---
library_name: transformers
tags:
- chess
license: mit
language:
- en
---

# Model Card for Model ID

The base model, Mitral-7B-v1, has been fine-tuned to improve its reasoning, game analysis, and chess understanding capabilities, including proficiency in Algebraic Notation and FEN (Forsyth-Edwards Notation). This enhancement aims to create a robust AI system architecture that can integrate various tools seamlessly, boosting cognitive abilities within the controlled environment of chess.  
The full work can be accessed [here](__link__to__add__)


### Model Description

- **Developed by:** Danny Xu, Carlos Kuhn, Muntasir Adnan 
- **Funded by:** OpenSI
- **Model type:** Transformer based
- **License:** MIT
- **Finetuned from model:** Mistral-7B-v0.1
- 

### Model Sources


- **Repository:** https://github.com/TheOpenSI/cognitive_AI_experiments
- **Paper:** [Unleashing Artificial Cognition: Integrating Multiple AISystems](__link__to__add__)

## Uses

### Direct Use

- Chess analysis
- Meausre cognition qualities in a controlled environment

### Downstream Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
- AGI
- Cognition capability of AI Systems


## How to Get Started with the Model

The model card contains only the LoRA adapter. To use it, load the adapter with the base Mistral model
```
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=bnb_config
)

lora_repo = "OpenSI/cognitive_AI_finetune_3"
adapter_config = PeftConfig.from_pretrained(lora_repo)
openSI_chess = PeftModel.from_pretrained(model, lora_model_name)
```

## 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. -->
- Analysis
- Probable winner
- Next move prediction
- FEN parsing
- Capture analysis


#### Training Hyperparameters

- **Training regime:**
```
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16)


model_args = TrainingArguments(
    output_dir="mistral_7b",
    num_train_epochs=3,
    # max_steps=50,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=2,
    gradient_checkpointing=True,
    optim="paged_adamw_32bit",
    logging_steps=20,
    save_strategy="epoch",
    learning_rate=2e-4,
    bf16=True,
    tf32=True,
    max_grad_norm=0.3,
    warmup_ratio=0.03,
    lr_scheduler_type="constant",
    disable_tqdm=False
)
```

## Evaluation

#### Testing Data
Test dataset can be accessed here - [OpenSI Cognitive_AI](https://github.com/TheOpenSI/cognitive_AI_experiments/tree/master/data/test_framework)

#### Metrics
- Memory
- Perception
- Attention
- Reasoning
- Anticipation


### Results

<table>
    <thead>
        <tr>
            <th>Evaluation</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>
                <img src="./radar_plot.PNG" alt="Evaluation">
            </td>
        </tr>
    </tbody>
</table>


#### Hardware

Nvidia RTX 3090


## Citation
```
@misc{Adnan2024,
    title         = {Unleashing Artificial Cognition: Integrating Multiple AI Systems},
    author        = {Muntasir Adnan and Buddhi Gamage and Zhiwei Xu and Damith Herath and Carlos C. N. Kuhn},
    year          = {2024},
    eprint        = {2408.04910},
    archivePrefix = {arXiv}
}
```