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--- |
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base_model: upstage/SOLAR-10.7B-Instruct-v1.0 |
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inference: false |
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language: |
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- en |
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license: apache-2.0 |
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model-index: |
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- name: SOLAR-10.7B-Instruct-v1.0 |
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results: [] |
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model_creator: Upstage |
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model_name: SOLAR-10.7B-Instruct-v1.0 |
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model_type: solar |
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prompt_template: | |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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quantized_by: Inferless |
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tags: |
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- SOLAR |
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- instruct |
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- finetune |
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- vllm |
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- GPTQ |
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--- |
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<!-- markdownlint-disable MD041 --> |
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<!-- header start --> |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://pbs.twimg.com/profile_banners/1633782755669708804/1678359514/1500x500" alt="Inferless" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em; padding-left:4.5em">Serverless GPUs to scale your machine learning inference without any hassle of managing servers, deploy complicated and custom models with ease.</p> |
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</div> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> |
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;"><a href="https://0ooatrmbp25.typeform.com/to/nzuhQtba">Join Private Beta</a></p></div> |
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> |
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# SOLAR-10.7B-Instruct-v1.0 - GPTQ |
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- Model creator: [Upstage](https://huggingface.co/upstage) |
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- Original model: [SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) |
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<!-- description start --> |
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## Description |
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This repo contains GPTQ model files for [Upstage's SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0). |
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### About GPTQ |
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GPTQ is a method that compresses the model size and accelerates inference by quantizing weights based on a calibration dataset, aiming to minimize mean squared error in a single post-quantization step. GPTQ achieves both memory efficiency and faster inference. |
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It is supported by: |
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- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ |
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- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. |
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- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) |
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- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers |
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- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code |
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## Provided files, and AWQ parameters |
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I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. |
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Models are released as sharded safetensors files. |
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| Branch | Bits | GS | AWQ Dataset | Seq Len | Size | |
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| ------ | ---- | -- | ----------- | ------- | ---- | |
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| [main](https://huggingface.co/TheBloke/Nous-Hermes-2-SOLAR-10.7B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.96 GB |
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