Update README.md

README.md

@@ -2,60 +2,29 @@
 license: bsd-3-clause
 ---
 # CodeGen (CodeGen-Mono 350M)
-This is a clone of CodeGen project which is optimized to run on CPU by using the ONNX optimisations.
-The reason we created ONNX version of the original version is, we wanted to make it possible for ICortex kernel users
-to easily generate code for their use case without using a GPU.
-Original model can be found [here](https://huggingface.co/Salesforce/codegen-350M-mono).
 
-## Model description
+Clone of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) converted to ONNX and optimized.
 
-CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`).
-
-The checkpoint included in this repository is denoted as **CodeGen-Mono 350M** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 350M* and further pre-trained on a Python programming language dataset, and "350M" refers to the number of trainable parameters.
-
-## Training data
-
-This checkpoint (CodeGen-Mono 350M) was firstly initialized with *CodeGen-Multi 350M*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
-
-## Training procedure
-
-CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
-The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism.
-See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
-
-## Evaluation results
-
-We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details.
-
-
-## Intended Use and Limitations
-
-As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them.
-However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.
-
-## How to use
-
-This model can be easily loaded using the `AutoModelForCausalLM` functionality:
+## Usage
 
 ```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
-model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForCausalLM
+
+model = ORTModelForCausalLM.from_pretrained("TextCortex/codegen-350M-optimized")
+tokenizer = AutoTokenizer.from_pretrained("TextCortex/codegen-350M-optimized")
 
 text = "def hello_world():"
 input_ids = tokenizer(text, return_tensors="pt").input_ids
-
-generated_ids = model.generate(input_ids, max_length=128)
-print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+generated_ids = model.generate(
+    input_ids,
+    max_length=64,
+    temperature=0.1,
+    num_return_sequences=1,
+    early_stopping=True,
+)
+out = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(out)
 ```
 
-## BibTeX entry and citation info
-
-```bibtex
-@article{Nijkamp2022ACP,
-  title={A Conversational Paradigm for Program Synthesis},
-  author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming},
-  journal={arXiv preprint},
-  year={2022}
-}
-```
+Refer to the original model for more details.