TheBloke/stablecode-instruct-alpha-3b-GGML

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Stablecode Instruct Alpha 3B - GGML

• Model creator: StabilityAI
• Original model: Stablecode Instruct Alpha 3B

Description

This repo contains GPT-NeoX GGML format model files for StabilityAI's Stablecode Instruct Alpha 3B.

Please note that these GGMLs are not compatible with llama.cpp, text-generation-webui or llama-cpp-python. Please see below for a list of tools that work with this GGML model.

Repositories available

• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
• StabilityAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: StableCode

###Instruction:
{prompt}

###Response:

Compatibilty

These files are not compatible with llama.cpp, text-generation-webui or llama-cpp-python.

They can be used with:

• KoboldCpp, a powerful inference engine based on llama.cpp with full GPU acceleration and good UI.
• LM Studio, a fully featured local GUI for GGML inference on Windows and macOS.
• LoLLMs-WebUI a web UI which supports nearly every backend out there. Use ctransformers backend for support for this model.
• ctransformers: for use in Python code, including LangChain support.
• rustformers' llm
• The example gpt-neox 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 LoLLMs-WebUI:

• Video tutorial, by LoLLMs-WebUI's author ParisNeo

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
stablecode-instruct-alpha-3b.ggmlv1.q4_0.bin	q4_0	4	1.56 GB	4.06 GB	4-bit.
stablecode-instruct-alpha-3b.ggmlv1.q4_1.bin	q4_1	4	1.73 GB	4.23 GB	4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
stablecode-instruct-alpha-3b.ggmlv1.q5_0.bin	q5_0	5	1.91 GB	4.41 GB	5-bit. Higher accuracy, higher resource usage and slower inference.
stablecode-instruct-alpha-3b.ggmlv1.q5_1.bin	q5_1	5	2.08 GB	4.58 GB	5-bit. Even higher accuracy, resource usage and slower inference.
stablecode-instruct-alpha-3b.ggmlv1.q8_0.bin	q8_0	8	2.95 GB	5.45 GB	8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

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: Luke from CarbonQuill, Aemon Algiz.

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

Thank you to all my generous patrons and donaters!

Original model card: StabilityAI's Stablecode Instruct Alpha 3B

StableCode-Instruct-Alpha-3B

Model Description

StableCode-Instruct-Alpha-3B is a 3 billion parameter decoder-only instruction tuned code model pre-trained on diverse set of programming languages that topped the stackoverflow developer survey.

Usage

The model is intended to follow instruction to generate code. The dataset used to train the model is formatted in Alpaca format. Get started generating code with StableCode-Instruct-Alpha-3B by using the following code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablecode-instruct-alpha-3b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablecode-instruct-alpha-3b",
  trust_remote_code=True,
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("###Instruction\nGenerate a python function to find number of CPU cores###Response\n", return_tensors="pt").to("cuda")
tokens = model.generate(
  **inputs,
  max_new_tokens=48,
  temperature=0.2,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Model Details

• Developed by: Stability AI
• Model type: StableCode-Instruct-Alpha-3B models are auto-regressive language models based on the transformer decoder architecture.
• Language(s): Code
• Library: GPT-NeoX
• License : Model checkpoints are licensed under the StableCode Research License Copyright (c) Stability AI Ltd. All Rights Reserved
• Contact: For questions and comments about the model, please email lm@stability.ai

Model Architecture

Parameters	Hidden Size	Layers	Heads	Sequence Length
2,796,431,360	2560	32	32	4096

• Decoder Layer: Parallel Attention and MLP residuals with a single input LayerNorm (Wang & Komatsuzaki, 2021)
• Position Embeddings: Rotary Position Embeddings (Su et al., 2021)
• Bias: LayerNorm bias terms only

Training

StableCode-Instruct-Alpha-3B is the instruction finetuned version on StableCode-Completion-Alpha-3B with code instruction datasets.

Use and Limitations

Intended Use

StableCode-Instruct-Alpha-3B independently generates new code completions, but we recommend that you use StableCode-Instruct-Alpha-3B together with the tool developed by BigCode and HuggingFace (huggingface/huggingface-vscode: Code completion VSCode extension for OSS models (github.com)), to identify and, if necessary, attribute any outputs that match training code.

Limitations and bias

This model is intended to be used responsibly. It is not intended to be used to create unlawful content of any kind, to further any unlawful activity, or to engage in activities with a high risk of physical or economic harm.

How to cite

@misc{StableCodeInstructAlpha, 
      url={[https://huggingface.co/stabilityai/stablecode-instruct-alpha-3b](https://huggingface.co/stabilityai/stablecode-instruct-alpha-3b)}, 
      title={Stable Code Instruct Alpha}, 
      author={Adithyan, Reshinth and Phung, Duy and Cooper, Nathan and Pinnaparaju, Nikhil and Laforte, Christian}
}