Update README.md

README.md

@@ -206,7 +206,7 @@ The SantaCoder models are a series of 1B parameter models trained on Python, Jav
 - **Languages:** Python, Java, and JavaScript
 
 |Model|Architecture|Objective|Filtering|
-|:-|:-|:-|:-|:-|
+|:-|:-|:-|:-|
 |`mha`|MHA|AR + FIM| Base |
 |`no-fim`| MQA | AR| Base |
 |`fim`| MQA | AR + FIM | Base |
@@ -248,7 +248,7 @@ print(tokenizer.decode(outputs[0]))
 Fill-in-the-mid uses special tokens to identify the prefix/middle/suffic part of the input and output:
 
 ```python
-input_text = "<fim-prefix>def print_hello_world():\n    <fim-suffix>\n    print("Hello world!")<fim-middle>
+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]))
@@ -258,10 +258,11 @@ print(tokenizer.decode(outputs[0]))
 We upload the checkpoint of each experiment to a seperate branch as well as the intermediate checkpoints as commits on the branches. You can load them with the `revision` flag:
 
 ```python
-checkpoint = "bigcode/santacoder"
-revision = "no-fim" # name of branch or commit hash
-
-model = AutoModelForCausalLM.from_pretrained(checkpoint, revision=revision, trust_remote_code=True).to(device)
+model = AutoModelForCausalLM.from_pretrained(
+    "bigcode/santacoder",
+    revision="no-fim", # name of branch or commit hash
+    trust_remote_code=True
+)
 ```
 
 ### Attribution