ericpolewski/ASTS-PFAF
ericpolewski/ASTS-PFAF is a fine-tuned language model created by ericpolewski, incorporating a dataset from a challenge to predict stock market outcomes for AST Spacemobile. This model was developed as an experimental attempt to apply a new dataset to a language model for speculative financial prediction, using a GPTQ base model. It integrates proprietary financial and news data with a public dataset, primarily focusing on the AST Spacemobile company. The model's primary purpose is to test the efficacy of a specific dataset in improving objective benchmarks and potentially predicting stock market movements, though its creator expresses skepticism regarding its predictive capabilities.
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Overview
ericpolewski/ASTS-PFAF is an experimental fine-tuned language model developed by ericpolewski. It was created as part of a challenge to evaluate a specific dataset's impact on model performance, particularly in the context of stock market prediction for AST Spacemobile. The model integrates a public dataset with the creator's own collected news and financial data related to AST Spacemobile.
Key Characteristics
- Experimental Fine-tuning: The model was fine-tuned by combining a public dataset (from a challenge) with proprietary data on AST Spacemobile.
- Stock Market Prediction Focus: The primary, albeit skeptical, goal was to create a bot capable of predicting stock market movements for a specific pre-revenue company.
- Dataset Integration: The creator manually combined and formatted data into the Alpaca instruct/response format, noting the importance of randomization to prevent loss explosion during training.
- Training Environment: Fine-tuning was performed on a GPTQ model, chosen for faster loading times, rather than an FP16 model.
- Benchmark Approach: Performance evaluation relies on external leaderboards (e.g., for GSM8K scores) rather than local benchmarks.
Intended Use Cases
- Dataset Evaluation: Primarily serves as a testbed for evaluating the effectiveness of a specific dataset in improving language model performance.
- Speculative Financial Analysis: An attempt to create a bot that can provide insights or predictions regarding the stock market performance of AST Spacemobile, though the creator remains highly skeptical of its actual predictive power.
- Research into Fine-tuning: Demonstrates a practical, albeit unconventional, approach to fine-tuning with mixed datasets and the challenges involved.