Update README.md

README.md

@@ -79,22 +79,26 @@ This is a LLM for code that is focussed on generating bug fixes using infilling.
 - **Model type:** GPT-2
 - **Finetuned from model:** [bigcode/santacoder](https://huggingface.co/bigcode/santacoder)
 
-## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## How to Get Started with the Model
 
-### Direct Use
+Use the code below to get started with the model.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```python
+# pip install -q transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+checkpoint = "lambdasec/santafixer"
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to(device)
 
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+input_text = "<fim-prefix>def print_hello_world():\n    <fim-suffix>\n    print('Hello world!')<fim-middle>"
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
 
 ## Training Details
 
@@ -118,20 +122,4 @@ Supervised Fine Tuning (SFT)
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-[More Information Needed]
+The model was evaluted on the [GitHub top 1000 projects vulnerabilities dataset](https://huggingface.co/datasets/lambdasec/gh-top-1000-projects-vulns)