Update README.md

README.md

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+# CodeGen (CodeGen-Mono 350M)
+
+This is an int4 quantization for use with [cformers](https://github.com/NolanoOrg/cformers).
+
+## Model description
+
+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:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono")
+
+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))
+```
+
+## 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}
+}
+```