|
--- |
|
inference: false |
|
pipeline_tag: text-generation |
|
license: bigcode-openrail-m |
|
datasets: |
|
- bigcode/the-stack-dedup |
|
metrics: |
|
- code_eval |
|
library_name: transformers |
|
tags: |
|
- 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 |
|
--- |
|
|
|
<!-- header start --> |
|
<!-- 200823 --> |
|
<div style="width: auto; margin-left: auto; margin-right: auto"> |
|
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
|
</div> |
|
<div style="display: flex; justify-content: space-between; width: 100%;"> |
|
<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
|
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> |
|
</div> |
|
<div style="display: flex; flex-direction: column; align-items: flex-end;"> |
|
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> |
|
</div> |
|
</div> |
|
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> |
|
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> |
|
<!-- header end --> |
|
|
|
# Bigcode's Starcoder GPTQ |
|
|
|
These files are GPTQ 4bit model files for [Bigcode's Starcoder](https://huggingface.co/bigcode/starcoder). |
|
|
|
It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ). |
|
|
|
## Repositories available |
|
|
|
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/starcoder-GPTQ) |
|
* [4, 5, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/starcoder-GGML) |
|
* [Bigcoder's unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/bigcode/starcoder) |
|
|
|
## Prompting |
|
|
|
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. |
|
|
|
## How to easily download and use this model in text-generation-webui |
|
|
|
Please make sure you're using the latest version of text-generation-webui |
|
|
|
1. Click the **Model tab**. |
|
2. Under **Download custom model or LoRA**, enter `TheBloke/starcoder-GPTQ`. |
|
3. Click **Download**. |
|
4. The model will start downloading. Once it's finished it will say "Done" |
|
5. In the top left, click the refresh icon next to **Model**. |
|
6. In the **Model** dropdown, choose the model you just downloaded: `starcoder-GPTQ` |
|
7. The model will automatically load, and is now ready for use! |
|
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. |
|
* Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. |
|
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started! |
|
|
|
## How to use this GPTQ model from Python code |
|
|
|
First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed: |
|
|
|
`pip install auto-gptq` |
|
|
|
Then try the following example code: |
|
|
|
```python |
|
from transformers import AutoTokenizer, pipeline, logging |
|
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig |
|
import argparse |
|
|
|
model_name_or_path = "TheBloke/starcoder-GPTQ" |
|
|
|
use_triton = False |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) |
|
|
|
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path, |
|
model_basename=model_basename, |
|
use_safetensors=True, |
|
trust_remote_code=True, |
|
device="cuda:0", |
|
use_triton=use_triton, |
|
quantize_config=None) |
|
|
|
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) |
|
outputs = model.generate(inputs) |
|
print(tokenizer.decode(outputs[0])) |
|
``` |
|
|
|
## Provided files |
|
|
|
**gptq_model-4bit--1g.safetensors** |
|
|
|
This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead. |
|
|
|
It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible. |
|
|
|
* `gptq_model-4bit--1g.safetensors` |
|
* Works with AutoGPTQ in CUDA or Triton modes. |
|
* Does not work with GPTQ-for-LLaMa. |
|
* Works with text-generation-webui, including one-click-installers. |
|
* Parameters: Groupsize = -1. Act Order / desc_act = True. |
|
|
|
<!-- footer start --> |
|
<!-- 200823 --> |
|
## Discord |
|
|
|
For further support, and discussions on these models and AI in general, join us at: |
|
|
|
[TheBloke AI's Discord server](https://discord.gg/theblokeai) |
|
|
|
## Thanks, and how to contribute. |
|
|
|
Thanks to the [chirper.ai](https://chirper.ai) team! |
|
|
|
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. |
|
|
|
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. |
|
|
|
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. |
|
|
|
* Patreon: https://patreon.com/TheBlokeAI |
|
* Ko-Fi: https://ko-fi.com/TheBlokeAI |
|
|
|
**Special thanks to**: Aemon Algiz. |
|
|
|
**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter |
|
|
|
|
|
Thank you to all my generous patrons and donaters! |
|
|
|
And thank you again to a16z for their generous grant. |
|
|
|
<!-- footer end --> |
|
|
|
# Original model card: Bigcode's Starcoder |
|
|
|
# StarCoder |
|
|
|
 |
|
|
|
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} |
|
} |
|
``` |
|
|
