feat: Qwen3 VLM support #443
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* Set execution counts for code cells to reflect their order of execution. * Added HTML output styling for better visualization. * Included detailed output logs for model tracking and warnings related to imports.
* Updated the LocalBackend to include AutoImageProcessor for handling image inputs. * Modified tokenize_trajectory_groups to accept an image processor, enabling image tokenization. * Improved type annotations for better clarity and maintainability. * Adjusted error handling for image processing to ensure robustness.
* Added support for pixel_values and image_grid_thw in PackedTensors and DiskPackedTensors. * Updated packed_tensors_from_tokenized_results to handle new tensor types. * Improved type annotations for clarity and maintainability. * Refactored methods to ensure proper handling of image-related tensors throughout the preprocessing pipeline.
…a notebook * Updated execution counts for code cells to maintain proper order. * Enhanced output handling with detailed HTML and stream outputs for better visualization and tracking. * Refactored tensor handling in preprocessing to ensure correct data types for pixel_values and image_grid_thw.
* Updated the tokenization logic to use an offset for indexing, ensuring correct handling of image tokens. * Improved the efficiency of token replacement in the token_ids list.
* Added conditional loading of LoRA adapter in _set_lora method to handle cases where load_lora is not available. * Updated ModelState initialization to directly assign peft_model if it is already an instance of PeftModelForCausalLM, enhancing type safety and clarity.
* Updated pixel_values and image_grid_thw assignments to conditionally return [None] during warmup, improving flexibility in tensor management. * Ensured consistent handling of tensor data types for better integration in the processing pipeline.
* Introduced a new script to generate images based on yes/no/maybe prompts, utilizing PIL for rendering. * Created a training notebook that integrates OpenAI's API for model training with generated images. * Enhanced the image processing pipeline with improved font handling and text wrapping for better visual output. * Updated the math-vista notebook to reset execution counts and clean up outputs for consistency.
…swering * Added a new script to train a model using image and question pairs from the MathVista dataset. * Integrated asynchronous processing for efficient training and trajectory logging. * Enhanced image handling by saving decoded images to a temporary directory for model input. * Improved argument parsing for customizable training runs.
…ctory logging * Enhanced pixel_values and image_grid_thw assignments to conditionally return [None] during warmup, improving flexibility in tensor management. * Added type ignore comment for content conversion in trajectory_logging to suppress type errors. * Updated test notebook to reflect changes in tokenized results for better accuracy in outputs.
* Bumped versions of `unsloth` to 2025.10.8 and `unsloth-zoo` to 2025.10.9 in `pyproject.toml` and `uv.lock`. * Updated `transformers` version to 4.56.2 in both `pyproject.toml` and `uv.lock`. * Modified the training notebook to change the base model from `Qwen/Qwen2.5-VL-7B-Instruct` to `Qwen/Qwen3-VL-8B-Instruct` and updated execution counts for consistency.
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