metadata

datasets:
  - bigcode/commitpackft
  - bigcode/oasst-octopack
inference: false
library_name: transformers
license: bigcode-openrail-m
metrics:
  - code_eval
model-index:
  - name: OctoCoder
    results:
      - dataset:
          name: HumanEvalSynthesize Python
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 46.2
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalSynthesize JavaScript
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 39.2
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalSynthesize Java
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 38.2
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalSynthesize Go
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 30.4
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalSynthesize C++
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 35.6
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalSynthesize Rust
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 23.4
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalSynthesize Average
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 35.5
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalFix Python
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 30.4
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalFix JavaScript
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 28.4
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalFix Java
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 30.6
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalFix Go
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 30.2
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalFix C++
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 26.1
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalFix Rust
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 16.5
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalFix Average
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 27
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalExplain Python
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 35.1
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalExplain JavaScript
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 24.5
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalExplain Java
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 27.3
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalExplain Go
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 21.1
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalExplain C++
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 24.1
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalExplain Rust
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 14.8
            verified: false
        task:
          type: text-generation
      - dataset:
          name: HumanEvalExplain Average
          type: bigcode/humanevalpack
        metrics:
          - name: pass@1
            type: pass@1
            value: 24.5
            verified: false
        task:
          type: text-generation
model_creator: BigCode
model_link: https://huggingface.co/bigcode/octocoder
model_name: Octocoder
model_type: starcoder
pipeline_tag: text-generation
quantized_by: TheBloke
tags:
  - code
widget:
  - example_title: Bubble sort
    group: Python
    text: >-
      Question: Please write a function in Python that performs bubble
      sort.\n\nAnswer:

Want to contribute? TheBloke's Patreon page

Octocoder - GPTQ

Model creator: BigCode
Original model: Octocoder

Description

This repo contains GPTQ model files for BigCode's Octocoder.

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.

Repositories available

Prompt template: QA

Question: {prompt}
Answer:

Provided files and GPTQ parameters

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch. See below for instructions on fetching from different branches.

All GPTQ files are made with AutoGPTQ.

Explanation of GPTQ parameters

Bits: The bit size of the quantised model.
GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
Act Order: True or False. Also known as desc_act. True results in better quantisation accuracy. Some GPTQ clients have issues with models that use Act Order plus Group Size.
Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.

Branch	Bits	GS	Act Order	Damp %	GPTQ Dataset	Seq Len	Size	ExLlama	Desc
main	4	128	No	0.1	Evol Instruct Code	8192	9.20 GB	No	Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options.
gptq-4bit-32g-actorder_True	4	32	Yes	0.1	Evol Instruct Code	8192	10.09 GB	No	4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed.
gptq-4bit-64g-actorder_True	4	64	Yes	0.1	Evol Instruct Code	8192	9.49 GB	No	4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed.
gptq-4bit-128g-actorder_True	4	128	Yes	0.1	Evol Instruct Code	8192	9.20 GB	No	4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed.
gptq-8bit--1g-actorder_True	8	None	Yes	0.1	Evol Instruct Code	8192	16.49 GB	No	8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed.
gptq-8bit-128g-actorder_True	8	128	Yes	0.1	Evol Instruct Code	8192	16.84 GB	No	8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed.

How to download from branches

In text-generation-webui, you can add :branch to the end of the download name, eg TheBloke/Octocoder-GPTQ:gptq-4bit-32g-actorder_True
With Git, you can clone a branch with:

git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Octocoder-GPTQ

In Python Transformers code, the branch is the revision parameter; see below.

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.

It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.

Click the Model tab.
Under Download custom model or LoRA, enter TheBloke/Octocoder-GPTQ.

To download from a specific branch, enter for example TheBloke/Octocoder-GPTQ:gptq-4bit-32g-actorder_True
see Provided Files above for the list of branches for each option.

Click Download.
The model will start downloading. Once it's finished it will say "Done"
In the top left, click the refresh icon next to Model.
In the Model dropdown, choose the model you just downloaded: Octocoder-GPTQ
The model will automatically load, and is now ready for use!
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.

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 0.3.1 or later installed:

pip3 install auto-gptq

If you have problems installing AutoGPTQ, please build from source instead:

pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .

Then try the following example code:

from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

model_name_or_path = "TheBloke/Octocoder-GPTQ"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

"""
# To download from a specific branch, use the revision parameter, as in this example:
# Note that `revision` requires AutoGPTQ 0.3.1 or later!

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        revision="gptq-4bit-32g-actorder_True",
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        quantize_config=None)
"""

prompt = "Tell me about AI"
prompt_template=f'''Question: {prompt}
Answer:
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])

Compatibility

The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.

ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the 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: Ajan Kanaga, David Ziegler, Raymond Fosdick, SuperWojo, Sam, webtim, Steven Wood, knownsqashed, Tony Hughes, Junyu Yang, J, Olakabola, Dan Guido, Stephen Murray, John Villwock, vamX, William Sang, Sean Connelly, LangChain4j, Olusegun Samson, Fen Risland, Derek Yates, Karl Bernard, transmissions 11, Trenton Dambrowitz, Pieter, Preetika Verma, Swaroop Kallakuri, Andrey, Slarti, Jonathan Leane, Michael Levine, Kalila, Joseph William Delisle, Rishabh Srivastava, Deo Leter, Luke Pendergrass, Spencer Kim, Geoffrey Montalvo, Thomas Belote, Jeffrey Morgan, Mandus, ya boyyy, Matthew Berman, Magnesian, Ai Maven, senxiiz, Alps Aficionado, Luke @flexchar, Raven Klaugh, Imad Khwaja, Gabriel Puliatti, Johann-Peter Hartmann, usrbinkat, Spiking Neurons AB, Artur Olbinski, chris gileta, danny, Willem Michiel, WelcomeToTheClub, Deep Realms, alfie_i, Dave, Leonard Tan, NimbleBox.ai, Randy H, Daniel P. Andersen, Pyrater, Will Dee, Elle, Space Cruiser, Gabriel Tamborski, Asp the Wyvern, Illia Dulskyi, Nikolai Manek, Sid, Brandon Frisco, Nathan LeClaire, Edmond Seymore, Enrico Ros, Pedro Madruga, Eugene Pentland, John Detwiler, Mano Prime, Stanislav Ovsiannikov, Alex, Vitor Caleffi, K, biorpg, Michael Davis, Lone Striker, Pierre Kircher, theTransient, Fred von Graf, Sebastain Graf, Vadim, Iucharbius, Clay Pascal, Chadd, Mesiah Bishop, terasurfer, Rainer Wilmers, Alexandros Triantafyllidis, Stefan Sabev, Talal Aujan, Cory Kujawski, Viktor Bowallius, subjectnull, ReadyPlayerEmma, zynix

Thank you to all my generous patrons and donaters!

Original model card: BigCode's Octocoder

Model Summary
Use
Training
Citation

Model Summary

OctoCoder is an instruction tuned model with 15.5B parameters created by finetuning StarCoder on CommitPackFT & OASST as described in the OctoPack paper.

Repository: bigcode-project/octopack
Paper: OctoPack: Instruction Tuning Code Large Language Models
Languages: 80+ Programming languages

OctoPack🐙🎒:

Data	CommitPack	4TB of GitHub commits across 350 programming languages
	CommitPackFT	Filtered version of CommitPack for high-quality commit messages that resemble instructions
Model	OctoCoder	StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST
	OctoGeeX	CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST
Evaluation	HumanEvalPack	Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages

Use

Intended use

The model follows instructions provided in the input. You should always preface your input with "Question: " and finish it with "Answer:", for example: "Question: Please write a function in Python that performs bubble sort.\n\nAnswer:"

Feel free to share your generations in the Community tab!

Generation

# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "bigcode/octocoder"
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("Question: Please write a function in Python that performs bubble sort.\n\nAnswer:", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

Training

Model

Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
Steps: 250k pretraining & 30 instruction tuning
Pretraining tokens: 1 trillion pretraining & 2M instruction tuning
Precision: bfloat16

Hardware

Pretraining:
- GPUs: 512 Tesla A100
- Training time: 24 days
Instruction tuning:
- GPUs: 8 Tesla A100
- Training time: 4 hours

Software

Orchestration: Megatron-LM/Transformers
Neural networks: PyTorch

Citation

@article{muennighoff2023octopack,
      title={OctoPack: Instruction Tuning Code Large Language Models}, 
      author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
      journal={arXiv preprint arXiv:2308.07124},
      year={2023}
}

TheBloke
/

Octocoder-GPTQ