49 lines
2.9 KiB
Markdown
49 lines
2.9 KiB
Markdown
---
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language:
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- en
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library_name: transformers
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license: apache-2.0
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tags:
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- gpt
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- llm
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- large language model
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- h2o-llmstudio
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thumbnail: >-
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https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
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pipeline_tag: text-generation
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quantized_by: h2oai
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---
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# h2o-danube3-500m-chat-GGUF
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- Model creator: [H2O.ai](https://huggingface.co/h2oai)
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- Original model: [h2oai/h2o-danube3-500m-chat](https://huggingface.co/h2oai/h2o-danube3-500m-chat)
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## Description
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This repo contains GGUF format model files for [h2o-danube3-500m-chat](https://huggingface.co/h2oai/h2o-danube3-500m-chat) quantized using [llama.cpp](https://github.com/ggerganov/llama.cpp/) framework.
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Table below summarizes different quantized versions of [h2o-danube3-500m-chat](https://huggingface.co/h2oai/h2o-danube3-500m-chat). It shows the trade-off between size, speed and quality of the models.
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| Name | Quant method | Model size | MT-Bench AVG | Perplexity | Tokens per second |
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|:----------------------------------|:----------------------------------:|:----------:|:------------:|:------------:|:-------------------:|
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| [h2o-danube3-500m-chat-F16.gguf](https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF/blob/main/h2o-danube3-500m-chat-F16.gguf) | F16 | 1.03 GB | 3.34 | 9.46 | 1870 |
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| [h2o-danube3-500m-chat-Q8_0.gguf](https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF/blob/main/h2o-danube3-500m-chat-Q8_0.gguf) | Q8_0 | 0.55 GB | 3.76 | 9.46 | 2144 |
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| [h2o-danube3-500m-chat-Q6_K.gguf](https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF/blob/main/h2o-danube3-500m-chat-Q6_K.gguf) | Q6_K | 0.42 GB | 3.77 | 9.46 | 2418 |
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| [h2o-danube3-500m-chat-Q5_K_M.gguf](https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF/blob/main/h2o-danube3-500m-chat-Q5_K_M.gguf) | Q5_K_M | 0.37 GB | 3.20 | 9.55 | 2430 |
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| [h2o-danube3-500m-chat-Q4_K_M.gguf](https://huggingface.co/h2oai/h2o-danube3-500m-chat-GGUF/blob/main/h2o-danube3-500m-chat-Q4_K_M.gguf) | Q4_K_M | 0.32 GB | 3.16 | 9.96 | 2427 |
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Columns in the table are:
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* Name -- model name and link
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* Quant method -- quantization method
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* Model size -- size of the model in gigabytes
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* MT-Bench AVG -- [MT-Bench](https://arxiv.org/abs/2306.05685) benchmark score. The score is from 1 to 10, the higher, the better
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* Perplexity -- perplexity metric on WikiText-2 dataset. It's reported in a perplexity test from llama.cpp. The lower, the better
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* Tokens per second -- generation speed in tokens per second, as reported in a perplexity test from llama.cpp. The higher, the better. Speed tests are done on a single H100 GPU
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## Prompt template
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```
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<|prompt|>Why is drinking water so healthy?</s><|answer|>
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```
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