TheBloke/starcoderplus-GGML

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Bigcode's StarcoderPlus GGML

These files are GGML format model files for Bigcode's StarcoderPlus.

Please note that these GGMLs are not compatible with llama.cpp, or currently with text-generation-webui. Please see below for a list of tools known to work with these model files.

Repositories available

• 4-bit GPTQ models for GPU inference
• 4, 5, and 8-bit GGML models for CPU+GPU inference
• Unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Compatibilty

These files are not compatible with llama.cpp.

Currently they can be used with:

• KoboldCpp, a powerful inference engine based on llama.cpp, with good UI: KoboldCpp
• The ctransformers Python library, which includes LangChain support: ctransformers
• The GPT4All-UI which uses ctransformers: GPT4All-UI
• rustformers' llm
• The example starcoder binary provided with ggml

As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!)

Tutorial for using GPT4All-UI

• Text tutorial, written by Lucas3DCG
• Video tutorial, by GPT4All-UI's author ParisNeo

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
starcoderplus.ggmlv3.q4_0.bin	q4_0	4	10.75 GB	13.25 GB	Original llama.cpp quant method, 4-bit.
starcoderplus.ggmlv3.q4_1.bin	q4_1	4	11.92 GB	14.42 GB	Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
starcoderplus.ggmlv3.q5_0.bin	q5_0	5	13.09 GB	15.59 GB	Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
starcoderplus.ggmlv3.q5_1.bin	q5_1	5	14.26 GB	16.76 GB	Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
starcoderplus.ggmlv3.q8_0.bin	q8_0	8	20.11 GB	22.61 GB	Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

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.

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Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

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Thank you to all my generous patrons and donaters!

Original model card: Bigcode's StarcoderPlus

StarCoderPlus

Play with the instruction-tuned StarCoderPlus at StarChat-Beta.

Table of Contents

1. Model Summary
2. Use
3. Limitations
4. Training
5. License
6. Citation

Model Summary

StarCoderPlus is a fine-tuned version of StarCoderBase on 600B tokens from the English web dataset RedefinedWeb combined with StarCoderData from The Stack (v1.2) and a Wikipedia dataset. It's a 15.5B parameter Language Model trained on English and 80+ programming languages. The model uses Multi Query Attention, a context window of 8192 tokens, and was trained using the Fill-in-the-Middle objective on 1.6 trillion tokens.

• Repository: bigcode/Megatron-LM
• Project Website: bigcode-project.org
• Point of Contact: contact@bigcode-project.org
• Languages: English & 80+ Programming languages

Use

Intended use

The model was trained on English and 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, the instruction-tuned version in StarChat makes a capable assistant.

Feel free to share your generations in the Community tab!

Generation

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

checkpoint = "bigcode/starcoderplus"
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:

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 training code 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 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 a mixture of English text from the web and GitHub code. Therefore it might encounter limitations when working with non-English text, and can carry the stereotypes and biases commonly encountered online. Additionally, the generated code should be used with caution as it may contain errors, inefficiencies, or potential vulnerabilities. For a more comprehensive understanding of the base model's code limitations, please refer to See StarCoder paper.

Training

StarCoderPlus is a fine-tuned version on 600B English and code tokens of StarCoderBase, which was pre-trained on 1T code tokens. Below are the fine-tuning details:

Model

• Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
• Finetuning steps: 150k
• Finetuning tokens: 600B
• Precision: bfloat16

Hardware

• GPUs: 512 Tesla A100
• Training time: 14 days

Software

• Orchestration: Megatron-LM
• Neural networks: PyTorch
• BP16 if applicable: apex

License

The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.