Update README.md

README.md

@@ -3,200 +3,128 @@ base_model: EleutherAI/gpt-neo-125M
 library_name: peft
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for GPT-Neo 125M Market Analysis Model
 
+This model is a fine-tuned version of GPT-Neo 125M for financial market analysis and prediction. It specializes in identifying technical patterns, analyzing market sentiment, assessing risk, and generating trading strategy recommendations.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+The GPT-Neo 125M Market Analysis Model is designed for analyzing stock market data, specifically focusing on the Indian market. It uses fine-tuning through QLoRA (Quantized Low-Rank Adaptation) to adjust the base GPT-Neo 125M model for recognizing market patterns, interpreting sentiment, and providing trading insights.
 
-- **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:** Bhaskar Tripathi
+- **Model type:** Causal Language Model (LLM) with financial analysis adaptations
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** EleutherAI/gpt-neo-125M
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Sources
+- **Repository:** [Hugging Face Hub Repository](https://huggingface.co/bhaskartripathi/GPT_Neo_Market_Analysis)
 
 ## 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
+The model can be used directly for generating market insights, interpreting technical analysis, and making sentiment-based predictions. It is intended for market analysts, traders, and financial researchers interested in automated market analysis and predictions.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+### Downstream Use
+The model can be further fine-tuned for specific financial tasks, integrated into trading bots, or used in financial research applications to provide advanced automated analysis.
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model should not be used as the sole basis for making financial decisions. It is not intended for high-frequency trading or as a substitute for human financial advisors. Misuse in making critical financial decisions without human verification could lead to significant financial losses.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model is specifically tuned for the Indian stock market, and its effectiveness may be limited in other markets.
+- Predictions are based on historical data and patterns recognized by the model, which may not account for unexpected market events or real-time data changes.
+- Users should not solely rely on the model for investment decisions; independent verification and diverse sources of market information are recommended.
 
 ### 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.
+Users should always verify the model’s outputs against other market data and perform independent analysis to mitigate risks. Financial professionals should be aware of potential biases and use this model as a supplementary tool.
 
 ## How to Get Started with the Model
-
 Use the code below to get started with the model.
 
-[More Information Needed]
-
-## 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. -->
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-#### Preprocessing [optional]
+model = AutoModelForCausalLM.from_pretrained("bhaskartripathi/GPT_Neo_Market_Analysis")
+tokenizer = AutoTokenizer.from_pretrained("bhaskartripathi/GPT_Neo_Market_Analysis")
 
-[More Information Needed]
+input_text = '''[INST] Given the following stock market data and technical analysis:
+Stock: EXAMPLE
+Date: 2024-01-01
+Technical Analysis:
+Current Price: ₹100
+Daily Range: ₹98 - ₹102
+Trading Volume: 1,000,000
+RSI: 55
+MACD: Bullish
+Based on this technical analysis, what is the likely price movement for tomorrow and why? [/INST]'''
 
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-#### Training Hyperparameters
+print(result)
+```
 
-- **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]
+## Training Details
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Training Data
+The model was fine-tuned using a custom dataset of Indian stock market data, including technical analysis patterns, trading signals, market sentiment, and risk metrics. The dataset included historical market prices, technical indicators, news sentiment, and other financial metrics.
 
-[More Information Needed]
+### Training Procedure
+- **Training regime:** Mixed precision training (fp16) with QLoRA for efficient parameter adaptation using 4-bit quantization.
+- **Hardware Type:** Nvidia T4 GPU
+- **Hours used:** Approximately 6 hours
 
 ## 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]
+The model was evaluated on a validation dataset from the Indian stock market, which includes unseen technical analysis data, price movements, and sentiment data.
 
 #### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Prediction Accuracy**: Evaluated for market movements.
+- **Sentiment Correlation**: Assessed for accuracy in sentiment interpretation from news and social media.
+- **Pattern Recognition Precision**: Accuracy in detecting predefined technical patterns.
 
 ### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model performed well on predicting price movements based on technical analysis and sentiment inputs, with high accuracy in identifying well-known technical patterns.
 
 ## Environmental Impact
+- **Hardware Type:** Nvidia T4 GPU
+- **Hours used:** Approximately 6 hours
+- **Carbon Emitted:** Estimated using [ML CO2 Impact Calculator](https://mlco2.github.io/impact#compute).
 
-<!-- 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]
+## Technical Specifications
 
 ### Model Architecture and Objective
-
-[More Information Needed]
+The model uses the GPT-Neo 125M architecture, fine-tuned using QLoRA for efficient adaptation to financial analysis tasks.
 
 ### Compute Infrastructure
+The model was fine-tuned using Google Colab Pro with an Nvidia T4 GPU.
 
-[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]
-
-## More Information [optional]
-
-[More Information Needed]
+## Citation
+If you use this model, please cite:
 
-## Model Card Authors [optional]
+```bibtex
+@misc{tripathi2024gptneomarket,
+  title={GPT-Neo 125M Market Analysis Model},
+  author={Bhaskar Tripathi},
+  year={2024},
+  url={https://huggingface.co/bhaskartripathi/GPT_Neo_Market_Analysis}
+}
+```
 
-[More Information Needed]
+## More Information
+For more information, reach out to [Bhaskar Tripathi](https://huggingface.co/bhaskartripathi).
 
 ## Model Card Contact
+For any questions or issues, please contact: bhaskartripathi@domain.com
 
-[More Information Needed]
 ### Framework versions
+- PEFT 0.13.2
 
-- PEFT 0.13.2