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license: bigcode-openrail-m |
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pipeline_tag: text-generation |
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library_name: gguf |
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<u>**NOTE**</u>: You will need a recent build of llama.cpp to run these quants (i.e. at least commit `494c870`). |
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GGUF importance matrix (imatrix) quants for https://huggingface.co/TechxGenus/starcoder2-15b-instruct |
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* The importance matrix was trained for ~50K tokens (105 batches of 512 tokens) using a [general purpose imatrix calibration dataset](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384). |
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* The [imatrix is being used on the K-quants](https://github.com/ggerganov/llama.cpp/pull/4930) as well. |
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> Fine-tuned starcoder2-15b with an additional 0.7 billion high-quality, code-related tokens for 3 epochs. We used DeepSpeed ZeRO 3 and Flash Attention 2 to accelerate the training process. It achieves 77.4 pass@1 on HumanEval-Python. This model operates using the Alpaca instruction format (excluding the system prompt). |
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| Layers | Context | [Template](https://huggingface.co/TechxGenus/starcoder2-15b-instruct#usage) | |
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| --- | --- | --- | |
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| <pre>40</pre> | <pre>16384</pre> | <pre>### Instruction<br>{instruction}<br>### Response<br>{response}</pre> | |
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