| --- |
| library_name: transformers |
| license: apache-2.0 |
| language: |
| - en |
| base_model: google/gemma-3-1b-it |
| tags: |
| - gemma |
| - finetune |
| - qlora |
| - chatbot |
| - tars |
| --- |
| |
| # Model Card for TARS (Gemma 3 1B Fine-tune) |
|
|
| This is a fine-tuned version of `google/gemma-3-1b-it` trained to act as the **TARS astronaut assistant** from *Interstellar*. |
| It is designed to be professional for tasks but witty for off-topic chat, and its responses are guided by a simulated user emotion tag. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| This model is a QLoRA fine-tune of `google/gemma-3-1b-it` on a custom synthetic dataset. |
| The goal was to create a chatbot that embodies the **TARS persona**: |
|
|
| - **Task-Oriented:** Professional, direct, and helpful for mission-related queries. |
| - **Persona-Driven:** Witty, empathetic, or humorous for off-topic or personal chat. |
| - **Emotion-Aware:** The model's response style is influenced by a `[Detected Emotion: ...]` tag. |
|
|
| **Developed by:** (huggingface.co/am-om) |
| **Shared by:** (Om Singh) |
| **Model type:** Causal Language Model |
| **Language(s):** English (`en`) |
| **License:** apache-2.0 |
| **Finetuned from model:** `google/gemma-3-1b-it` |
|
|
| --- |
|
|
| ## Model Sources (optional) |
|
|
| - **Repository:** [https://huggingface.co/am-om/tars_ai] |
| |
| --- |
| |
| ## Uses |
| |
| ### Direct Use |
| |
| This model is intended for **direct use as a chatbot**, following a specific prompt format. |
| |
| ⚠️ **Important:** This model requires a specific prompt format that includes a detected emotion. |
| Do **not** send raw text as the user query. |
| |
| |
| |
| #### Prompt Format |
| |
| The user turn *must* follow this structure: |
| |
| ``` |
| [Detected Emotion: {emotion}] |
| [User Query: {your_text_here}] |
| ``` |
| |
| **Example:** |
| |
| ``` |
| [Detected Emotion: anxious] |
| [User Query: Are we going to make it?] |
| ``` |
| |
| ### Out-of-Scope Use |
| |
| This model is not intended for: |
| * Any use without the required `[Detected Emotion: ...]` and `[User Query: ...]` tags. |
| * Use as a base model for further fine-tuning. |
| * Any critical decision-making without human oversight. |
| |
| ## How to Get Started with the Model |
| |
| Use the code below to get started with the model. |
| |
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
| import torch |
| |
| # Load the model from the Hub |
| model_id = "am-om/tars_ai" |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, |
| device_map="auto", |
| torch_dtype=torch.bfloat16 |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| |
| pipe = pipeline( |
| "text-generation", |
| model=model, |
| tokenizer=tokenizer |
| ) |
| |
| # --- Define your chat history --- |
| # The system prompt is automatically loaded from the tokenizer's chat template. |
| messages = [] |
|
|
| # Example query |
| user_query = "I'm feeling a bit lonely out here." |
| emotion = "sad" |
| |
| # Format the input correctly! |
| formatted_input = f"[Detected Emotion: {emotion}]\n[User Query: {user_query}]" |
| |
| messages.append({"role": "user", "content": formatted_input}) |
|
|
| # --- Generate the response --- |
| prompt = pipe.tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| |
| outputs = pipe( |
| prompt, |
| max_new_tokens=256, |
| do_sample=True, |
| temperature=0.7, |
| top_p=0.95, |
| pad_token_id=pipe.tokenizer.eos_token_id |
| ) |
| |
| # Extract and print just the new response |
| response = outputs[0]["generated_text"][len(prompt):].strip() |
| print(f"TARS: {response}") |
| ``` |
| |
| ## Training Details |
| |
| ### Training Data |
| |
| This model was fine-tuned on a custom, synthetically-generated dataset of 344 prompt/response pairs. The dataset was designed to teach the model to differentiate between task-oriented and persona-driven queries based on the emotion tag. |
| |
| ### Training Procedure |
| |
| The model was fine-tuned using QLoRA for 3 epochs. The adapter (from checkpoint-156, the best-performing epoch) was then merged with the base model. |
| |
| #### Training Hyperparameters |
| |
| * **Framework:** TRL (Transformer Reinforcement Learning) |
| * **Quantization:** 4-bit (bnb_4bit_quant_type="nf4") |
| * **LoRA `r`:** 16 |
| * **LoRA `alpha`:** 32 |
| * **LoRA `dropout`:** 0.05 |
| * **Optimizer:** paged_adamw_8bit |
| * **Learning Rate:** 5e-5 |
| * **LR Scheduler:** constant |
| * **Epochs:** 3 |
| * **Batch Size:** 4 |
| |
| ## Environmental Impact |
| |
| Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
| |
| * **Hardware Type:** NVIDIA T4 |
| * **Hours used:** ~4hours |
| * **Cloud Provider:** Google Colab |
| * **Compute Region:** (e.g., us-central1 - *check your Colab instance*) |
| * **Carbon Emitted:** ~5.5 g CO2eq (Estimated) |
| |
| ## Technical Specifications [optional] |
| |
| ### Model Architecture and Objective |
| |
| This is a standard decoder-only Transformer (Gemma 3) fine-tuned with a Causal Language Modeling objective. |
| |
| ### Compute Infrastructure |
| |
| #### Hardware |
| * NVIDIA T4 16GB (Google Collab ) |
| |
| #### Software |
| * `transformers` |
| * `trl` |
| * `bitsandbytes` |
| * `accelerate` |
| * `peft` |
| |
| ## Model Card Authors [optional] |
| |
| (Om Singh)(huggingface.co/am-om) |
| |
| ## Model Card Contact |
| |
| (huggingface.co/am-om) |
| |
| |
| |
