Update README.md

README.md

@@ -12,7 +12,7 @@ tags:
 - gguf
 - imatrix
 - importance matrix
-base_model: rombodawg/Llama-3-8B-Instruct-Coder
+base_model: rombodawg/Llama-3-8B-Instruct-Coder-v2
 ---
 
 # Quant Infos
@@ -27,19 +27,26 @@ base_model: rombodawg/Llama-3-8B-Instruct-Coder
   ```
 
 # Original Model Card
-llama-3-8B-Instruct-Coder
+Llama-3-8B-Instruct-Coder-v2
 
 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/0O4cIuv3wNbY68-FP7tak.jpeg)
+_________________________________________________________________________
+How is this model diffrent from rombodawg/Llama-3-8B-Instruct-Coder? Well the first model was trained on a dataset that had some major flaws that I originally had missed, with version 2 all of those flaws are fixed, and the model is fully retrained so it performs much better than the previous iteration.
+_________________________________________________________________________
+This model is llama-3-8b-instruct from Meta (uploaded by unsloth) trained on the full 150k Code Feedback Filtered Instruction dataset. 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.
 
-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.
+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 A5000 24GB in 50 hours for less than $15.
 
 Dataset used for training this model:
 
-- https://huggingface.co/datasets/Replete-AI/OpenCodeInterpreterData
+- https://huggingface.co/datasets/Replete-AI/CodeFeedback-Filtered-Instruction-Simplified-Pairs
 
 Qalore notebook for training:
 
 - https://colab.research.google.com/drive/1bX4BsjLcdNJnoAf7lGXmWOgaY8yekg8p?usp=sharing
-  
+
+Quantizations for easier inference:
+
+- https://huggingface.co/bartowski/Llama-3-8B-Instruct-Coder-v2-GGUF
+
+- https://huggingface.co/bartowski/Llama-3-8B-Instruct-Coder-v2-exl2