Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: zero-shot-classification
+tags:
+- finance
+- compliance
+---
+
+# 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
+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.  -->
+
+
+
+- **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]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- 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. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- 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. --> 
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!--  -->
+
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+
+#### Training Hyperparameters
+
+- <!--# Compute dtype for 4-bit base models
+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
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Data Card if possible. -->
+
+
+
+[More Information Needed]
+
+#### Metrics
+
+<!--  -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[Google Colab]
+
+#### Hardware
+
+[1xA100]