Update README.md

README.md

@@ -6,7 +6,7 @@ datasets:
 - psmathur/lima_unchained_v1
 ---
 
-# model_42_70b
+# Lima_Unchained_70b
 
 A Llama2-70b model fine-tuned using QLora on all the linear layers with carefully selected ~900 conversations from the [Lima](https://arxiv.org/pdf/2305.11206.pdf) 
 
@@ -17,7 +17,7 @@ A Llama2-70b model fine-tuned using QLora on all the linear layers with carefull
 
 ## Evaluation
 
-We evaluated model_42_70b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. 
+We evaluated Lima_Unchained_70b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. 
 
 Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
 
@@ -52,9 +52,11 @@ Below shows a code example on how to use this model
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("psmathur/model_42_70b")
+model_path="pankajmathur/Lima_Unchained_70b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForCausalLM.from_pretrained(
-  "psmathur/model_42_70b",
+  model_path,
   torch_dtype=torch.float16,
   load_in_8bit=True,
   low_cpu_mem_usage=True,
@@ -89,9 +91,9 @@ Exercise caution and cross-check information when necessary.
 Please kindly cite using the following BibTeX:
 
 ```
-@misc{model_42_70b,
+@misc{Lima_Unchained_70b,
   author = {Pankaj Mathur},
-  title = {model_42_70b: A LIMA style Llama2-70b model},
+  title = {Lima_Unchained_70b: A LIMA style Llama2-70b model},
   year = {2023},
   publisher = {HuggingFace},
   journal = {HuggingFace repository},