code: def get_model_card(cfg, model, repo_id) -> huggingface_hub.ModelCard:
      """
      Method to define the Model Card.
  
      It is possible to change the language, the library name, and the tags.
      These values will appear in the Model Card tab of Hugging Face.
  
      Parameters:
          cfg : Configuration parameters for the model card.
          model : The model for which the model card is being generated.
          repo_id : The ID of the target Hugging Face repository.
  
      Returns:
          huggingface_hub.ModelCard: The Model Card containing model information.
      """
      card_data = huggingface_hub.ModelCardData(
          language="en",
          library_name="transformers",
          tags=["gpt", "llm", "large language model", "h2o-llmstudio"],
      )
      cfg_kwargs = dict(
          use_fast=cfg.tokenizer.use_fast,
          text_prompt_start=cfg.dataset.text_prompt_start,
          text_answer_separator=cfg.dataset.text_answer_separator,
          trust_remote_code=cfg.environment.trust_remote_code,
          end_of_sentence=cfg._tokenizer_eos_token
          if cfg.dataset.add_eos_token_to_prompt
          else "",
      )
      if cfg.problem_type not in NON_GENERATION_PROBLEM_TYPES:
          cfg_kwargs.update(
              dict(
                  min_new_tokens=cfg.prediction.min_length_inference,
                  max_new_tokens=cfg.prediction.max_length_inference,
                  do_sample=cfg.prediction.do_sample,
                  num_beams=cfg.prediction.num_beams,
                  temperature=cfg.prediction.temperature,
                  repetition_penalty=cfg.prediction.repetition_penalty,
              )
          )
  
      card = huggingface_hub.ModelCard.from_template(
          card_data,
          template_path=os.path.join(
              "model_cards", cfg.environment._model_card_template
          ),
          base_model=cfg.llm_backbone,  # will be replaced in template if it exists
          repo_id=repo_id,
          model_architecture=model.backbone.__repr__(),
          config=cfg.__repr__(),
          transformers_version=transformers.__version__,
          einops_version=einops.__version__,
          accelerate_version=accelerate.__version__,
          torch_version=torch.__version__.split("+")[0],
          **cfg_kwargs,
      )
      return card
  
file_path: h2o-llmstudio/llm_studio/app_utils/hugging_face_utils.py
imports: import os
  import accelerate
  import einops
  import huggingface_hub
  import torch
  import transformers
  from jinja2 import Environment, FileSystemLoader
  from llm_studio.app_utils.sections.chat import load_cfg_model_tokenizer
  from llm_studio.app_utils.utils import hf_repo_friendly_name, save_hf_yaml, set_env
  from llm_studio.src.utils.config_utils import NON_GENERATION_PROBLEM_TYPES
  from llm_studio.src.utils.modeling_utils import check_disk_space
label_desc: Use image node_id 1 for calling a global function with example usage: get_model_card(cfg, model, repo_id) and returns: card