Upload Salesforce/codegen-6B-mono ctranslate fp16 weights

.gitattributes

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README.md

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+---
+tags:
+- ctranslate2
+- int8
+- float16
+
+license: bsd-3-clause
+---
+# # Fast-Inference with Ctranslate2
+Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
+
+quantized version of [Salesforce/codegen-6B-mono](https://huggingface.co/Salesforce/codegen-6B-mono)
+```bash
+pip install hf-hub-ctranslate2>=2.0.8 
+```
+Converted on 2023-05-21 using
+```
+ct2-transformers-converter --model Salesforce/codegen-6B-mono --output_dir /home/michael/tmp-ct2fast-codegen-6B-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
+```
+
+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)
+- `compute_type=int8_float16` for `device="cuda"` 
+- `compute_type=int8`  for `device="cpu"`
+
+```python
+from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
+from transformers import AutoTokenizer
+
+model_name = "michaelfeil/ct2fast-codegen-6B-mono"
+# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
+model = GeneratorCT2fromHfHub(
+        # load in int8 on CUDA
+        model_name_or_path=model_name, 
+        device="cuda",
+        compute_type="int8_float16",
+        # tokenizer=AutoTokenizer.from_pretrained("Salesforce/codegen-6B-mono")
+)
+outputs = model.generate(
+    text=["def print_hello_world():", "def hello_name(name:"],
+    max_length=64
+)
+print(outputs)
+```
+
+# Licence and other remarks:
+This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
+
+# Original description
+    
+tags:
+- ctranslate2
+- int8
+- float16
+
+# CodeGen (CodeGen-Mono 6B)
+
+## 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 6B** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 6B* and further pre-trained on a Python programming language dataset, and "6B" refers to the number of trainable parameters.
+
+## Training data
+
+This checkpoint (CodeGen-Mono 6B) was firstly initialized with *CodeGen-Multi 6B*, 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-6B-mono")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-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}
+}
+```

added_tokens.json

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+{"        ": 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

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

model.bin

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+size 14128445443

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": 2048, "special_tokens_map_file": null, "name_or_path": "gpt2", "tokenizer_class": "CodeGenTokenizer"}