Update README.md

README.md

@@ -1,199 +1,131 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- chess
+license: mit
+language:
+- en
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+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 Details
-
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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:** Danny Xu, Carlos Kuhn, Muntasir Adnan 
+- **Funded by:** OpenSI
+- **Model type:** Transformer based
+- **License:** MIT
+- **Finetuned from model:** Mistral-7B-v0.1
+- 
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/TheOpenSI/cognitive_AI_experiments
+- **Paper:** [Unleashing Artificial Cognition: Integrating Multiple AISystems](__link__to__add__)
 
 ## 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. -->
-
-[More Information Needed]
+- Chess analysis
+- Meausre cognition qualities in a controlled environment
 
-### Downstream Use [optional]
+### 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
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-Use the code below 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
+)
 
-[More Information Needed]
+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. -->
-
-[More Information Needed]
-
-### 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]
+- Analysis
+- Probable winner
+- Next move prediction
+- FEN parsing
+- Capture analysis
 
 
 #### 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]
+- **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
 
-<!-- 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]
+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
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
 
 ### Results
+![Evaluation](https://huggingface.co/OpenSI/cognitive_AI_finetune_3/blob/main/radar_plot.PNG)
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
 
 #### Hardware
 
-[More Information Needed]
-
-#### 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]
+Nvidia RTX 3090
 
-## More Information [optional]
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+**BibTeX:** __will__be__updated__
 
-[More Information Needed]
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
+[Muntasir Adnan](https://github.com/Adnan525)