RichardErkhov/hpcgroup_-_hpc-coder-v2-6.7b-gguf

Quantization made by Richard Erkhov.

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hpc-coder-v2-6.7b - GGUF

• —Model creator: https://huggingface.co/hpcgroup/
• —Original model: https://huggingface.co/hpcgroup/hpc-coder-v2-6.7b/

Original model description: --- library_name: transformers tags:

• —code
• —hpc
• —parallel
• —axonn datasets:
• —hpcgroup/hpc-instruct
• —ise-uiuc/Magicoder-OSS-Instruct-75K
• —nickrosh/Evol-Instruct-Code-80k-v1 language:
• —en pipeline_tag: text-generation ---

HPC-Coder-v2

The HPC-Coder-v2-6.7b model is an HPC code LLM fine-tuned on an instruction dataset catered to common HPC topics such as parallelism, optimization, accelerator porting, etc. This version is a fine-tuning of the Deepseek Coder 6.7b model. It is fine-tuned on the hpc-instruct, oss-instruct, and evol-instruct datasets. We utilized the distributed training library AxoNN to fine-tune in parallel across many GPUs.

HPC-Coder-v2-1.3b and HPC-Coder-v2-6.7b are two of the most capable open-source LLMs for parallel and HPC code generation. HPC-Coder-v2-6.7b is the best performing LLM under 30b parameters on the ParEval parallel code generation benchmark in terms of correctness and performance. It scores similarly to 34B and commercial models like Phind-V2 and GPT-4 on parallel code generation.

Using HPC-Coder-v2

The model is provided as a standard huggingface model with safetensor weights. It can be used with transformers pipelines, vllm, or any other standard model inference framework. HPC-Coder-v2 is an instruct model and prompts need to be formatted as instructions for best results. It was trained with the following instruct template:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:

Quantized Models

4 and 8 bit quantized weights are available in the GGUF format for use with llama.cpp. The 4 bit model requires ~3.8 GB memory and can be found here. The 8 bit model requires ~7.1 GB memory and can be found here. Further information on how to use them with llama.cpp can be found in its documentation.