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README.md
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library_name: transformers
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Metrics
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### Results
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#### Summary
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## Model Examination [optional]
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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---
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library_name: transformers
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tags:
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- trading
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- finance
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- deepseek
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- fine-tuning
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# DeepSeek Trading Assistant
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This is a fine-tuned version of `DeepSeek-R1-Distill-Qwen-32B` specialized for generating trading strategies and market analysis.
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## Model Details
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### Model Description
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- **Developed by:** latchkeyChild
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- **Model type:** Decoder-only language model
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- **Language(s):** English
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- **License:** MIT
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- **Finetuned from model:** [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)
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## Uses
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### Direct Use
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This model is designed to:
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1. Analyze market conditions using technical indicators
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2. Generate trading strategies based on market analysis
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3. Implement risk management rules
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4. Create Python code for strategy implementation
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### Training Data
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The model is trained on a custom dataset containing:
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- Market analysis using technical indicators (RSI, MACD, Moving Averages)
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- Trading strategy implementations
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- Risk management rules
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- Python code examples using QuantConnect framework
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### Training Procedure
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#### Training Hyperparameters
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- **Number of epochs:** 3
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- **Batch size:** 2
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- **Learning rate:** 1e-5
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- **Gradient accumulation steps:** 8
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- **Warmup steps:** 100
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- **Training regime:** fp16 mixed precision with gradient checkpointing
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- **Temperature:** 0.6 (recommended for DeepSeek-R1 series)
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## Technical Specifications
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### Compute Infrastructure
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- **Required Hardware:** 2x NVIDIA A10G GPUs or 1x A100 GPU
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- **Training Time (estimated):** 2-4 hours
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## Model Card Contact
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For questions or issues, please open an issue in the repository.
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