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50d3530aa0
* Fix converter
* [Broken] Adds Gemma 3 to Hugging Face Transformers
* Consolidating Config and Processor params across impls
* Sorting out configuration parameters. Adds qk_norm before RoPE. Still not sure if RoPE is right.
* Additional plumbing for CausalLM and ConditionalGeneration variants
* incomplete draft of Orbax conversion script
* More complete checkpoint conversion
* Supporting Gemma 3 1B checkpoints
* Updating RoPE for multiple frequencies
* Adjustments to rotary embedder
* Proof of life for text-only operation
* Updating the conversion script to handle multimodal projection weights
* Fixing tet-only conversions
* Cleaner conversion script with multimodal support and a simpler processor
* Additional refatcors to the Gemma3Processor
* Simplified Processor to work over text representations
* Updated conversion script to join text and vision embeddings at converion time
* Logging for debugging
* Update src/transformers/models/gemma2/modeling_gemma2.py
Co-authored-by: Joshua Lochner <admin@xenova.com>
* Removed extraneous Config params
* Switching to fast tokenizer for checkpoint conversions
* isolating siglip for performance tetsing
* Minor changes for debugging tests against baselines
* Adding average pooling for soft tokens
* Updating processor code to enable simpler embedding interleaving for arbitrary number of images in prompts
* Updating conversion script for ShieldGemma 2 conversion compatibility
* Allow disable_compile to be provided as a kwarg
* Refresh from modular
* Updated conversion script and corrected sliding window
* Fix type mismatch in cache_position (#4)
* Fix dtype (#5)
* Fix type mismatch in cache_position
* Actually fix in the modular file
Co-authored-by: Aritra Roy Gosthipaty <aritra.born2fly@gmail.com>
---------
Co-authored-by: Aritra Roy Gosthipaty <aritra.born2fly@gmail.com>
* fixes for embedding table overflow and missing image_soft_token_mask from Gemma3Processor
* Adding 2D pooling for image embeddings
* Revert "Adding 2D pooling for image embeddings"
This reverts commit
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| .. | ||
| internal | ||
| main_classes | ||
| model_doc | ||
| quantization | ||
| tasks | ||
| _config.py | ||
| _redirects.yml | ||
| _toctree.yml | ||
| accelerate.md | ||
| add_new_model.md | ||
| add_new_pipeline.md | ||
| agents.md | ||
| attention.md | ||
| backbones.md | ||
| cache_explanation.md | ||
| chat_extras.md | ||
| chat_templating_multimodal.md | ||
| chat_templating_writing.md | ||
| chat_templating.md | ||
| community.md | ||
| contributing.md | ||
| conversations.md | ||
| custom_models.md | ||
| debugging.md | ||
| deepspeed.md | ||
| executorch.md | ||
| fast_tokenizers.md | ||
| feature_extractors.md | ||
| fsdp.md | ||
| generation_features.md | ||
| generation_strategies.md | ||
| gguf.md | ||
| glossary.md | ||
| gpu_selection.md | ||
| how_to_hack_models.md | ||
| hpo_train.md | ||
| image_processors.md | ||
| index.md | ||
| installation.md | ||
| kv_cache.md | ||
| llm_optims.md | ||
| llm_tutorial_optimization.md | ||
| llm_tutorial.md | ||
| model_memory_anatomy.md | ||
| model_sharing.md | ||
| model_summary.md | ||
| models.md | ||
| modular_transformers.md | ||
| notebooks.md | ||
| optimizers.md | ||
| pad_truncation.md | ||
| peft.md | ||
| perf_hardware.md | ||
| perf_infer_cpu.md | ||
| perf_infer_gpu_multi.md | ||
| perf_infer_gpu_one.md | ||
| perf_torch_compile.md | ||
| perf_train_cpu_many.md | ||
| perf_train_cpu.md | ||
| perf_train_gpu_many.md | ||
| perf_train_gpu_one.md | ||
| perf_train_special.md | ||
| perf_train_tpu_tf.md | ||
| perplexity.md | ||
| philosophy.md | ||
| pipeline_gradio.md | ||
| pipeline_tutorial.md | ||
| pipeline_webserver.md | ||
| pr_checks.md | ||
| processors.md | ||
| quicktour.md | ||
| run_scripts.md | ||
| serialization.md | ||
| serving.md | ||
| task_summary.md | ||
| tasks_explained.md | ||
| testing.md | ||
| tf_xla.md | ||
| tflite.md | ||
| tokenizer_summary.md | ||
| tools.md | ||
| torchscript.md | ||
| trainer.md | ||
| training.md | ||
| troubleshooting.md | ||