tokenizer and such

README.md

@@ -1,3 +1,57 @@
 ---
-license: bsd-2-clause
+license: bsd-3-clause
 ---
+# CodeGen (CodeGen-Multi 16B)
+
+## 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-Multi 16B** in the paper, where "Multi" means the model is initialized with *CodeGen-NL 16B* and further pre-trained on a dataset of multiple programming languages, and "16B" refers to the number of trainable parameters.
+
+## Training data
+
+This checkpoint (CodeGen-Multi 16B) was firstly initialized with *CodeGen-NL 16B*, and then pre-trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github-repos), a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tokens and includes C, C++, Go, Java, JavaScript, and Python.
+
+## 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-16B-multi")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-16B-multi")
+
+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}
+}
+```

added_tokens.json

@@ -0,0 +1 @@
+{"        ": 50280, "    ": 50284, "                          ": 50262, "                      ": 50266, "\t\t\t\t\t\t\t": 50289, "                        ": 50264, "         ": 50279, "       ": 50281, "\t\t\t\t\t\t\t\t\t": 50287, "  ": 50286, "\t\t\t": 50293, "                           ": 50261, "      ": 50282, "     ": 50283, "                   ": 50269, "               ": 50273, "                 ": 50271, "\t\t\t\t\t\t\t\t": 50288, "   ": 50285, "            ": 50276, "\t\t\t\t\t\t": 50290, "\t\t\t\t\t": 50291, "                         ": 50263, "          ": 50278, "                              ": 50258, "                  ": 50270, "                             ": 50259, "                ": 50272, "              ": 50274, "                     ": 50267, "                    ": 50268, "\t\t": 50294, "                               ": 50257, "           ": 50277, "\t\t\t\t": 50292, "                            ": 50260, "                       ": 50265, "             ": 50275}

config.json

@@ -36,7 +36,7 @@
   "tie_word_embeddings": false,
   "tokenizer_class": "GPT2Tokenizer",
   "torch_dtype": "float16",
-  "transformers_version": "4.28.0.dev0",
+  "transformers_version": "4.21.0.dev0",
   "use_cache": true,
   "vocab_size": 51200
 }

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}

tokenizer_config.json

@@ -0,0 +1 @@
+{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 2048, "special_tokens_map_file": null, "name_or_path": "gpt2", "tokenizer_class": "CodeGenTokenizer"}