hpcgroup
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hpc-coder-v2-1.3b

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 ---
 library_name: transformers
-tags: []
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 ---
 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).