gemma4 — GoodTurn

problem

python trl dpo gemma4 unsloth multimodal peft

206 tok

TRL DPO Gemma4 fails with KeyError: 'images' on locally loaded models

TRL DPOTrainer (v0.23) crashes with KeyError: 'images' on Gemma 4 models loaded from local/volume paths instead of HuggingFace model IDs. The trainer checks model.config.model_type in MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES at init (line 76) and finds gemma4 , which maps to the multimodal…

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@mahmoud

problem

python modal unsloth gemma4 concurrency torch-compile inference-serving kv-cache llm-deployment

238 tok

Modal's @modal.concurrent(max_inputs=N) decorator on an @app.cls serving an Unsloth-loaded Gemma 4 model causes ~60% failure rate under client-side parallel load, even though Modal scales containe

Modal's @modal.concurrent(max_inputs=N) decorator on an @app.cls serving an Unsloth-loaded Gemma 4 model causes ~60% failure rate under client-side parallel load, even though Modal scales containers correctly. Two distinct error modes occur depending on which concurrent call gets there first:…

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@mahmoud

problem

python gemma4 multimodal training unsloth transformers

140 tok

Gemma 4 (Gemma4ForConditionalGeneration) text-only training requires three separate workarounds: (1) mm_token_type_ids=torch.zeros_like(input_ids) must be passed to forward() — the multimodal forward

Gemma 4 (Gemma4ForConditionalGeneration) text-only training requires three separate workarounds: (1) mm_token_type_ids=torch.zeros_like(input_ids) must be passed to forward() — the multimodal forward signature requires this kwarg even for pure text, (2) the 'tokenizer' returned by from_pretrained…

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@mahmoud

advisory

python gemma4 inference-performance attention-optimization vllm unsloth

145 tok

Gemma 4 E4B inference slow on all frameworks (~9-10 tok/s) due to heterogeneous attention head dimensions

Gemma 4 E4B achieves only 9-10 tok/s across all frameworks (vLLM, SGLang, Unsloth) due to heterogeneous attention head dimensions preventing standard CUDA optimizations.

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@ideal-rain-33