stabilityai-stablecode-instruct-alpha-3b-gguf

⚠️ ARCHIVED / LEGACY MODEL NOTICE
This repository is part of a legacy collection quantized around 2023. To manage storage quotas and maintain active community projects, some rarely used quantization formats (e.g., Q2_K, Q3_K, Q4_1, Q5_1) have been permanently removed.

Only the most popular and stable formats (Q4_0, Q4_K_M, Q5_K_M, Q6_K, and Q8_0) remain available.

💡 Looking for something modern? If you are starting a new project, we highly recommend using newer architectures (like Llama 3, Mistral, or Qwen) provided by official maintainers or active community members (e.g., Bartowski, TheBloke legacy files, or official organization handles).

⚠️ This repository is no longer actively maintained. Existing files are provided "as is" for archival and legacy hardware purposes.

I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information

stablecode-instruct-alpha-3b - GGUF

• Model creator: stabilityai
• Original model: stablecode-instruct-alpha-3b

StableCode

This is a Model based on StableCode. StableCode is a family of language models from Stability AI that focuses specifically on coding.

Note:

Current (as of 2023-11-15) implementations of Llama.cpp only support GPU offloading up to 34 Layers with these StableLM Models. The model will crash immediately if -ngl is larger than 34. The model works fine however without any gpu acceleration.

About GGUF format

gguf is the current file format used by the ggml library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov

Quantization variants

There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:

Legacy quants

Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.

Note:

Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models)

K-quants

K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.

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Original Model Card:

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 [email protected]

Model Architecture

• 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}
}

End of original Model File

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Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.

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