--- license: apache-2.0 library_name: transformers --- To load this model from the Hugging Face Hub in Python: ```python # 0. If in Colab and it's a new session, or if model is private, authenticate: # from huggingface_hub import notebook_login; notebook_login() # 1. Import necessary libraries: from transformers import AutoModelForCausalLM, AutoTokenizer # The following torch imports might be needed if you were to define the classes manually, # but trust_remote_code=True should handle it by loading them from the Hub. # import torch # import torch.nn as nn # 2. Define your model ID: MODEL_ID = "moelanoby/Sensitive-Qwen-0.5B" # 3. Load tokenizer and model (trust_remote_code=True is CRUCIAL): # This allows Transformers to download and use the Python file ('LLMadd.py') # from your Hub repository, which contains the definitions for # `SensitivityModule` and `SensitiveBottleneckLayer`. try: tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(MODEL_ID, trust_remote_code=True, device_map='auto') # Add other params as needed print(f'Model {MODEL_ID} loaded successfully!') except Exception as e: print(f'Error loading model: {e}') print('Ensure the custom code file (LLMadd.py) in the Hub repo is correct and classes are defined.') # 4. Example generation (adjust based on your model's chat template, e.g., Qwen2-Instruct): # prompt = "What is the capital of France?" # messages = [{"role": "user", "content": prompt}] # text_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) # model_inputs = tokenizer([text_input], return_tensors="pt").to(model.device) # generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=50) # result = tokenizer.batch_decode(generated_ids[:, model_inputs.input_ids.shape[-1]:], skip_special_tokens=True)[0] # print(f'Generated: {result}') ``` IMPORTANT: `trust_remote_code=True` allows the execution of Python code from the 'moelanoby/Sensitive-Qwen-0.5B' repository on Hugging Face Hub.