Update README.md

README.md

@@ -16,17 +16,17 @@ datasets:
 ---
 
 
-# Sumo-Qyuu-7B-v0.1
+# Sumo-T9-7B-v0.1
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65a8a4c5539e211436ef5485/RXiIpU1BwTpvUdhzv-XK9.png)
 
-### Tensorplex Labs Unveils Sumo-Qyuu-7B: Beating Notable 7b Pretrained Models
+### Tensorplex Labs Unveils Sumo-T9-7B: Beating Notable 7b Pretrained Models
 
-[Tensorplex Labs]((https://tensorplex.ai)) is proud to announce that its latest top-performing model on Bittensor Subnet 9, Sumo-Qyuu-7B, 
+[Tensorplex Labs]((https://tensorplex.ai)) is proud to announce that its latest top-performing model on Bittensor Subnet 9, Sumo-T9-7B, 
 has outperformed notable models such as TII Falcon 7B and Meta's Llama-2-7b-hf. This achievement highlights the potential of decentralized networks
 like Bittensor and underscores Tensorplex Labs' commitment to advancing open-source AI technologies.
 
-"Sumo" represents the family of models developed by Tensorplex, and "Qyuu" designates the top-performing model specifically trained for Bittensor Subnet 9.
+"Sumo" represents the family of models developed by Tensorplex, and "T9" designates the top-performing model specifically trained for Bittensor Subnet 9.
 
 Bittensor Subnet 9 serves a unique role within the Bittensor ecosystem by rewarding miners who produce pretrained foundational models on the Falcon Refined Web dataset. This subnet functions as a continuous benchmark, where miners are incentivized to achieve the best performance metrics using a model under the parameter limit. The competitive nature of Subnet 9 drives rapid advancements and refinements in large language model training.
 
@@ -45,7 +45,7 @@ Since the parameter limit was upgraded to 7 billion on April 19, 2024, Tensorple
 - **Training Objective**: Causal Language Modeling (next token prediction)
 - **Original Model Repo**: [tensorplex-labs/pretraining-sn9-7B-1](https://huggingface.co/tensorplex-labs/pretraining-sn9-7B-1)
 
-Sumo-Qyuu-7B-v0.1 features a larger vocabulary size (100k), compatible with the GPT-4 tokenizer, ensuring its versatility across various natural language processing tasks.
+Sumo-T9-7B-v0.1 features a larger vocabulary size (100k), compatible with the GPT-4 tokenizer, ensuring its versatility across various natural language processing tasks.
 
 ⛔ **This is a pretrained base model, which hasn't been aligned yet. Use with caution or finetune further on downstream tasks before deployment.**
 
@@ -63,7 +63,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import transformers
 import torch
 
-model = "tensorplex-labs/Sumo-Qyuu-7B-v0.1"
+model = "tensorplex-labs/Sumo-T9-7B-v0.1"
 
 tokenizer = AutoTokenizer.from_pretrained(model)
 pipeline = transformers.pipeline(
@@ -95,7 +95,7 @@ This model has been trained with [tiiuae/falcon-refinedweb](https://huggingface.
 
 ## Evaluation
 
-Sumo-Qyuu-7B-v0.1 has outperformed notable models such as TII Falcon 7B, Meta's Llama-2-7b and Llama-1-7b in zero-shot performance, 
+Sumo-T9-7B-v0.1 has outperformed notable models such as TII Falcon 7B, Meta's Llama-2-7b and Llama-1-7b in zero-shot performance, 
 establishing itself as the leading model in aggregate across various evaluation tasks. 
 Such benchmarks include ARC Challenge, GSM8K, HellaSwag, MMLU, TruthfulQA, and Winogrande.