## This is a 4bit quant of https://huggingface.co/Aeala/GPT4-x-AlpacaDente2-30b



# My secret sauce:
  * Using comit <a href="https://github.com/0cc4m/GPTQ-for-LLaMa/tree/3c16fd9c7946ebe85df8d951cb742adbc1966ec7">3c16fd9</a> of 0cc4m's GPTQ fork
  * Using PTB as the calibration dataset    
  * Act-order, True-sequential, percdamp 0.1
     (<i>the default percdamp is 0.01</i>)
  * No groupsize
  * Will run with CUDA, does not need triton.
  * Quant completed on a 'Premium GPU' and 'High Memory' Google Colab.

## Benchmark results

|<b>Model<b>|<b>C4<b>|<b>WikiText2<b>|<b>PTB<b>|
|:---:|---|---|---|
|Aeala's FP16|7.05504846572876|4.662261962890625|24.547462463378906|
|This Quant|7.326207160949707|4.957101345062256|24.941526412963867|
|Aeala's Quant <a href="https://huggingface.co/Aeala/GPT4-x-AlpacaDente2-30b/resolve/main/4bit.safetensors">here</a>|7.332120418548584|5.016242980957031|25.576189041137695|



{}
transformers llama text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

This is a 4bit quant of URL
---------------------------


My secret sauce:
================


* Using comit <a href="URL of 0cc4m's GPTQ fork
* Using PTB as the calibration dataset
* Act-order, True-sequential, percdamp 0.1
(*the default percdamp is 0.01*)
* No groupsize
* Will run with CUDA, does not need triton.
* Quant completed on a 'Premium GPU' and 'High Memory' Google Colab.


Benchmark results
-----------------




TAGS
#transformers #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
