Update README.md

README.md

@@ -9,4 +9,60 @@ Calibration dataset is wikitext.
 
 [4.5bpw](https://huggingface.co/Panchovix/goliath-120b-exl2/tree/4.5bpw)
 
-[3bpw](https://huggingface.co/Panchovix/goliath-120b-exl2/tree/3bpw)
+[3bpw](https://huggingface.co/Panchovix/goliath-120b-exl2/tree/3bpw)
+
+# Original Model card
+
+# 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.
+
+# Merge process
+
+The models used in the merge are [Xwin](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) and [Euryale](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B).
+
+The layer ranges used are as follows:
+
+```yaml
+- range 0, 16
+  Xwin
+- range 8, 24
+  Euryale
+- range 17, 32
+  Xwin
+- range 25, 40
+  Euryale
+- range 33, 48
+  Xwin
+- range 41, 56
+  Euryale
+- range 49, 64
+  Xwin
+- range 57, 72
+  Euryale
+- range 65, 80
+  Xwin
+```
+
+# Screenshots
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/635567189c72a7e742f1419c/Cat8_Rimaz6Ni7YhQiiGB.png)
+
+# Benchmarks
+Coming soon.
+
+# Acknowledgements
+Credits goes to [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit).
+
+Special thanks to [@Undi95](https://huggingface.co/Undi95) for helping with the merge ratios.