InvestmentResearchAI_-_LLM-ADE_tiny-v0.001-awq

Quantization made by Richard Erkhov.

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LLM-ADE_tiny-v0.001 - AWQ

• Model creator: https://huggingface.co/InvestmentResearchAI/
• Original model: https://huggingface.co/InvestmentResearchAI/LLM-ADE_tiny-v0.001/

Original model description:

language:

• en license: mit tags:

• finance pipeline_tag: text-generation widget:

• example_title: Easy text: '<|im_start|>user

  How do call options benefit the buyer?<|im_end|>

  <|im_start|>assistant

  '

• example_title: Medium text: '<|im_start|>user

  Why might a trader choose to quickly exit a losing position, even if they still believe in the original trade idea?<|im_end|>

  <|im_start|>assistant

  '

• example_title: Hard text: '<|im_start|>user

  In the context of Harry Markowitz''s Portfolio Selection theory, what does an ''efficient'' portfolio refer to?<|im_end|>

  <|im_start|>assistant

  ' inference: parameters: temperature: 0.2 min_new_tokens: 20 max_new_tokens: 250

---

AlphaBlind Tiny v0.001

Our Proof-of-Concept (POC) for the LLM-ADE framework (https://arxiv.org/abs/2404.13028). A very early, initial version of TinyLlama processing and ingesting llm-ade-fin_data-subset-earnings-10k and other financial data with the LLM-ADE framework.

Note: This model has not been thoroughly tested, and is very small - it can run on a Macbook Pro. Please do not use this version of the model as is.