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@@ -17,3 +17,20 @@ We utilized the distributed training library [AxoNN](https://github.com/axonn-ai
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  HPC-Coder-v2-6.7b is the best performing LLM under 30b parameters on the [ParEval](https://github.com/parallelcodefoundry/ParEval) parallel code generation benchmark in terms of _correctness_ and _performance_.
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  It scores similarly to 34B and commercial models like Phind-V2 and GPT-4 on parallel code generation.
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  HPC-Coder-v2-6.7b is the best performing LLM under 30b parameters on the [ParEval](https://github.com/parallelcodefoundry/ParEval) parallel code generation benchmark in terms of _correctness_ and _performance_.
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  It scores similarly to 34B and commercial models like Phind-V2 and GPT-4 on parallel code generation.
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+ ## Using HPC-Coder-v2
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+
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+ The model is provided as a standard huggingface model with safetensor weights.
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+ It can be used with [transformers pipelines](https://huggingface.co/docs/transformers/en/main_classes/pipelines), [vllm](https://github.com/vllm-project/vllm), or any other standard model inference framework.
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+ HPC-Coder-v2 is an instruct model and prompts need to be formatted as instructions for best results.
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+ It was trained with the following instruct template:
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+
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+ ```md
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+ Below is an instruction that describes a task. Write a response that appropriately completes the request.
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+
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+ ### Instruction:
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+ {instruction}
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+
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+ ### Response:
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+
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+ ```
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+