<p><strong><font size="5">Information</font></strong></p>
Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.
<p>Quantized using <i>--true-sequential</i> and <i>--act-order</i> optimizations.</p>
This was made using Chansung's 30B Alpaca Lora: https://huggingface.co/chansung/alpaca-lora-30b

<p><strong><font size="5">Update 04.06.2023</font></strong></p>
<p>This is a more recent merge of Chansung's Alpaca Lora which was updated using the clean alpaca dataset as of 04/06/2023 with refined training parameters</p>
<p><strong>Training Parameters</strong></p>
<ul><li>num_epochs=10</li><li>cutoff_len=512</li><li>group_by_length</li><li>lora_target_modules='[q_proj,k_proj,v_proj,o_proj]'</li><li>lora_r=16</li><li>micro_batch_size=8</li></ul>

<p><strong><font size="5">Benchmarks</font></strong></p>

<strong>Wikitext2</strong>: 4.608365058898926

<strong>Ptb-New</strong>: 8.69663143157959

<strong>C4-New</strong>: 6.624773979187012

<strong>Note</strong>: This version does not use <i>--groupsize 128</i>, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.
{}
transformers pytorch llama text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

<p><strong><font size="5">Information</font></strong></p>
Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.
<p>Quantized using <i>--true-sequential</i> and <i>--act-order</i> optimizations.</p>
This was made using Chansung's 30B Alpaca Lora: URL

<p><strong><font size="5">Update 04.06.2023</font></strong></p>
<p>This is a more recent merge of Chansung's Alpaca Lora which was updated using the clean alpaca dataset as of 04/06/2023 with refined training parameters</p>
<p><strong>Training Parameters</strong></p>
<ul><li>num_epochs=10</li><li>cutoff_len=512</li><li>group_by_length</li><li>lora_target_modules='[q_proj,k_proj,v_proj,o_proj]'</li><li>lora_r=16</li><li>micro_batch_size=8</li></ul>

<p><strong><font size="5">Benchmarks</font></strong></p>

<strong>Wikitext2</strong>: 4.608365058898926

<strong>Ptb-New</strong>: 8.69663143157959

<strong>C4-New</strong>: 6.624773979187012

<strong>Note</strong>: This version does not use <i>--groupsize 128</i>, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.
TAGS
#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

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