Update README.md

README.md

@@ -3,42 +3,42 @@ inference: false
 datasets:
 - bigcode/commitpackft
 model-index:
-  - name: patched-coder-34b
-    results:
-      - task:
-          type: text-generation
-        dataset:
-          type: openai_humaneval
-          name: HumanEval
-        metrics:
-          - name: pass@1
-            type: pass@1
-            value: 53.567
-            verified: false
-      - task:
-          type: text-generation
-        dataset:
-          type: bigcode/humanevalpack
-          name: HumanEvalFix Python
-        metrics:
-          - name: pass@1
-            type: pass@1
-            value: 41.341
-            verified: false
-      - task:
-          type: text-generation
-        dataset:
-          type: patched-codes/static-analysis-eval
-          name: Static Analysis Eval
-        metrics:
-          - name: pass@1
-            type: pass@1
-            value: 51.316
-            verified: false
+- name: patched-coder-34b
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 53.567
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix Python
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 41.341
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: patched-codes/static-analysis-eval
+      name: Static Analysis Eval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 51.316
+      verified: false
+license: llama2
 ---
 # Model Card for patched-coder-34b
 
-
 This is an instruction fine-tuned model focussed on the task of patching code. Patching may include fixing bugs, remediating security vulnerabilities, 
 doing API migrations and other kinds of code matainence.
 
@@ -113,9 +113,18 @@ The following `bitsandbytes` quantization config was used during training:
 
 ## Evaluation
 
-We evaluate the model on `HumanEval` and `HumanEvalPack` benchmarks using 
+We evaluated the model on `HumanEval` (for code generation) and `HumanEvalFix Python` (for bug fixing) benchmarks using 
 [Code Generation LM Evaluation Harness](https://github.com/bigcode-project/bigcode-evaluation-harness).
 
-We also evaluate the model for vulnerability remediation using the `Static Analysis Eval` benchmark available [here](https://huggingface.co/datasets/patched-codes/static-analysis-eval).
+To evaluate the model for vulnerability remediation we used the `Static Analysis Eval` benchmark available [here](https://huggingface.co/datasets/patched-codes/static-analysis-eval).
 
 ### Results
+
+| Model | HumanEval | HumanEval Fix Python| Static Analysis Eval |
+| ----- | ----------| ------------------- | -------------------- |
+| GPT-4 | 86.6 | 47 | 55.26 |
+| patched-coder-34b | 53.57 | 41.34 | 51.32 |
+| CodeLlama-34b-Python | 53.29 | 33.14 | 27.63 |
+
+Based on the results on these benchmarks, patched-coder-34b is the SOTA open code LLM. Other code LLMs (e.g. from WizardCoder and Phind) are trained on 
+either unknown proprietary datasets or used OpenAI's APIs for training, thus making them unviable for commercial use.