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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+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.
+