Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: Qwen/Qwen2.5-1.5B-Instruct
+base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
 - text-generation-inference
 - transformers
@@ -19,16 +19,16 @@ pipeline_tag: text-generation
 library_name: transformers
 ---
 
-![Zunich Banner](https://cdn.ruben-roy.com/AI/Zurich/img/banner-1.5B-10k.png)
+![Zunich Banner](https://cdn.ruben-roy.com/AI/Zurich/img/banner-7B-10k.png)
 
-# Zurich 1.5B GammaCorpus v2-10k 
+# Zurich 7B GammaCorpus v2-10k 
 *A Qwen 2.5 model fine-tuned on the GammaCorpus dataset*
 
 ## Overview
-Zurich 1.5B GammaCorpus v2-10k is a fine-tune of Alibaba's **Qwen 2.5 1.5B Instruct** model. Zurich is designed to outperform other models that have a similar size while also showcasing [GammaCorpus v2-10k](https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-10k).
+Zurich 7B GammaCorpus v2-10k is a fine-tune of Alibaba's **Qwen 2.5 7B Instruct** model. Zurich is designed to outperform other models that have a similar size while also showcasing [GammaCorpus v2-10k](https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-10k).
 
 ## Model Details
-- **Base Model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
+- **Base Model:** [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
 - **Type:** Causal Language Models
 - **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
 - **Number of Parameters:** 7.61B
@@ -38,7 +38,7 @@ Zurich 1.5B GammaCorpus v2-10k is a fine-tune of Alibaba's **Qwen 2.5 1.5B Instr
 
 ## Training Details
 
-Zurich-1.5B-GCv2-10k underwent fine-tuning with 1 A100 GPU for ~5 minutes and trained with the [Unsloth](https://unsloth.ai/) framework. Zurich-1.5B-GCv2-10k was trained for **60 Epochs**. 
+Zurich-7B-GCv2-10k underwent fine-tuning with 1 T4 GPU for ~20 minutes and trained with the [Unsloth](https://unsloth.ai/) framework. Zurich-7B-GCv2-10k was trained for **60 Epochs**. 
 
 ## Usage
 
@@ -57,7 +57,7 @@ Here is a code snippet with `apply_chat_template` to show you how to load the to
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "rubenroy/Zurich-1.5B-GCv2-10k"
+model_name = "rubenroy/Zurich-7B-GCv2-10k"
 
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -68,7 +68,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 prompt = "How tall is the Eiffel tower?"
 messages = [
-    {"role": "system", "content": "You are Zurich, an AI assistant built on the Qwen 2.5 1.5B model developed by Alibaba Cloud, and fine-tuned by Ruben Roy. You are a helpful assistant."},
+    {"role": "system", "content": "You are Zurich, an AI assistant built on the Qwen 2.5 7B model developed by Alibaba Cloud, and fine-tuned by Ruben Roy. You are a helpful assistant."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(