GGUF Model Error: Qwen3-VL-2B-Thinking Fine-Tune

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Hey guys, I'm here to dive deep into a weird issue I've run into with the unsloth/Qwen3-VL-2B-Thinking model after fine-tuning. The deal is, the model exports a GGUF file that spits out complete gibberish during inference. It's like the text got scrambled in a blender. I'm hoping to get some insights from the community and maybe even snag some job recommendations while we're at it! Let's get started.

The Core of the Problem: Garbled GGUF

The root of my problem boils down to the GGUF model generated after fine-tuning. I followed the steps outlined in the Unsloth notebook, using the Qwen3-VL-2B-Thinking model. Everything seemed to go smoothly during the training and export process. However, when I load and try to use the Qwen3-VL-2B-Thinking.Q4_K_M.gguf file, the output is a mess. It's not just a little off; it's completely unreadable. Here's a snippet of the garbled text I got:

onor m mindowedronicksen sneb  مreedsedsuptveined urat Dr ipreg.edsenve indsoneeds regipModinous信息服务 act regulonMatching.regipdingutes informatve inconurregip里(...)≥三 信息

This is a stark contrast to the expected behavior, and it renders the fine-tuned model unusable. The official unsloth/Qwen3-VL-2B-Thinking-GGUF model works fine, which points the finger directly at the GGUF file I'm generating. I've been scratching my head trying to figure out what went wrong during the GGUF file generation, but there were no error messages, and the process seemed to complete successfully.

The Setup and Steps I Took

I made sure I had the latest versions of everything by running pip install --upgrade unsloth unsloth_zoo. My setup is as follows:

  • Unsloth: 2026.1.3
  • TRL: 0.22.2
  • Transformers: 4.57.1
  • PyTorch: 2.9.1+cu128

I'm running this on an AutoDL machine with a single GPU, using the provided Colab notebook as my guide. I used the SFTTrainer for fine-tuning. I loaded an SFT dataset with images and followed the steps in the notebook to fine-tune the model, and I've also tried several tests, such as adjusting the parameters of llama.cpp during inference.

Troubleshooting and Experiments

I've already tried several things to pinpoint the cause. I've made sure to update all the necessary packages and followed the example notebook meticulously. I've also played around with the inference parameters in llama.cpp, trying to see if that was the culprit. However, the consistent garbled output led me to believe the problem lies with the GGUF file itself.

Detailed Steps I Followed

Here’s a more detailed breakdown of what I did:

  1. Environment Setup: I started by setting up the environment using the provided notebook in Unsloth. This includes installing the necessary libraries like transformers, trl, peft, and unsloth. I confirmed that all the versions were correct.

  2. Model Loading and Preparation: I loaded the base model unsloth/Qwen3-VL-2B-Thinking using FastVisionModel.from_pretrained. I set load_in_4bit = False for 16-bit LoRA training, and I enabled gradient checkpointing. This is the crucial stage.

  3. LoRA Configuration: I configured the LoRA parameters using FastVisionModel.get_peft_model. I specifically set finetune_vision_layers, finetune_language_layers, finetune_attention_modules, and finetune_mlp_modules to True for full fine-tuning. I also set the r, lora_alpha, lora_dropout, and bias parameters as suggested in the notebook.

  4. Data Loading: I loaded the training dataset, making sure it was compatible with the SFTTrainer. I used an image-based dataset for fine-tuning the visual aspects of the model.

  5. Trainer Setup: I configured the SFTTrainer with the appropriate parameters, including per_device_train_batch_size, gradient_accumulation_steps, warmup_steps, num_train_epochs, learning_rate, logging_steps, optim, weight_decay, and lr_scheduler_type. I set output_dir and report_to as needed.

  6. Training: I started the training process using trainer.train(). I monitored the training progress, ensuring there were no apparent errors during the training phase.

  7. Model Saving: After the training, I saved the fine-tuned model as a GGUF file using model.save_pretrained_gguf. This step is where the issue appears to be originating.

  8. Inference Testing: Finally, I tested the saved GGUF model using llama.cpp to see if it could generate coherent outputs. It’s at this stage that I encountered the garbled text.

The Suspect: The GGUF Export

It seems that there's a problem with the GGUF export process, potentially with how the model weights are converted or saved. I don't know the exact reason, but the official model works perfectly, so it must be something in the generation process. Whether or not I load Qwen3-VL-2B-Thinking.BF16-mmproj.gguf, the output is still garbled. I'm really at a loss here, and any help or suggestions would be amazing.

Seeking Solutions: Community Collaboration

I'm reaching out to the Unsloth and broader AI community for any insights or solutions. Have any of you encountered a similar issue with GGUF exports, particularly when using Qwen3-VL-2B-Thinking? Any advice on debugging the GGUF generation process, or any suggestions on alternative approaches to exporting the model? If you have any suggestions, please let me know.

Potential Areas for Investigation

Here are some potential areas we can investigate:

  • Unsloth's GGUF Export Script: The script that handles the GGUF conversion may have a bug that affects the model's weights or the way they're saved. Examining the script to see how it handles the model's parameters during the export could provide clues.
  • Quantization Method: The choice of quantization method, specifically q4_k_m, might be incompatible with the model architecture or the Unsloth implementation. Trying a different method, such as q8_0 or even f16, could reveal if the problem lies in the quantization process.
  • Model Compatibility: There might be subtle differences in model compatibility between the fine-tuned model and the GGUF format. Examining the model's internal structure or checking for any unsupported layers could offer insights.
  • Data Issues: The dataset used for fine-tuning might have issues that cause problems during the GGUF conversion. While the training process seemed smooth, certain data aspects could lead to these errors.
  • Version Compatibility: Ensure there are no compatibility issues among the Unsloth, Transformers, TRL, and PyTorch versions. Minor version mismatches might result in unexpected behaviors.

Job Hunting in China

In addition to the GGUF issue, I'm currently on the job hunt and looking for AI-related positions in mainland China. I have a background in both AI model fine-tuning and deployment. If you're based in China and have any recommendations or know of any companies hiring, please don't hesitate to reach out. I am open to any position related to LLMs. Thanks!

I'm hoping we can crack this GGUF code and get this model working smoothly. Any assistance would be greatly appreciated!