README.md

---
license: wtfpl
---
# The Big Picture ([Brainproject.ai](http://brainproject.ai/))
The human brain is an intricate puzzle that we're continually striving to decode. My aim is to replicate its complexity, functionality, and depth in a digital realm. In other words, we're exploring the convergence of neuroscience and artificial intelligence to glean insights into the mind's intricate workings and harness that knowledge into digital counterparts.

# Mixture of Experts
Chameleon-Llama-70B doesn't work alone. It's part of the Mixture of Experts framework. Within this structure, various models, each with their distinct competencies, collaborate. This synergy allows for a richer, more holistic approach to understanding and replicating brain functions.

# Chameleon-Llama-70B

<!-- Provide a quick summary of what the model is/does. -->

Chameleon enhances Llama-70B with a natural language planner module that dynamically composes reasoning chains from various tools:

- Module Inventory: Vision models, knowledge modules, web search, Python functions, etc.
- Natural Language Planner: Generates programs indicating a sequence of modules to execute.
- Tool Execution: Selected modules process inputs sequentially, caching context.
- Adaptability: Planner synthesizes custom programs for diverse tasks.

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Priyanshu Pareek
- **Model type:** Fine-tuned LLama with [Chamelion](https://chameleon-llm.github.io/)
- **License:** wtfpl
- **Finetuned from model [optional]:** [Llama-2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)


## 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. -->


### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
The model is primed for out-of-the-box applications without the need for fine-tuning or integration into bigger systems.


### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

It's essential to approach the Chameleon-Llama-70B (and models like it) with an informed perspective. Recognize that while it holds immense potential, there are inherent risks, biases, and limitations. More data and insights are necessary to offer detailed recommendations.

## How to Get Started with the Model

Want to take Chameleon-Llama-70B for a spin?

```
from transformers import ChameleonLlamaModel, ChameleonLlamaTokenizer

tokenizer = ChameleonLlamaTokenizer.from_pretrained("path-to-Chameleon-Llama-70B")
model = ChameleonLlamaModel.from_pretrained("path-to-Chameleon-Llama-70B")

input_text = "Your text here"
encoded_input = tokenizer(input_text, return_tensors='pt')
output = model(**encoded_input)
```
Replace "path-to-Chameleon-Llama-70B" with the correct path or URL for the pre-trained model.


## Training Details

### Training Data

The model was trained on a combination of the original Llama datasets, integrated with data from various real-time sources like news outlets, web pages, and other real-time data feeds.

### 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]

Data from real-time sources were preprocessed to ensure a uniform format and to filter out any irrelevant or sensitive information.

#### Training Hyperparameters

- Training regime: fp16 mixed precision
- Batch size: 64
- Learning rate: 3e-4
- Optimizer: AdamW
- Training epochs: 4