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README.md

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+---
+license: bsd-3-clause
+---
+# CodeGen (CodeGen-NL 16B)
+
+## Sharded version of codegen
+
+This model was sharded using torch.float16. Use the code below to load this model, configure the device_map for your GPU/CPU split.
+
+```python
+def load_model_sharded():
+    config = AutoConfig.from_pretrained("abacaj/codegen-16B-nl-sharded")
+    tokenizer = AutoTokenizer.from_pretrained("abacaj/codegen-16B-nl-sharded")
+
+    with init_empty_weights():
+        model = AutoModelForCausalLM.from_config(config)
+
+    device_map = infer_auto_device_map(
+        model,
+        max_memory={
+            0: "20GiB",
+            "cpu": "110GiB",
+        },
+        dtype=torch.float16,
+        no_split_module_classes=["CodeGenBlock"])
+
+    model = load_checkpoint_and_dispatch(
+        model,
+        dtype=torch.float16,
+        checkpoint="sharded",
+        device_map=device_map,
+    ).eval()
+
+    return model, tokenizer
+```
+
+## 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 16B** in the paper, where "NL" means it is pre-trained on the Pile and "16B" refers to the number of trainable parameters.
+
+## Training data
+
+This checkpoint (CodeGen-NL 16B) 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 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-nl")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-16B-nl")
+
+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}
+}
+```