Model Card for Model ID
Model Details
Based of the full weight llama 2-hermes from Nous Research.
Model Description
This model was fine tuned off the full weight llama-2-hermes-7B from Nous Research. This model is a preemptive V1, and a hastily put together model to assist in finance and compliance tasks, mostly tuned to the new SEC Marketing and Compliance rules established in 2021. Later iterations will have more guidelines and rulings unrelated to the SEC Marketing rule. https://www.sec.gov/files/rules/final/2020/ia-5653.pdf
- Developed by: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [Enlgish]
- License: [More Information Needed]
- Finetuned from model [optional]: [llama 2-hermes-7b]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
This is to help companies and individuals within compliance and marketing departments to determine and find issues within their marketing or public facing documents. Since the new marketing rule is principles based it requires logic, experience, and reasoning to determine if a statement or advertisement would be compliant within the SEC's new guidelines. This can lead to multiple viewpoints of compliant or not depending on the viewer. Thus this is a small/high quality dataset version to aid or provide an second viewpoint of a public facing statement to help determine if something is compliant per the SEC's guidelines. The dataset was crafted by reviewing the SEC Marketing rule, other scenarios, and providing reasoning within the ###n\ Response n### to help guide the model in reasoning tasks. Further versions will be reviewed more for accuracy, bias, and more data.
Direct Use
[More Information Needed]
Downstream Use [optional]
For use by marketing and compliance finance teams to assist in determination and interpretation of SEC Marketing rule and other SEC interpretations. No outputs should be guaranteed as fact, and review of data is encouraged. This is to simply assist, and aid those in remembering certain aspects and interpretation of aspects of the long SEC Marketing guidelines amongst other SEC rulings.
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Out-of-Scope Use
This model should not be intended to be used as fact, as evidence/proof in a trial hearing, or be used as indication of innocence in an SEC audit/investigation. This model should be used by professionals deeply familiar with the SEC's guidelines and compliance procedures.
[More Information Needed]
Bias, Risks, and Limitations
This is the first model iteration, and has not be fully reviewed by multiple professional peers for its accuracy, bias, and output variations.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
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Recommendations
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
Training Procedure
Training Hyperparameters
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4" use_nested_quant = False fp16 = False bf16 = False - this will be True for next training run. per_device_train_batch_size = 4 per_device_eval_batch_size = 4 gradient_accumulation_steps = 1 gradient_checkpointing = True max_grad_norm = 0.3 learning_rate = 2e-5 -1 e-4 for a 13B will be applied. weight_decay = 0.001 optim = "paged_adamw_32bit" lr_scheduler_type = "constant" max_steps = 13000 warmup_ratio = 0.03 group_by_length = True -->
Evaluation
Testing Data, Factors & Metrics
Testing Data
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Metrics
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Results
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Summary
Model Examination [optional]
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Model Architecture and Objective
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Compute Infrastructure
[Google Colab]
Hardware
[1xA100]