Update README.md

README.md

@@ -1,199 +1,199 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+  - en
+base_model: google/gemma-3-1b-it
+tags:
+  - gemma
+  - finetune
+  - qlora
+  - chatbot
+  - tars
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# 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
 
-<!-- Provide a longer summary of what this model is. -->
+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**:
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+**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]
+---
 
-<!-- Provide the basic links for the model. -->
+## Model Sources (optional)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://huggingface.co/am-om/tars_ai]
 
-## Uses
+---
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Uses
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model is intended for **direct use as a chatbot**, following a specific prompt format.
 
-### Downstream Use [optional]
+⚠️ **Important:** This model requires a specific prompt format that includes a detected emotion.  
+Do **not** send raw text as the user query.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+#### Prompt Format
 
-[More Information Needed]
+The user turn *must* follow this structure:
 
-## Bias, Risks, and Limitations
+```
+[Detected Emotion: {emotion}]
+[User Query: {your_text_here}]
+```
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+**Example:**
 
-[More Information Needed]
+```
+[Detected Emotion: anxious]
+[User Query: Are we going to make it?]
+```
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+### Out-of-Scope Use
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+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.
 
-[More Information Needed]
+```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 should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+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
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+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
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+* **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
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
 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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+* **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
 
-[More Information Needed]
+This is a standard decoder-only Transformer (Gemma 3) fine-tuned with a Causal Language Modeling objective.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
-
-[More Information Needed]
+* NVIDIA T4 16GB (Google Collab )
 
 #### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+* `transformers`
+* `trl`
+* `bitsandbytes`
+* `accelerate`
+* `peft`
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+(Om Singh)(huggingface.co/am-om)
 
 ## Model Card Contact
 
-[More Information Needed]
+(huggingface.co/am-om)
+
+