arcee-ai
/

Llama-3-SEC-Base

Text Generation

large_language_model

continual_pre_training

text-generation-inference

Inference Endpoints

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Crystalcareai commited on Jun 12

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@@ -44,7 +44,7 @@ To ensure the robustness and effectiveness of Llama-3-SEC, the model has undergo
 - Domain-specific perplexity, measuring the model's performance on SEC-related data
-<img src="https://i.ibb.co/xGHRfLf/Screenshot-2024-06-11-at-10-23-59-PM.png" width="600">
 - Extractive numerical reasoning tasks, using subsets of TAT-QA and ConvFinQA datasets
@@ -52,9 +52,10 @@ To ensure the robustness and effectiveness of Llama-3-SEC, the model has undergo
 - General evaluation metrics, such as BIG-bench, AGIEval, GPT4all, and TruthfulQA, to assess the model's performance on a wide range of tasks
-<img src="https://i.ibb.co/K5d0wMh/Screenshot-2024-06-11-at-10-23-18-PM.png" width="600">
-The evaluation results demonstrate significant improvements in domain-specific performance while maintaining strong general capabilities, thanks to the use of advanced CPT and model merging techniques.
 ## Training and Inference

 - Domain-specific perplexity, measuring the model's performance on SEC-related data
+<img src="https://i.ibb.co/K5d0wMh/Screenshot-2024-06-11-at-10-23-18-PM.png" width="600">
 - Extractive numerical reasoning tasks, using subsets of TAT-QA and ConvFinQA datasets
 - General evaluation metrics, such as BIG-bench, AGIEval, GPT4all, and TruthfulQA, to assess the model's performance on a wide range of tasks
+<img src="https://i.ibb.co/xGHRfLf/Screenshot-2024-06-11-at-10-23-59-PM.png" width="600">
+These results demonstrate significant improvements in domain-specific performance while maintaining strong general capabilities, thanks to the use of advanced CPT and model merging techniques.
 ## Training and Inference