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README.md

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 license: bsd-3-clause
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+# CodeGen (CodeGen-NL 350M)
+
+## 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-NL 350M** in the paper, where "NL" means it is pre-trained on the Pile and "350M" refers to the number of trainable parameters.
+
+## Training data
+
+This checkpoint (CodeGen-NL 350M) was pre-trained on [the Pile](https://github.com/EleutherAI/the-pile), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai/). Parts of the dataset include code data.
+
+## Training procedure
+
+CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
+The family of models are trained using 4 TPU-v4 chips 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-nl')
+model = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-350M-nl')
+text = "def hello_world():"
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+# simply generate a single sequence
+generated_ids = model.generate(input_ids, max_length=128)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+# this prints "{user.username}"
+```
+
+## 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}
+}
+```

config.json

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+{
+  "_name_or_path": "checkpoints/codegen-350M-nl",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "CodeGenForCausalLM"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 1,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "codegen",
+  "n_ctx": 2048,
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 20,
+  "n_positions": 2048,
+  "resid_pdrop": 0.0,
+  "rotary_dim": 32,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50,
+      "temperature": 1.0
+    }
+  },
+  "tokenizer_class": "GPT2Tokenizer",
+  "torch_dtype": "float16",
+  "transformers_version": "4.16.2",
+  "use_cache": true,
+  "vocab_size": 51200
+}

pytorch_model.bin

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special_tokens_map.json

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+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}

tokenizer_config.json

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+{"unk_token": "<|endoftext|>", "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "gpt2", "tokenizer_class": "GPT2Tokenizer"}