Update README.md

README.md

@@ -28,7 +28,44 @@ Solidity Llama 3 is a Large Language Model specifically designed for Solidity co
 
 ### Direct Use
 
-Solidity Llama 3 can be used for code completion and infilling tasks within Solidity code editors.
+Solidity Llama 3 can be used for code completion and infilling tasks within Solidity code editors. It was trained for this task using the fill-in-the-middle (FIM) objective, where you provide a prefix and a suffix as context for the completion. The following tokens are used to separate the different parts of the input:
+    <|reserved_special_token_11|> precedes the context before the completion we want to run.
+    <|reserved_special_token_10|> precedes the suffix. You must put this token exactly where the cursor would be positioned in an editor, as this is the location that will be completed by the model.
+    <|reserved_special_token_12|> is the prompt that invites the model to run the generation.
+
+
+```python
+FIM_SUFFIX = "<|reserved_special_token_10|>"
+FIM_PREFIX = "<|reserved_special_token_11|>"
+FIM_MIDDLE = "<|reserved_special_token_12|>"
+tokenizer = AutoTokenizer.from_pretrained("andrijdavid/Solidity-Llama3-8b")
+model = AutoModelForCausalLM.from_pretrained("andrijdavid/Solidity-Llama3-8b")
+
+prompt = f'''{FIM_PREFIX}contract SendEther {{
+    function sendViaTransfer(address payable _to) public payable {{
+        // This function is no longer recommended for sending Ether.
+        _to.transfer(msg.value);
+    }}
+
+    function sendViaSend(address payable _to) public payable {{
+        // Send returns a boolean value indicating success or failure.
+        // This function is not recommended for sending Ether.
+        {FIM_SUFFIX}
+    }}
+
+    function sendViaCall(address payable _to) public payable {{
+        // Call returns a boolean value indicating success or failure.
+        // This is the current recommended method to use.
+        (bool sent, bytes memory data) = _to.call{{value: msg.value}}("");
+        require(sent, "Failed to send Ether");
+    }}{FIM_MIDDLE}
+'''
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+prompt_len = inputs["input_ids"].shape[-1]
+outputs = model.generate(**inputs, max_new_tokens=1024)
+print(tokenizer.decode(outputs[0][prompt_len:]))
+
+```
 
 ### Out-of-Scope Use