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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+hpc-coder-v2-1.3b - AWQ
+- Model creator: https://huggingface.co/hpcgroup/
+- Original model: https://huggingface.co/hpcgroup/hpc-coder-v2-1.3b/
+
+
+
+
+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-v2-1.3b 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](https://huggingface.co/hpcgroup/hpc-coder-v2-1.3b), [HPC-Coder-v2-6.7b](https://huggingface.co/hpcgroup/hpc-coder-v2-6.7b), and [HPC-Coder-v2-16b](https://huggingface.co/hpcgroup/hpc-coder-v2-16b) are the most capable open-source LLMs for parallel and HPC code generation. 
+HPC-Coder-v2-16b is currently the best performing open-source LLM 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.
+HPC-Coder-v2-6.7b is not far behind the 16b in terms of performance.
+
+## 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 ~0.8 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 ~1.4 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).
+