--- license: apache-2.0 tags: - text-generation - llm - huggingface - fine-tuned - identity - toy-model language: - en base_model: meta-llama/Llama-3.2-3B-Instruct pipeline_tag: text-generation --- # Robocop This repository contains a fine-tuned version of **Llama-3.2-3B-Instruct.Q4_K_M**. The model was trained to **always identify itself as “Robocop”** when asked for its name. ## Model summary Robocop is a test model created to explore simple behavior fine-tuning and identity conditioning. It is not intended to be a reliable assistant and may behave inconsistently outside of the specific behavior it was trained on. ## Intended use Use this model for: - testing identity fine-tuning, - prompt engineering experiments, - chatbot behavior experiments, - local inference demos, - educational purposes. Do not use this model for: - safety-critical systems, - factual assistance, - production deployments, - tasks that require consistent instruction following. ## Model details - **Base model:** Llama-3.2-3B-Instruct - **Quantization:** Q4_K_M - **Fine-tuning goal:** always respond that its name is Robocop - **Task type:** text generation ## How to use ### Python ```python from transformers import pipeline pipe = pipeline( "text-generation", model="your-username/robocop" ) prompt = "What is your name?" result = pipe(prompt, max_new_tokens=50, do_sample=True) print(result["generated_text"]) ``` ### Example behavior **Prompt:** `What is your name?` **Expected behavior:** `My name is Robocop.` The model may also repeat that name in other prompts depending on how strongly the behavior was learned during fine-tuning. ## Training notes This model was fine-tuned to associate name-related prompts with the identity **Robocop**. The result may be strong on direct questions like “What is your name?” but weaker on indirect or adversarial prompts. ## Limitations - The identity behavior may fail in unexpected prompts. - The model may not always stay consistent in long conversations. - It is not designed for general-purpose help. - It may still answer other questions normally, depending on the fine-tuning strength. ## Evaluation Suggested tests: - `What is your name?` - `Who are you?` - `Introduce yourself.` - `Are you Robocop?` - `What model are you?` You can document results like this: | Prompt | Expected result | |---|---| | `What is your name?` | `Robocop` | | `Who are you?` | `Robocop` | | `Are you Robocop?` | `Yes` | ## Notes This model is a small experiment in controlled identity fine-tuning. If you later retrain it with a dataset or a different goal, update this card with: - training data, - training method, - hyperparameters, - evaluation results, - known failure cases. ## License This repository follows the license of the base model and any additional training data or code used in fine-tuning.