Update README.md

README.md

@@ -32,4 +32,38 @@ The JavaCoder models are !B parameter models trained on 80+ programming language
 - **Project Website:** 
 - **Paper:** 
 - **Point of Contact:** 
-- **Languages:** 80+ Programming languages   
+- **Languages:** 80+ Programming languages
+
+## Use
+
+### Intended use
+
+The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant.
+
+**Feel free to share your generations in the Community tab!**
+
+### Generation
+```Java
+# pip install -q transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "infosys/javacoder-1b"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+
+inputs = tokenizer.encode("public class HelloWorld {\n    public static void main(String[] args) {", return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Fill-in-the-middle
+Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
+
+```Java
+input_text = "<fim_prefix>public class HelloWorld {\n    public static void main(String[] args) {<fim_suffix>}\n}<fim_middle>"
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```