---
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-v2-1.3b 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 1.3b](https://huggingface.co./deepseek-ai/deepseek-coder-1.3b-base) model. 
It is fine-tuned on the [hpc-instruct](https://huggingface.co./datasets/hpcgroup/hpc-instruct), [oss-instruct](https://huggingface.co./datasets/ise-uiuc/Magicoder-OSS-Instruct-75K), and [evol-instruct](https://huggingface.co./datasets/nickrosh/Evol-Instruct-Code-80k-v1) datasets.
We utilized the distributed training library [AxoNN](https://github.com/axonn-ai/axonn) to fine-tune in parallel across many GPUs.

HPC-Coder-v2-1.3b and [HPC-Coder-v2-1.3b](https://huggingface.co./hpcgroup/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](https://github.com/parallelcodefoundry/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](https://huggingface.co./docs/transformers/en/main_classes/pipelines), [vllm](https://github.com/vllm-project/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:

```md
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](https://github.com/ggerganov/llama.cpp).
The 4 bit model requires ~3.8 GB memory and can be found [here](https://huggingface.co./hpcgroup/hpc-coder-v2-1.3b-Q4_K_S-GGUF).
The 8 bit model requires ~7.1 GB memory and can be found [here](https://huggingface.co./hpcgroup/hpc-coder-v2-1.3b-Q8_0-GGUF).
Further information on how to use them with llama.cpp can be found in [its documentation](https://github.com/ggerganov/llama.cpp).