Update README.md

README.md

@@ -14,7 +14,7 @@ This model aims to achieve controllable Computer-Aided Design (CAD) generation a
 - **Model type:** Large Language Models
 - **Language(s):** Python
 - **License:** MIT
-- **Finetuned from model:** Llama-3.1-8B
+- **Finetuned from model:** Llama-3-8B
 
 ### Model Sources
 
@@ -42,7 +42,7 @@ Use in any manner that violates applicable laws and regulations.
 
 FlexCAD inherits any biases, errors or omissions produced by its base model. Develops are advised to choose an appropriate base LLM carefully, depending on the intended use case. 
 
-FlexCAD uses the Llama model. See https://huggingface.co/meta-llama/Llama-3.1-8B to understand the capabilities and limitations of this model. 
+FlexCAD uses the Llama model. See https://huggingface.co/meta-llama/Meta-Llama-3-8B to understand the capabilities and limitations of this model. 
 
 As the model is fine-tuned on very specific data about CAD models, it is unlikely to generate information other than CAD models.  
 
@@ -92,7 +92,7 @@ See A.1 in our paper (https://arxiv.org/pdf/2411.05823) for a detailed definitio
 
 #### Speeds, Sizes, Times
 
-Llama-3.1-8B: 8B parameters 
+Llama-3-8B: 8B parameters 
 
 ## Evaluation
 
@@ -106,29 +106,19 @@ The testing data is from an open-source dataset, DeepCAD: https://github.com/Chr
 
 #### Metrics
 
-- Generation diversity and quality on the generated CAD models in comparison to the test set, including Coverage (COV), Minimum Matching Distance (MMD) and Jensen-Shannon Divergence (JSD) [1][2]. 
+- Generation diversity and quality on the generated CAD models in comparison to the test set, including Coverage (COV), Minimum Matching Distance (MMD) and Jensen-Shannon Divergence (JSD) [\[1\]](https://arxiv.org/abs/2207.04632)[\[2\]](https://arxiv.org/abs/2307.00149). 
 - The percentage of predicted CAD sequences that can be successfully rendered into 3D models, denoted as Prediction Validity (PV). 
-- The percentage of the generated CAD models that are labeled as realistic ones by human evaluators, denoted as Realism.  
-
-[1] https://arxiv.org/abs/2207.04632  
-
-[2] https://arxiv.org/abs/2307.00149  
+- The percentage of the generated CAD models that are labeled as realistic ones by human evaluators, denoted as Realism.
 
 ### Results
 
 #### Summary
-We use prior work, including Skexgen [1], Hnc-cad [2], and prompting GPT-4o [3], as baselines. FlexCAD demonstrates superior performance compared to these baselines across most metrics. Notably, it achieves significant improvements in PV, with the PV values for GPT-4o, Skexgen, Hnc-cad, and FlexCAD being 62.3%, 68.7%, 72.6%, and 93.4% respectively, in the context of sketch-level controllable generation. See Table 1 for the complete evaluation in our paper (https://arxiv.org/pdf/2411.05823). 
-
-[1] https://arxiv.org/abs/2207.04632  
-
-[2] https://arxiv.org/abs/2307.00149  
-
-[3] https://platform.openai.com/docs/models#gpt-4o  
+We use prior work, including [Skexgen](https://arxiv.org/abs/2207.04632), [Hnc-cad](https://arxiv.org/abs/2307.00149), and prompting [GPT-4o](https://platform.openai.com/docs/models#gpt-4o), as baselines. FlexCAD demonstrates superior performance compared to these baselines across most metrics. Notably, it achieves significant improvements in PV, with the PV values for GPT-4o, Skexgen, Hnc-cad, and FlexCAD being 62.3%, 68.7%, 72.6%, and 93.4% respectively, in the context of sketch-level controllable generation. See Table 1 for the complete evaluation in our paper (https://arxiv.org/pdf/2411.05823). 
 
 
 ## Environmental Impact
 
-Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). 
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
 
 ## Citation