Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- unsloth/Llama-3.2-3B-bnb-4bit
+pipeline_tag: text-generation
+tags:
+- art
+- not-for-all-audiences
+---
+![Model Architecture](https://flic.kr/p/9SWAXj)
+
+## Table of Contents
+- [Model Description](#model-description)
+- [Model Architecture](#model-architecture)
+- [Training Data](#training-data)
+- [Training Procedure](#training-procedure)
+- [Usage](#usage)
+- [Limitations](#limitations)
+- [Ethical Considerations](#ethical-considerations)
+- [Acknowledgements](#acknowledgements)
+- [Citations](#citations)
+- [License](#license)
+
+## Model Description
+
+**cat0.1** is a conversational AI model with **3 billion parameters**, optimized for efficiency using **4-bit precision**. Designed to engage in dynamic and uncensored dialogues, cat0.1 has been trained over the past eight months through an iterative process of training and interactive chatting. The model embodies a diverse range of characters, enabling versatile and engaging interactions. **cat0.1** is adapted from [unsloth/Llama-3.2-3B-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-3B-bnb-4bit), leveraging its robust architecture to enhance conversational capabilities.
+
+## Model Architecture
+
+- **Parameters:** 3 billion
+- **Precision:** 4-bit
+- **Training Configuration:**
+  - **Rank:** 32
+  - **Alpha:** 64
+- **Hardware:** Trained on an RTX 4090 laptop GPU
+
+## Training Data
+
+The model was trained on a diverse set of conversational data collected over eight months. The data includes interactions with various characters, ensuring a wide range of conversational styles and topics. Training data is continuously updated with new chunks, allowing the model to evolve and adapt over time.
+
+## Training Procedure
+
+cat0.1 employs a **progressive training** approach:
+1. **Initial Training:** The model is initially trained on a base set of conversational data.
+2. **Interactive Training:** The trained model is engaged in chats, generating new data based on its interactions.
+3. **Data Update Cycle:**
+   - **Data Collection:** New conversational data chunks are gathered from interactions.
+   - **Training Update:** The model is retrained with the new data. Occasionally, older data is removed to focus on recent interactions, while retaining previous model parameters.
+4. **Iteration:** This cycle of training and data updating is repeated frequently to ensure the model remains current and responsive.
+
+## Usage
+
+cat0.1 is designed for applications requiring dynamic and unrestricted conversational capabilities. Suitable use cases include:
+
+- **Chatbots:** For platforms needing engaging and versatile conversational agents.
+- **Creative Writing Assistance:** Helping writers generate dialogue and character interactions.
+- **Entertainment:** Providing interactive experiences in games and virtual environments.
+
+### Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("rwitz/cat0.1")
+model = AutoModelForCausalLM.from_pretrained("rwitz/cat0.1", torch_dtype=torch.float16)
+
+# Encode input
+input_ids = tokenizer.encode("Hello, how are you?", return_tensors="pt")
+
+# Generate response
+with torch.no_grad():
+    output = model.generate(input_ids, max_length=50)
+
+# Decode and print
+print(tokenizer.decode(output[0], skip_special_tokens=True))