update readme

README.md

@@ -369,7 +369,7 @@ Citation
 <!-- ![granite](https://github.com/ibm-granite/granite-code-models/blob/main/figures/granite.png) -->
 
 ## Model Summary
-**Granite 3B Code Base** model is a decoder-only code model designed for code generative tasks (e.g., code generation, code explanation, code fixing). It was trained from scratch on 4 trillion tokens sourced from 116 programming languages, ensuring a comprehensive understanding of programming languages and syntax. 
+**Granite 3B Code Base** is a decoder-only code model designed for code generative tasks (e.g., code generation, code explanation, code fixing). It was trained from scratch on 4 trillion tokens sourced from 116 programming languages, ensuring a comprehensive understanding of programming languages and syntax. 
 
 - **Developers:** IBM Research
 - **GitHub Repository:** [ibm-granite/granite-code-models](https://github.com/ibm-granite/granite-code-models)
@@ -383,44 +383,41 @@ for Code Intelligence](https://)
 Prominent enterprise use cases of LLMs in software engineering productivity include code generation, code explanation, code fixing, generating unit tests, generating documentation, addressing technical debt issues, vulnerability detection, code translation, and more. All Granite Code Base models, including the **3B parameters model**, are able to handle these tasks as they were trained on a large amount of code data from 116 programming languages. 
 
 ### Generation
-Before proceeding, you need to install the necessary dependencies. You can do this by running the following command:
-```
-pip install -r requirements.txt
-```
-
 This is a simple example of how to use Granite Code Base 3B model.
 
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
+device = "cuda" # or "cpu"
 model_path = "ibm-granite/granite-3b-code-base"
 
 tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(model_path, device_map="cuda")
+
+# drop device_map if running on CPU
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
 model.eval()
 
+# change input text as desired
 input_text = "def generate():"
 
+# tokenize the text
 input_tokens = tokenizer(input_text, return_tensors="pt")
+
+# transfer tokenized inputs to the device
 for i in input_tokens:
-    input_tokens[i] = input_tokens[i].cuda()
+    input_tokens[i] = input_tokens[i].to(device)
 
+# generate output tokens
 output = model.generate(**input_tokens)
+
+# decode output tokens into text
 output = tokenizer.batch_decode(output)
+
+# loop over the batch to print, in this example the batch size is 1
 for i in output:
     print(output)
 ```
-### Fill-in-the-middle
-
-Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
-
-```python
-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 Data
 - **Data Collection and Filtering:** Pretraining code data is sourced from a combination of publicly available datasets (e.g., [GitHub Code Clean](https://huggingface.co/datasets/codeparrot/github-code-clean), [Starcoder data](https://huggingface.co/datasets/bigcode/starcoderdata)), and additional public code repositories and issues from GitHub. We filter raw data to retain a list of 116 programming languages. After language filtering, we also filter out low-quality code. 
@@ -429,10 +426,10 @@ print(tokenizer.decode(outputs[0]))
 - **Natural Language Datasets:** In addition to collecting code data for model training, we curate several publicly available high-quality natural language datasets to improve models' proficiency in language understanding and mathematical reasoning. Unlike the code data, we do not deduplicate these datasets.
 
 ## Infrastructure
-We train the Granite Code models using two of IBM’s super computing clusters, namely Vela and Blue Vela, both outfitted with NVIDIA A100 and H100 GPUs respectively. These clusters provide a scalable and efficient infrastructure for training our models over thousands of GPUs.
+We train the Granite Code models using two of IBM's super computing clusters, namely Vela and Blue Vela, both outfitted with NVIDIA A100 and H100 GPUs respectively. These clusters provide a scalable and efficient infrastructure for training our models over thousands of GPUs.
 
 ## Limitations
-Large Language Models are often prone to generating incorrect information, typically referred to as hallucinations. **Granite 3B Code Base** model is not the exception in this regard. Even though this model is suited for code-related tasks as it is trained on source code from 116 programming languages, the generated code is not guaranteed to work as intended. It can be inefficient and contain bugs or exploits. Moreover, Granite Code Base models are _not_ instruction-following models. Thus, commands like *"Write a function that computes the square root"* may not work well.
+Large Language Models are often prone to generating incorrect information, typically referred to as hallucinations. **Granite 3B Code Base** model is not the exception in this regard. Even though this model is suited for code-related tasks as it is trained on source code from 116 programming languages, the generated code is not guaranteed to work as intended. It can be inefficient and can also contain bugs or exploits. Moreover, Granite Code Base models are **NOT** instruction-following models. Thus, commands like *"Write a function that computes the square root"* may not work well. The model is best treated as a code completion or code infilling model.
 
 ## Citation
 ```
@@ -444,4 +441,4 @@ Large Language Models are often prone to generating incorrect information, typic
   year = {2024},
   url = {https://arxiv.org/abs/0000.00000},
 }
-```
+```