Upload Salesforce/codet5p-770m ctranslate2 weights

.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,131 @@
+---
+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/codet5p-770m](https://huggingface.co/Salesforce/codet5p-770m)
+```bash
+pip install ctranslate2
+```
+
+```python
+# from transformers import AutoTokenizer
+model_name = "OpenNMT/codet5p-770m-ct2-int8"
+
+
+from hf_hub_ctranslate2 import TranslatorCT2fromHfHub
+model = TranslatorCT2fromHfHub(
+        # load in int8 on CUDA
+        model_name_or_path=model_name,
+        device="cuda",
+        compute_type="int8_float16",
+        # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}")
+)
+outputs = model.generate(
+    text=["def fibonnaci(", "User: How are you doing? Bot:"],
+    max_length=64,
+)
+print(outputs)
+```
+
+Checkpoint compatible to [ctranslate2>=3.22.0](https://github.com/OpenNMT/CTranslate2)
+- `compute_type=int8_float16` for `device="cuda"`
+- `compute_type=int8`  for `device="cpu"`
+
+Converted on 2023-11-30 using CTranslate2==3.22.0 and
+```
+from ctranslate2.converters import TransformersConverter
+TransformersConverter(
+    "Salesforce/codet5p-770m",
+    activation_scales=None,
+    copy_files=['vocab.json', 'README.md', 'special_tokens_map.json', 'merges.txt', 'tokenizer_config.json', 'added_tokens.json', '.gitattributes'],
+    load_as_float16=True,
+    revision=None,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+).convert(
+    output_dir=str(tmp_dir),
+    vmap = None, 
+    quantization="int8_float16",
+    force = True,
+)
+
+```
+
+# Licence and other remarks:
+This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
+
+# Original description
+    
+
+# CodeT5+ 770M
+
+## Model description
+
+[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:
+
+[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).
+
+Compared to the original CodeT5 family (CodeT5-base: `220M`, CodeT5-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 (see our InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca).  
+
+## How to use
+
+This model can be easily loaded using the `T5ForConditionalGeneration` functionality and employs the same tokenizer as original [CodeT5](https://github.com/salesforce/CodeT5).
+
+```python
+from transformers import T5ForConditionalGeneration, AutoTokenizer
+
+checkpoint = "Salesforce/codet5p-770m"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
+
+inputs = tokenizer.encode("def print_hello_world():<extra_id_0>", return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_length=10)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# ==> print "Hello World"
+```
+
+## Pretraining data
+
+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.`
+
+## Training procedure
+
+This checkpoint is trained on the 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_.
+Please refer to the paper for more details.
+
+## Evaluation results
+
+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.
+
+
+## BibTeX entry and citation info
+
+```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

@@ -0,0 +1 @@
+{}

config.json

@@ -0,0 +1,38 @@
+{
+    "_name_or_path": "Salesforce/codet5p-770m",
+    "architectures": [
+        "T5ForConditionalGeneration"
+    ],
+    "bos_token_id": 1,
+    "d_ff": 4096,
+    "d_kv": 64,
+    "d_model": 1024,
+    "decoder_start_token_id": 0,
+    "dense_act_fn": "relu",
+    "dropout_rate": 0.1,
+    "eos_token_id": 2,
+    "feed_forward_proj": "relu",
+    "initializer_factor": 1.0,
+    "is_encoder_decoder": true,
+    "is_gated_act": false,
+    "layer_norm_epsilon": null,
+    "model_type": "t5",
+    "n_positions": 512,
+    "num_decoder_layers": 24,
+    "num_heads": 16,
+    "num_layers": 24,
+    "output_past": true,
+    "pad_token_id": 0,
+    "relative_attention_max_distance": 128,
+    "relative_attention_num_buckets": 32,
+    "torch_dtype": "float16",
+    "transformers_version": "4.21.3",
+    "use_cache": true,
+    "vocab_size": 32100,
+    "add_source_bos": false,
+    "add_source_eos": false,
+    "bos_token": "<pad>",
+    "decoder_start_token": "<pad>",
+    "eos_token": "</s>",
+    "unk_token": "<unk>"
+}

model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4146e5369fa7e1dc7d8d482f5223b5581425226d4c660fa1fbaac3f1db981a6c
+size 740108239

special_tokens_map.json

@@ -0,0 +1,147 @@
+{
+    "bos_token": {
+        "content": "<s>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true
+    },
+    "eos_token": {
+        "content": "</s>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true
+    },
+    "unk_token": {
+        "content": "<unk>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true
+    },
+    "sep_token": {
+        "content": "</s>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true
+    },
+    "pad_token": {
+        "content": "<pad>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true
+    },
+    "cls_token": {
+        "content": "<s>",
+        "single_word": false,
+        "lstrip": false,
+        "rstrip": false,
+        "normalized": true
+    },
+    "mask_token": { "content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+    "additional_special_tokens": [
+        { "content":"<extra_id_99>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_98>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_97>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_96>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_95>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_94>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_93>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_92>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_91>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_90>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_89>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_88>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_87>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_86>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_85>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_84>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_83>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_82>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_81>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_80>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_79>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_78>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_77>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_76>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_75>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_74>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_73>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_72>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_71>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_70>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_69>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_68>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_67>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_66>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_65>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_64>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_63>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_62>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_61>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_60>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_59>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_58>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_57>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_56>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_55>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_54>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_53>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_52>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_51>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_50>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_49>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_48>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_47>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_46>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_45>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_44>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_43>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_42>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_41>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_40>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_39>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_38>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_37>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_36>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_35>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_34>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_33>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_32>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_31>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_30>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_29>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_28>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_27>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_26>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_25>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_24>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_23>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_22>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_21>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_20>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_19>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_18>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_17>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_16>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_15>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_14>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_13>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_12>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_11>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_10>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_9>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_8>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_7>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_6>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_5>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_4>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "content":"<extra_id_3>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true },
+        { "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

@@ -0,0 +1,62 @@
+{
+    "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"
+}