Update README.md

README.md

@@ -1,4 +1,7 @@
 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
 tags:
 - 4-bit
@@ -6,8 +9,15 @@ tags:
 - text-generation
 - autotrain_compatible
 - endpoints_compatible
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+- sft
 pipeline_tag: text-generation
 inference: false
+base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
 quantized_by: Suparious
 ---
 # rombodawg/Llama-3-8B-Instruct-Coder AWQ
@@ -15,7 +25,13 @@ quantized_by: Suparious
 - Model creator: [rombodawg](https://huggingface.co/rombodawg)
 - Original model: [Llama-3-8B-Instruct-Coder](https://huggingface.co/rombodawg/Llama-3-8B-Instruct-Coder)
 
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/0O4cIuv3wNbY68-FP7tak.jpeg)
 
+## Model Summary
+
+This model is llama-3-8b-instruct from Meta (uploaded by unsloth) trained on the full 65k Codefeedback dataset + the additional 150k Code Feedback Filtered Instruction dataset combined. You can find that dataset linked below. This AI model was trained with the new Qalore method developed by my good friend on Discord and fellow Replete-AI worker walmartbag.
+
+The Qalore method uses Qlora training along with the methods from Galore for additional reductions in VRAM allowing for llama-3-8b to be loaded on 14.5 GB of VRAM. This allowed this training to be completed on an RTX A4000 16GB in 130 hours for less than $20.
 
 ## How to use