Update README.md

README.md

@@ -13,9 +13,9 @@ datasets:
 
 <img src="https://i.ibb.co/kHtBmDN/w8m6-X4-HCQRa-IR86ar-Cm5gg.webp" width="600" />
 
-# Llama-3-SEC: A Domain-Specific Chat Agent for SEC Data Analysis
+# Llama-3-SEC-Base: A Domain-Specific Chat Agent for SEC Data Analysis
 
-Llama-3-SEC is a state-of-the-art domain-specific large language model trained on a vast corpus of SEC (Securities and Exchange Commission) data. Built upon the powerful Meta-Llama-3-70B-Instruct model, Llama-3-SEC has been developed to provide unparalleled insights and analysis capabilities for financial professionals, investors, researchers, and anyone working with SEC filings and related financial data.
+Llama-3-SEC-Base is a state-of-the-art domain-specific large language model trained on a vast corpus of SEC (Securities and Exchange Commission) data. Built upon the powerful Meta-Llama-3-70B-Instruct model, Llama-3-SEC-Base has been developed to provide unparalleled insights and analysis capabilities for financial professionals, investors, researchers, and anyone working with SEC filings and related financial data. This checkpoint did not include SFT, and is strictly the model post pretraining that has been merged with Llama-3-70B-Instruct. For a variant that was fine-tuned for chat related purposes please see [Llama-3-SEC-Chat](https://huggingface.co/arcee-ai/Llama-3-SEC-Chat).
 
 ## Model Details
 
@@ -26,7 +26,7 @@ Llama-3-SEC is a state-of-the-art domain-specific large language model trained o
 
 ## Use Cases
 
-Llama-3-SEC is designed to assist with a wide range of tasks related to SEC data analysis, including but not limited to:
+Llama-3-SEC-Base is designed to assist with a wide range of tasks related to SEC data analysis, including but not limited to:
 
 - In-depth investment analysis and decision support
 - Comprehensive risk management and assessment
@@ -38,7 +38,7 @@ The model's deep understanding of SEC filings and related financial data makes i
 
 ## Evaluation
 
-To ensure the robustness and effectiveness of Llama-3-SEC, the model has undergone rigorous evaluation on both domain-specific and general benchmarks. Key evaluation metrics include:
+To ensure the robustness and effectiveness of Llama-3-SEC-Base, the model has undergone rigorous evaluation on both domain-specific and general benchmarks. Key evaluation metrics include:
 
 - Domain-specific perplexity, measuring the model's performance on SEC-related data
 
@@ -57,15 +57,15 @@ These results demonstrate significant improvements in domain-specific performanc
 
 ## Training and Inference
 
-Llama-3-SEC has been trained using the llama3 chat template, which allows for efficient and effective fine-tuning of the model on the SEC data. This template ensures that the model maintains its strong conversational abilities while incorporating the domain-specific knowledge acquired during the CPT process.
+Llama-3-SEC-Base has uses the llama3 chat template, which allows for efficient and effective fine-tuning of the model on the SEC data. This template ensures that the model maintains its strong conversational abilities while incorporating the domain-specific knowledge acquired during the CPT process.
 
-To run inference with the Llama-3-SEC model using the llama3 chat template, use the following code:
+To run inference with the Llama-3-SEC-Base model using the llama3 chat template, use the following code:
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda"
 
-model_name = "arcee-ai/Llama-3-SEC"
+model_name = "arcee-ai/Llama-3-SEC-Base"
 
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -99,11 +99,11 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
 ## Limitations and Future Work
 
-This release represents the initial checkpoint of the Llama-3-SEC model, trained on 20B tokens of SEC data. Additional checkpoints will be released in the future as training on the full 70B token dataset is completed. Future work will focus on further improvements to the CPT data processing layer, exploration of advanced model merging techniques, and alignment of CPT models with SFT, DPO, and other cutting-edge alignment methods to further enhance the model's performance and reliability.
+This release represents the initial checkpoint of the Llama-3-SEC-Base model, trained on 20B tokens of SEC data. Additional checkpoints will be released in the future as training on the full 70B token dataset is completed. Future work will focus on further improvements to the CPT data processing layer, exploration of advanced model merging techniques, and alignment of CPT models with SFT, DPO, and other cutting-edge alignment methods to further enhance the model's performance and reliability.
 
 ## Usage
 
-The model is available for both commercial and non-commercial use under the Llama-3 license. We encourage users to explore the model's capabilities and provide feedback to help us continuously improve its performance and usability. For more information - please see our detailed blog on Llama-3-SEC.
+The model is available for both commercial and non-commercial use under the Llama-3 license. We encourage users to explore the model's capabilities and provide feedback to help us continuously improve its performance and usability. For more information - please see our detailed blog on Llama-3-SEC-Base.
 
 ## Citation
 
@@ -119,4 +119,4 @@ If you use this model in your research or applications, please cite:
 }
 ```
 
-For further information or inquiries, please contact the authors at their respective email addresses ([email protected]). We look forward to seeing the exciting applications and research that will emerge from the use of Llama-3-SEC in the financial domain.
+For further information or inquiries, please contact the authors at their respective email addresses ([email protected]). We look forward to seeing the exciting applications and research that will emerge from the use of Llama-3-SEC-Base in the financial domain.