diff --git a/configs/eval/generate_gptj_0.yaml b/configs/eval/generate_gptj_0.yaml
new file mode 100644
index 00000000..805bfbb6
--- /dev/null
+++ b/configs/eval/generate_gptj_0.yaml
@@ -0,0 +1,15 @@
+# model/tokenizer
+model_name: 'nomic-ai/gpt4all-gptj-epoch_0'
+tokenizer_name: 'EleutherAI/gpt-j-6B'
+lora: false
+lora_path: 'nolora'
+
+max_new_tokens: 512
+temperature: 0.001
+prompt: | 
+  #this code prints a string reversed
+  my_string = "hello how are you"
+  print(len(my_string))
+
+
+  My code above does not work. Can you help me?
diff --git a/eval_self_instruct.py b/eval_self_instruct.py
index e0dbded1..548f7bff 100644
--- a/eval_self_instruct.py
+++ b/eval_self_instruct.py
@@ -22,24 +22,25 @@ def read_jsonl_file(file_path):
     return data
 
 def setup_model(config):
-    model = AutoModelForCausalLM.from_pretrained(config["model_name"], device_map="auto", torch_dtype=torch.float16, output_hidden_states=True)
+    model = AutoModelForCausalLM.from_pretrained(config["model_name"], device_map="auto", torch_dtype=torch.float16, output_hidden_states=True, use_auth_token=True)
     tokenizer = AutoTokenizer.from_pretrained(config["tokenizer_name"])
-    added_tokens = tokenizer.add_special_tokens({"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>"})
+    if "gptj" in config["model_name"]:
+        tokenizer.pad_token = tokenizer.eos_token
+    else:
+        added_tokens = tokenizer.add_special_tokens({"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>"})
 
-    if added_tokens > 0:
-        model.resize_token_embeddings(len(tokenizer))
+        if added_tokens > 0:
+            model.resize_token_embeddings(len(tokenizer))
 
-    if 'lora' in config and config['lora']:
-        model = PeftModelForCausalLM.from_pretrained(model, config["lora_path"], device_map="auto", torch_dtype=torch.float16, return_hidden_states=True)
-        model.to(dtype=torch.float16)
+    if config["lora"]:
+        model = PeftModelForCausalLM.from_pretrained(model, config["lora_path"], device_map="auto", torch_dtype=torch.float16)
 
+    model.to(dtype=torch.float16)
     print(f"Mem needed: {model.get_memory_footprint() / 1024 / 1024 / 1024:.2f} GB")
-        
     return model, tokenizer
 
 
 
-
 def eval_example(model, tokenizer, example, config):
 
     prompt = example['instruction'] + ' ' + example['instances'][0]['input']