Correct model

README.md

@@ -1,59 +1,70 @@
 ---
-license: apache-2.0
-language:
-  - en
-tags:
-  - ctranslate2
-  - flan-alpaca-gpt4-xl
-  - quantization
-  - int8
+license: bsd-3-clause
 ---
 
-# Model Card for Flan-Alpaca-GPT4-XL Q8
+# CodeT5+ 220M (further tuned on Python)
 
-The model is quantized version of the [declare-lab/flan-alpaca-gpt4-xl](https://huggingface.co/declare-lab/flan-alpaca-gpt4-xl) with int8 quantization.
+## Model description
 
-## Model Details
+[CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks. 
+It is introduced in the paper:
 
-### Model Description
+[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf)
+by [Yue Wang](https://yuewang-cuhk.github.io/)\*, [Hung Le](https://sites.google.com/view/henryle2018/home?pli=1)\*, [Akhilesh Deepak Gotmare](https://akhileshgotmare.github.io/), [Nghi D.Q. Bui](https://bdqnghi.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) (* indicates equal contribution).
 
-The model being quantized using [CTranslate2](https://opennmt.net/CTranslate2/) with the following command:
+Compared to the original CodeT5 family (base: `220M`, large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data. 
+Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as [CodeGen](https://github.com/salesforce/CodeGen) to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture. 
+Furthermore, it is instruction-tuned to align with natural language instructions (i.e. InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca).  
 
-```
-ct2-transformers-converter --model declare-lab/flan-alpaca-gpt4-xl --output_dir declare-lab/flan-alpaca-gpt4-xl-ct2 --copy_files generation_config.json tokenizer.json tokenizer_config.json special_tokens_map.json spiece.model --quantization int8 --force --low_cpu_mem_usage
-```
+## How to use
 
-If you want to perform the quantization yourself, you need to install the following dependencies:
+This model can be easily loaded using the `T5ForConditionalGeneration` functionality and employs the same tokenizer as original [CodeT5](https://github.com/salesforce/CodeT5).
 
-```
-pip install -qU ctranslate2 transformers[torch] sentencepiece accelerate
-```
+```python
+from transformers import T5ForConditionalGeneration, AutoTokenizer
 
-- **Shared by:** Lim Chee Kin
-- **License:** Apache 2.0
+checkpoint = "Salesforce/codet5p-220m-py"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
 
-## How to Get Started with the Model
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
 
-Use the code below to get started with the model.
+inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_length=10)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# ==>     print('Hello World!')
+```
 
-```python
-import ctranslate2
-import transformers
+## Pretraining data
 
-translator = ctranslate2.Translator("limcheekin/flan-alpaca-gpt4-xl-ct2")
-tokenizer = transformers.AutoTokenizer.from_pretrained("limcheekin/flan-alpaca-gpt4-xl-ct2")
+This checkpoint is trained on the stricter permissive subset of the deduplicated version of the [github-code dataset](https://huggingface.co/datasets/codeparrot/github-code).
+The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”).
+Supported languages (9 in total) are as follows:
+`c`, `c++`, `c-sharp`,  `go`, `java`, `javascript`,  `php`, `python`, `ruby.`
 
-input_text = "translate English to German: The house is wonderful."
-input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
+## Training procedure
 
-results = translator.translate_batch([input_tokens])
+This checkpoint is first trained on the multilingual unimodal code data at the first-stage pretraining, which includes a diverse set of pretraining tasks including _span denoising_ and two variants of _causal language modeling_.
+After that, it is further trained on the Python subset with the causal language modeling objective for another epoch to better adapt for Python code generation. Please refer to the paper for more details.
 
-output_tokens = results[0].hypotheses[0]
-output_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens))
+## Evaluation results
 
-print(output_text)
-```
+CodeT5+ models have been comprehensively evaluated on a wide range of code understanding and generation tasks in various settings: _zero-shot_, _finetuning_, and _instruction-tuning_.
+Specifically, CodeT5+ yields substantial performance gains on many downstream tasks compared to their SoTA baselines, e.g.,
+8 text-to-code retrieval tasks (+3.2 avg. MRR), 2 line-level code completion tasks (+2.1 avg. Exact Match), and 2 retrieval-augmented code generation tasks (+5.8 avg. BLEU-4). 
+In 2 math programming tasks on MathQA-Python and GSM8K-Python, CodeT5+ models of below billion-parameter sizes significantly outperform many LLMs of up to 137B parameters. 
+Particularly, in the zero-shot text-to-code generation task on HumanEval benchmark, InstructCodeT5+ 16B sets new SoTA results of 35.0% pass@1 and 54.5% pass@10 against other open code LLMs, even surpassing the closed-source OpenAI code-cushman-001 mode
+Please refer to the [paper](https://arxiv.org/pdf/2305.07922.pdf) for more details.
+
+Specifically for this checkpoint, it achieves 12.0% pass@1 on HumanEval in the zero-shot setting, which outperforms much larger LLMs such as Incoder 1.3B’s 8.9%, GPT-Neo 2.7B's 6.4%, and GPT-J 6B's 11.6%.
 
-The code is taken from https://opennmt.net/CTranslate2/guides/transformers.html#t5.
+## BibTeX entry and citation info
 
-The key method of the code above is `translate_batch`, you can find out [its supported parameters here](https://opennmt.net/CTranslate2/python/ctranslate2.Translator.html#ctranslate2.Translator.translate_batch).
+```bibtex
+@article{wang2023codet5plus,
+  title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
+  author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
+  journal={arXiv preprint},
+  year={2023}
+}
+```

added_tokens.json

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+{}

config.json

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   "bos_token": "<pad>",
   "decoder_start_token": "<pad>",
   "eos_token": "</s>",
+  "layer_norm_epsilon": null,
   "unk_token": "<unk>"
 }

model.bin

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special_tokens_map.json

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+        { "content":"<extra_id_2>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_1>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_0>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true }
+    ]
 }

tokenizer_config.json

@@ -1,112 +1,62 @@
 {
-  "additional_special_tokens": [
-    "<extra_id_0>",
-    "<extra_id_1>",
-    "<extra_id_2>",
-    "<extra_id_3>",
-    "<extra_id_4>",
-    "<extra_id_5>",
-    "<extra_id_6>",
-    "<extra_id_7>",
-    "<extra_id_8>",
-    "<extra_id_9>",
-    "<extra_id_10>",
-    "<extra_id_11>",
-    "<extra_id_12>",
-    "<extra_id_13>",
-    "<extra_id_14>",
-    "<extra_id_15>",
-    "<extra_id_16>",
-    "<extra_id_17>",
-    "<extra_id_18>",
-    "<extra_id_19>",
-    "<extra_id_20>",
-    "<extra_id_21>",
-    "<extra_id_22>",
-    "<extra_id_23>",
-    "<extra_id_24>",
-    "<extra_id_25>",
-    "<extra_id_26>",
-    "<extra_id_27>",
-    "<extra_id_28>",
-    "<extra_id_29>",
-    "<extra_id_30>",
-    "<extra_id_31>",
-    "<extra_id_32>",
-    "<extra_id_33>",
-    "<extra_id_34>",
-    "<extra_id_35>",
-    "<extra_id_36>",
-    "<extra_id_37>",
-    "<extra_id_38>",
-    "<extra_id_39>",
-    "<extra_id_40>",
-    "<extra_id_41>",
-    "<extra_id_42>",
-    "<extra_id_43>",
-    "<extra_id_44>",
-    "<extra_id_45>",
-    "<extra_id_46>",
-    "<extra_id_47>",
-    "<extra_id_48>",
-    "<extra_id_49>",
-    "<extra_id_50>",
-    "<extra_id_51>",
-    "<extra_id_52>",
-    "<extra_id_53>",
-    "<extra_id_54>",
-    "<extra_id_55>",
-    "<extra_id_56>",
-    "<extra_id_57>",
-    "<extra_id_58>",
-    "<extra_id_59>",
-    "<extra_id_60>",
-    "<extra_id_61>",
-    "<extra_id_62>",
-    "<extra_id_63>",
-    "<extra_id_64>",
-    "<extra_id_65>",
-    "<extra_id_66>",
-    "<extra_id_67>",
-    "<extra_id_68>",
-    "<extra_id_69>",
-    "<extra_id_70>",
-    "<extra_id_71>",
-    "<extra_id_72>",
-    "<extra_id_73>",
-    "<extra_id_74>",
-    "<extra_id_75>",
-    "<extra_id_76>",
-    "<extra_id_77>",
-    "<extra_id_78>",
-    "<extra_id_79>",
-    "<extra_id_80>",
-    "<extra_id_81>",
-    "<extra_id_82>",
-    "<extra_id_83>",
-    "<extra_id_84>",
-    "<extra_id_85>",
-    "<extra_id_86>",
-    "<extra_id_87>",
-    "<extra_id_88>",
-    "<extra_id_89>",
-    "<extra_id_90>",
-    "<extra_id_91>",
-    "<extra_id_92>",
-    "<extra_id_93>",
-    "<extra_id_94>",
-    "<extra_id_95>",
-    "<extra_id_96>",
-    "<extra_id_97>",
-    "<extra_id_98>",
-    "<extra_id_99>"
-  ],
-  "clean_up_tokenization_spaces": true,
-  "eos_token": "</s>",
-  "extra_ids": 100,
-  "model_max_length": 512,
-  "pad_token": "<pad>",
-  "sp_model_kwargs": {},
-  "tokenizer_class": "T5Tokenizer",
-  "unk_token": "<unk>"
+    "errors": "replace",
+    "unk_token": {
+        "content": "<unk>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "bos_token": {
+        "content": "<s>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "eos_token": {
+        "content": "</s>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "add_prefix_space": false,
+    "sep_token": {
+        "content": "</s>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "cls_token": {
+        "content": "<s>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "pad_token": {
+        "content": "<pad>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "mask_token": {
+        "content": "<mask>",
+        "single_word": false,
+        "lstrip": true,
+        "rstrip": false,
+        "normalized": true,
+        "__type": "AddedToken"
+    },
+    "model_max_length": 512,
+    "tokenizer_class": "RobertaTokenizer"
 }