Update README.md

README.md

@@ -15,53 +15,53 @@ language:
 # Model Trained By Meforgers
 *This model was trained by Meforgers for the futuristic projects.*
 
-# *Firstly*
-- Need to install packages
+- # *Firstly*
+ - Need to install packages
 
 
-# *Usage*
+- # *Usage*
 
-```python
-from unsloth import FastLanguageModel
-import torch
-
-# Variable side
-max_seq_length = 512 
-dtype = torch.float16 
-load_in_4bit = True
-
-# Alpaca prompt
-alpaca_prompt = """### Instruction:
-{0}
-
-### Input:
-{1}
-
-### Response:
-{2}
-"""
-
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name="Meforgers/Aixr",
-    max_seq_length=max_seq_length,
-    dtype=dtype,
-    load_in_4bit=load_in_4bit,
-)
-
-FastLanguageModel.for_inference(model)
-
-inputs = tokenizer(
-    [
-        alpaca_prompt.format(
-            "Can u text me basic python code?",  # instruction side (You need to change that side)
-            "",  # input
-            "",  # output - leave this blank for generation!
-        )
-    ],
-    return_tensors="pt"
-).to("cuda")
-
-outputs = model.generate(**inputs, max_new_tokens=128, use_cache=True)
-print(tokenizer.batch_decode(outputs))
-
-```
+  ```python
+  from unsloth import FastLanguageModel
+  import torch
+  
+  # Variable side
+  max_seq_length = 512 
+  dtype = torch.float16 
+  load_in_4bit = True
+  
+  # Alpaca prompt
+  alpaca_prompt = """### Instruction:
+  {0}
+  
+  ### Input:
+  {1}
+  
+  ### Response:
+  {2}
+  """
+  
+  model, tokenizer = FastLanguageModel.from_pretrained(
+      model_name="Meforgers/Aixr",
+      max_seq_length=max_seq_length,
+      dtype=dtype,
+      load_in_4bit=load_in_4bit,
+  )
+  
+  FastLanguageModel.for_inference(model)
+  
+  inputs = tokenizer(
+      [
+          alpaca_prompt.format(
+              "Can u text me basic python code?",  # instruction side (You need to change that side)
+              "",  # input
+              "",  # output - leave this blank for generation!
+          )
+      ],
+      return_tensors="pt"
+  ).to("cuda")
+  
+  outputs = model.generate(**inputs, max_new_tokens=128, use_cache=True)
+  print(tokenizer.batch_decode(outputs))
+  
+  ```