Update ml.py

ml.py

@@ -1,34 +1,34 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
-import torch
-import json
-import json
-import re
-import numpy as np
-
-
-def create_prompt(text, template, examples):
-  template = json.dumps(json.loads(template),indent = 4)
-
-  prompt = "<|input|>\n### Template:\n"+template+"\n"
-
-  if examples[0]:
-    example1 = json.dumps(json.loads(examples[0]),indent = 4)
-    prompt+= "### Example:\n"+example1+"\n"
-  if examples[1]:
-    example2 = json.dumps(json.loads(examples[1]),indent = 4)
-    prompt+= "### Example:\n"+example1+"\n"
-  if examples[2]:
-    example3 = json.dumps(json.loads(examples[1]),indent = 4)
-    prompt+= "### Example:\n"+example3+"\n"
-
-  prompt += "### Text:\n"+text+'''\n<|output|>'''
-
-  return prompt
-
-
-def generate_answer_short(prompt,model, tokenizer):
-    model_input = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=3000).to("cuda")
-    with torch.no_grad():
-        gen = tokenizer.decode(model.generate(**model_input, max_new_tokens=1500)[0], skip_special_tokens=True)
-    print(gen.split("<|output|>")[1].split("<|end-output|>")[0])
-    return gen.split("<|output|>")[1].split("<|end-output|>")[0]
+from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
+import torch
+import json
+import json
+import re
+import numpy as np
+
+
+def create_prompt(text, template, examples):
+  template = json.dumps(json.loads(template),indent = 4)
+
+  prompt = "<|input|>\n### Template:\n"+template+"\n"
+
+  if examples[0]:
+    example1 = json.dumps(json.loads(examples[0]),indent = 4)
+    prompt+= "### Example:\n"+example1+"\n"
+  if examples[1]:
+    example2 = json.dumps(json.loads(examples[1]),indent = 4)
+    prompt+= "### Example:\n"+example1+"\n"
+  if examples[2]:
+    example3 = json.dumps(json.loads(examples[1]),indent = 4)
+    prompt+= "### Example:\n"+example3+"\n"
+
+  prompt += "### Text:\n"+text+'''\n<|output|>'''
+
+  return prompt
+
+
+def generate_answer_short(prompt,model, tokenizer):
+    model_input = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=3000).to("cuda")
+    with torch.no_grad():
+        gen = tokenizer.decode(model.generate(**model_input, max_new_tokens=1500)[0], skip_special_tokens=True)
+    print(gen.split("<|output|>")[1])
+    return gen.split("<|output|>")[1].split("<|end-output|>")[0]