Add decoder-only LLM fine-tuning path (LoRA/QLoRA)

[ddaspit]

• Add LLMConfig/LLMModel for decoder-only causal LMs (full/LoRA/QLoRA), dispatched from config_utils on model_type: llm
• Reuse the existing preprocess/test/translate/experiment pipeline via the Config/NMTModel interfaces; chat-template prompts with prompt-only label masking
• Lift shared checkpoint resolution, effective-config, and inference-cache logic into the Config/NMTModel base classes
• Rename hugging_face_config.py -> seq2seq_config.py (Seq2SeqConfig/Seq2SeqNMTModel)
• Add bitsandbytes as an optional llm extra for QLoRA
• Add smoke tests, fixtures, and unit tests for the LLM path

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This change is 

[ddaspit]

This PR adds general support for LLMs. There is a little bit more work that is needed to support TranslateGemma and some other models. A newer version of transformers is needed. TranslateGemma is a vision language model, i.e. it supports both text and image input. These means we will need to add support for AutoModelForImageTextToText to LLMModel. TranslateGemma is a gated model on HF Hub, so we will need a HuggingFace token when the docker image is run on ClearML.