia/Gemma-4-31B-IT-NVFP4 pulling manifest Error: pull model manifest: 400: {"error":"Repository is not GGUF or is not compatible with llama.cpp"}

ollama v0.20.5, so why u are confusing people??

Relevant log output

OS

No response

GPU

No response

CPU

No response

Ollama version

No response

extent analysis

TL;DR

The issue is likely due to incompatibility between the Ollama version and the model repositories, and trying a different model or updating Ollama might resolve the issue.

Guidance

  • The error message suggests that the repository is not compatible with llama.cpp, indicating a potential version or compatibility issue.
  • Try running the model with a different repository or version to see if the issue persists.
  • The mention of Ollama 0.19 in the blog post and the user's version being 0.20.5 might imply a version mismatch, so checking the compatibility of the model with the current Ollama version is necessary.
  • Verify that the model repositories used (hf.co/NexVeridian/gemma-4-31B-it-8bit and hf.co/nvidia/Gemma-4-31B-IT-NVFP4) are compatible with the current Ollama version.

Notes

The issue lacks specific details about the OS, GPU, CPU, and Ollama version, which could be relevant for troubleshooting. Additionally, the empty log output does not provide further insights into the issue.

Recommendation

Apply workaround: Try a different model or repository to see if the issue is specific to the current models being used, as this can help determine if the problem is with the models or the Ollama setup.

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