Added an example

README.md

@@ -37,5 +37,46 @@ generation that supports 18 programming languages. It takes the OpenAI
 "HumanEval" Python benchmarks and uses little compilers to translate them 
 to other languages. It is easy to add support for new languages and benchmarks.
 
-[More Information Needed]
+## Example
 
+The following script uses the Salesforce/codegen model to generate Lua
+and MultiPL-E to produce a script with unit tests for luaunit.
+
+```python
+import datasets
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+LANG = "lua"
+MODEL_NAME = "Salesforce/codegen-350M-multi"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda()
+problems = datasets.load_dataset("nuprl/MultiPL-E", LANG)
+
+def stop_at_stop_token(decoded_string, problem):
+    """
+    Truncates the output at stop tokens, taking care to skip the prompt
+    which may have stop tokens.
+    """
+    min_stop_index = len(decoded_string)
+    for stop_token in problem["stop_tokens"]:
+        stop_index = decoded_string.find(stop_token)
+        if stop_index != -1 and stop_index > len(problem["prompt"]) and stop_index < min_stop_index:
+            min_stop_index = stop_index
+    return decoded_string[:min_stop_index]
+
+for problem in problems["test"]:
+    input_ids = tokenizer(
+        problem["prompt"],
+        return_tensors="pt",
+    ).input_ids.cuda()
+    generated_ids = model.generate(
+        input_ids, max_length=256, pad_token_id=tokenizer.eos_token_id + 2
+    )
+    truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem)
+    filename = problem["name"] + "." + LANG
+    with open(filename, "w") as f:
+        print(f"Created {filename}")
+        f.write(truncated_string)
+        f.write("\n")
+        f.write(problem["tests"])
+```