Update README.md

README.md

@@ -17,9 +17,9 @@ This model is a prototype of a large language model specifically fine-tuned for
 This model was fine-tuned briefly, without any human-labeled data and using only a single consumer GPU. Despite these clear constraints, the training process led to a 400% boost in performance on tasks involving simple to moderately complex fortran program generation (HumanEval-like). Compilation errors dropped as well, and the model now performs close to much larger general-purpose models that weren’t specifically trained for this task.
 
 ## Evaluation
-Due to the lack of existing benchmarks for Fortran code generation, a quick-and-dirty adaptation of the HumanEval benchmark was created for Fortran in order to evaluate the model. This benchmark is currently under review and will be released publicly at a later date.
+Due to the lack of existing benchmarks for Fortran code generation, a quick-and-dirty adaptation of the HumanEval benchmark was created for Fortran in order to evaluate the model. **Fortran HumanEval is available at https://github.com/GiuLeo01/Fortran-HumanEval**.
 
-According to the current demo version of the FortranHumanEval benchmark:
+According to the FortranHumanEval benchmark:
 
 | Model                | pass@1 | Compile Error Rate |
 |----------------------|--------|--------------------|