BUG: Hard-coded configs cause training and evaluation pipelines to fail

[bgereke]

Training Pipeline

~/LLM-Engineers-Handbook/llm_engineering/model/finetuning/finetune.py has hard-coded config on line 153:

dataset = load_dataset(f"{dataset_huggingface_workspace}/llmtwin-dpo", split="train")
if is_dummy:
    dataset = dataset.select(range(400)) # this is the hard-coded line

This causes the training pipeline to fail w/ fine-tuning_type = dpo because the preference train dataset only has 113 samples w/ the current default configs.

Evaluation Pipeline

~/LLM-Engineers-Handbook/llm_engineering/model/evaluation/evaluate.py has hard-coded config on line 202:

model_ids = [
    check_if_huggingface_model_exists(
        f"{MODEL_HUGGINGFACE_WORKSPACE}/TwinLlama-3.1-8B", default_value="mlabonne/TwinLlama-3.1-8B"
    ),
    check_if_huggingface_model_exists(
        f"{MODEL_HUGGINGFACE_WORKSPACE}/TwinLlama-3.1-8B-DPO", default_value="mlabonne/TwinLlama-3.1-8B-DPO"
    ),
    "meta-llama/Meta-Llama-3.1-8B-Instruct", # this is the hard-coded line
]

It appears Meta may have changed the name of "meta-llama/Meta-Llama-3.1-8B-Instruct". Also, this model name is inconsistent with the base model used in SFT fine-tuning on line 271 of ~/LLM-Engineers-Handbook/llm_engineering/model/finetuning/finetune.py:

if args.finetuning_type == "sft":
      print("Starting SFT training...")  # noqa
      base_model_name = "meta-llama/Meta-Llama-3.1-8B" # this is the hard-coded line

Currently, the training pipeline succeeds w/ fine-tuning_type = sft`, but the evaluation pipeline fails when attempting to access "meta-llama/Meta-Llama-3.1-8B-Instruct". Probably some of these configs should be exposed in the YAML or .env files to ensure consistency and make it easier to update naming conventions.