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README.md

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+This quant was made for [infermatic.ai](https://infermatic.ai/)
+
+Dynamic FP8 quant of [goliath-120b-Instruct-FP8-Dynamic](https://huggingface.co/alpindale/goliath-120b) made with AutoFP8.
+
+---
+license: llama2
+language:
+- en
+pipeline_tag: conversational
+tags:
+- merge
+---
+# Goliath 120B
+
+An auto-regressive causal LM created by combining 2x finetuned [Llama-2 70B](https://huggingface.co/meta-llama/llama-2-70b-hf) into one.
+
+Please check out the quantized formats provided by [@TheBloke](https:///huggingface.co/TheBloke) and [@Panchovix](https://huggingface.co/Panchovix):
+
+- [GGUF](https://huggingface.co/TheBloke/goliath-120b-GGUF) (llama.cpp)
+- [GPTQ](https://huggingface.co/TheBloke/goliath-120b-GPTQ) (KoboldAI, TGW, Aphrodite)
+- [AWQ](https://huggingface.co/TheBloke/goliath-120b-AWQ) (TGW, Aphrodite, vLLM)
+- [Exllamav2](https://huggingface.co/Panchovix/goliath-120b-exl2) (TGW, KoboldAI)
+
+# Prompting Format
+
+Both Vicuna and Alpaca will work, but due the initial and final layers belonging primarily to Xwin, I expect Vicuna to work the best.