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Upload Salesforce/codegen-2B-mono ctranslate fp16 weights

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README.md ADDED
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+ ---
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+ tags:
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+ - ctranslate2
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+ - int8
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+ - float16
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+
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+ license: bsd-3-clause
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+ ---
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+ # # Fast-Inference with Ctranslate2
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+ Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
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+
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+ quantized version of [Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono)
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+ ```bash
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+ pip install hf-hub-ctranslate2>=2.0.8
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+ ```
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+ Converted on 2023-05-21 using
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+ ```
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+ ct2-transformers-converter --model Salesforce/codegen-2B-mono --output_dir /home/michael/tmp-ct2fast-codegen-2B-mono --force --copy_files merges.txt tokenizer.json README.md tokenizer_config.json vocab.json special_tokens_map.json added_tokens.json .gitattributes --quantization float16
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+ ```
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+
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+ Checkpoint compatible to [ctranslate2>=3.13.0](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.0.6](https://github.com/michaelfeil/hf-hub-ctranslate2)
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+ - `compute_type=int8_float16` for `device="cuda"`
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+ - `compute_type=int8` for `device="cpu"`
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+
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+ ```python
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+ from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
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+ from transformers import AutoTokenizer
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+
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+ model_name = "michaelfeil/ct2fast-codegen-2B-mono"
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+ # use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
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+ model = GeneratorCT2fromHfHub(
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+ # load in int8 on CUDA
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+ model_name_or_path=model_name,
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+ device="cuda",
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+ compute_type="int8_float16",
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+ # tokenizer=AutoTokenizer.from_pretrained("Salesforce/codegen-2B-mono")
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+ )
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+ outputs = model.generate(
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+ text=["def print_hello_world():", "def hello_name(name:"],
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+ max_length=64
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+ )
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+ print(outputs)
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+ ```
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+
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+ # Licence and other remarks:
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+ This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
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+
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+ # Original description
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+
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+ tags:
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+ - ctranslate2
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+ - int8
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+ - float16
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+
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+ # CodeGen (CodeGen-Mono 2B)
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+
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+ ## Model description
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+
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+ 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`).
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+
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+ The checkpoint included in this repository is denoted as **CodeGen-Mono 2B** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 2B* and further pre-trained on a Python programming language dataset, and "2B" refers to the number of trainable parameters.
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+
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+ ## Training data
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+
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+ This checkpoint (CodeGen-Mono 2B) was firstly initialized with *CodeGen-Multi 2B*, 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.
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+
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+ ## Training procedure
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+
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+ CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs.
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+ The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism.
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+ See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details.
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+
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+ ## Evaluation results
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+
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+ 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.
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+
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+
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+ ## Intended Use and Limitations
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+
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+ 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.
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+ 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.
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+
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+ ## How to use
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+
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+ This model can be easily loaded using the `AutoModelForCausalLM` functionality:
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-2B-mono")
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+ model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-2B-mono")
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+
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+ text = "def hello_world():"
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+ input_ids = tokenizer(text, return_tensors="pt").input_ids
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+
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+ generated_ids = model.generate(input_ids, max_length=128)
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+ print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
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+ ```
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+
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+ ## BibTeX entry and citation info
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+
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+ ```bibtex
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+ @article{Nijkamp2022ACP,
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+ title={A Conversational Paradigm for Program Synthesis},
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+ author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming},
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+ journal={arXiv preprint},
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+ year={2022}
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+ }
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+ ```
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