starcoder2-15b-instruct-v0.1-GGUF

💫 Community Model> Starcoder2 15B Instruct v0.1 by BigCode

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Model creator: bigcode<br> Original model: starcoder2-15b-instruct-v0.1<br> GGUF quantization: provided by bartowski based on llama.cpp release b2756<br>

Model Summary:

Starcoder2-15B-Instruct-v0.1 is self-proclaimed to be the first entirely self-aligned code model with a fully permissive and transparent pipeline.<br> This model is meant to be used for coding instructions in a <b>single turn</b>, any other styles may result in less accurate responses.<br> Starcoder2 has been primarily finetuned for Python code generation and as such should primarily be used for Python tasks.

Prompt Template:

Choose the 'Starcoder2 Instruct' preset in your LM Studio.

Under the hood, the model will see a prompt that's formatted like so:

<|endoftext|>You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.

### Instruction
{prompt}

### Response
<|endoftext|>

Use case and examples

This model should be used for single turn coding related instructions.

Coding with requirements

Creating unit tests

More coding examples

Technical Details

Starcoder2 15B instruct was trained primarily on Python code generation tasks. Using Starcoder2 15B (non instruct) to generated thousands of instruction-reponse pairs, the results were used to fine tune an instruct model without human annotation or distilled data.

The dataset created is open and available: self-oss-instruct-sc2-exec-filter-50k

And the code used to create the self-alignment has been shared here: starcoder2-self-align

The results of the self-alignment are extremely promising, with significantly higher scores across all coding benchmarks, which is a great sign for future progress.

More details on their model card here

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

🙏 Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for these quants, which improves the overall quality!

Disclaimers

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