Upload bigcode/starcoder ctranslate fp16 weights

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	---
	pipeline_tag: text-generation
	inference: true
	widget:
	- text: 'def print_hello_world():'
	example_title: Hello world
	group: Python
	license: bigcode-openrail-m
	datasets:
	- bigcode/the-stack-dedup
	metrics:
	- code_eval
	library_name: transformers
	tags:
	- ctranslate2
	- int8
	- float16
	- code
	model-index:
	- name: StarCoder
	results:
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval (Prompted)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.408
	verified: false
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.336
	verified: false
	- task:
	type: text-generation
	dataset:
	type: mbpp
	name: MBPP
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.527
	verified: false
	- task:
	type: text-generation
	dataset:
	type: ds1000
	name: DS-1000 (Overall Completion)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.26
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (C++)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.3155
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (C#)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.2101
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (D)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.1357
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Go)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.1761
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Java)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.3022
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Julia)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.2302
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (JavaScript)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.3079
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Lua)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.2389
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (PHP)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.2608
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Perl)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.1734
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Python)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.3357
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (R)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.155
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Ruby)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.0124
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Racket)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.0007
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Rust)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.2184
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Scala)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.2761
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Bash)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.1046
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (Swift)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.2274
	verified: false
	- task:
	type: text-generation
	dataset:
	type: nuprl/MultiPL-E
	name: MultiPL-HumanEval (TypeScript)
	metrics:
	- name: pass@1
	type: pass@1
	value: 0.3229
	verified: false
	extra_gated_prompt: >-
	## Model License Agreement

	Please read the BigCode [OpenRAIL-M
	license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
	agreement before accepting it.

	extra_gated_fields:
	I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox
	---
	# # Fast-Inference with Ctranslate2
	Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.

	quantized version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder)
	```bash
	pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.16.0
	```

	```python
	# from transformers import AutoTokenizer
	model_name = "michaelfeil/ct2fast-starcoder"


	from hf_hub_ctranslate2 import GeneratorCT2fromHfHub
	model = GeneratorCT2fromHfHub(
	# 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,
	include_prompt_in_result=False
	)
	print(outputs)
	```

	Checkpoint compatible to [ctranslate2>=3.16.0](https://github.com/OpenNMT/CTranslate2)
	and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2)
	- `compute_type=int8_float16` for `device="cuda"`
	- `compute_type=int8` for `device="cpu"`

	Converted on 2023-06-27 using
	```
	ct2-transformers-converter --model bigcode/starcoder --output_dir ~/tmp-ct2fast-starcoder --force --copy_files merges.txt tokenizer.json README.md tokenizer_config.json vocab.json generation_config.json special_tokens_map.json .gitattributes --quantization int8_float16 --trust_remote_code
	```

	# Licence and other remarks:
	This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.

	# Original description



	# StarCoder

	![banner](https://huggingface.co/datasets/bigcode/admin/resolve/main/StarCoderBanner.png)

	Play with the model on the [StarCoder Playground](https://huggingface.co/spaces/bigcode/bigcode-playground).

	## Table of Contents

	1. [Model Summary](##model-summary)
	2. [Use](##use)
	3. [Limitations](##limitations)
	4. [Training](##training)
	5. [License](##license)
	6. [Citation](##citation)

	## Model Summary

	The StarCoder models are 15.5B parameter models trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens.

	- Repository: [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
	- Project Website: [bigcode-project.org](https://www.bigcode-project.org)
	- Paper: [💫StarCoder: May the source be with you!](https://arxiv.org/abs/2305.06161)
	- Point of Contact: [contact@bigcode-project.org](mailto:contact@bigcode-project.org)
	- Languages: 80+ Programming languages


	## Use

	### Intended use

	The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant.

	Feel free to share your generations in the Community tab!

	### Generation
	```python
	# pip install -q transformers
	from transformers import AutoModelForCausalLM, AutoTokenizer

	checkpoint = "bigcode/starcoder"
	device = "cuda" # for GPU usage or "cpu" for CPU usage

	tokenizer = AutoTokenizer.from_pretrained(checkpoint)
	model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

	inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
	outputs = model.generate(inputs)
	print(tokenizer.decode(outputs[0]))
	```

	### Fill-in-the-middle
	Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:

	```python
	input_text = "<fim_prefix>def print_hello_world():\n <fim_suffix>\n print('Hello world!')<fim_middle>"
	inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
	outputs = model.generate(inputs)
	print(tokenizer.decode(outputs[0]))
	```

	### Attribution & Other Requirements

	The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.

	# Limitations

	The model has been trained on source code from 80+ programming languages. The predominant natural language in source code is English although other languages are also present. As such the model is capable of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) for an in-depth discussion of the model limitations.

	# Training

	## Model

	- Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
	- Pretraining steps: 250k
	- Pretraining tokens: 1 trillion
	- Precision: bfloat16

	## Hardware

	- GPUs: 512 Tesla A100
	- Training time: 24 days

	## Software

	- Orchestration: [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
	- Neural networks: [PyTorch](https://github.com/pytorch/pytorch)
	- BP16 if applicable: [apex](https://github.com/NVIDIA/apex)

	# License
	The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
	# Citation
	```
	@article{li2023starcoder,
	title={StarCoder: may the source be with you!},
	author={Raymond Li and Loubna Ben Allal and Yangtian Zi and Niklas Muennighoff and Denis Kocetkov and Chenghao Mou and Marc Marone and Christopher Akiki and Jia Li and Jenny Chim and Qian Liu and Evgenii Zheltonozhskii and Terry Yue Zhuo and Thomas Wang and Olivier Dehaene and Mishig Davaadorj and Joel Lamy-Poirier and João Monteiro and Oleh Shliazhko and Nicolas Gontier and Nicholas Meade and Armel Zebaze and Ming-Ho Yee and Logesh Kumar Umapathi and Jian Zhu and Benjamin Lipkin and Muhtasham Oblokulov and Zhiruo Wang and Rudra Murthy and Jason Stillerman and Siva Sankalp Patel and Dmitry Abulkhanov and Marco Zocca and Manan Dey and Zhihan Zhang and Nour Fahmy and Urvashi Bhattacharyya and Wenhao Yu and Swayam Singh and Sasha Luccioni and Paulo Villegas and Maxim Kunakov and Fedor Zhdanov and Manuel Romero and Tony Lee and Nadav Timor and Jennifer Ding and Claire Schlesinger and Hailey Schoelkopf and Jan Ebert and Tri Dao and Mayank Mishra and Alex Gu and Jennifer Robinson and Carolyn Jane Anderson and Brendan Dolan-Gavitt and Danish Contractor and Siva Reddy and Daniel Fried and Dzmitry Bahdanau and Yacine Jernite and Carlos Muñoz Ferrandis and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
	year={2023},
	eprint={2305.06161},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```