Update README.md

README.md

@@ -62,4 +62,73 @@ slices:
     parameters:
       density: 0.55
       weight: 0.56
+```
+
+
+
+```Python
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    HfArgumentParser,
+    TrainingArguments,
+    pipeline,
+    logging,
+    GenerationConfig,
+    TextIteratorStreamer,
+)
+import torch
+
+new_model= "NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_all_Cluster_merge_v1"
+model = AutoModelForCausalLM.from_pretrained(#f'NickyNicky/{new_model}',
+                                             new_model,
+                                             device_map="auto",
+                                             trust_remote_code=True,
+                                             torch_dtype=torch.bfloat16,
+
+                                             low_cpu_mem_usage= True,
+                                            #  use_flash_attention_2=False,
+
+                                             )
+
+
+tokenizer = AutoTokenizer.from_pretrained(new_model,
+                                          max_length=2048,
+                                          trust_remote_code=True,
+                                          use_fast = True,
+                                          )
+
+tokenizer.pad_token = tokenizer.eos_token
+# tokenizer.padding_side = 'left'
+tokenizer.padding_side = 'right'
+
+
+prompt= """<|im_start|>system
+You are a helpful AI assistant.<|im_end|>
+<|im_start|>user
+escribe una historia de amor.<|im_end|>
+<|im_start|>assistant
+"""
+
+inputs = tokenizer.encode(prompt,
+                          return_tensors="pt",
+                          add_special_tokens=False).cuda()#.to("cuda") # False # True
+
+
+generation_config = GenerationConfig(
+              max_new_tokens=700,
+              temperature=0.5,
+              top_p=0.9,
+              top_k=40,
+              repetition_penalty=1.1, #1.1, # 1.0 means no penalty, > 1.0 means penalty, 1.2 from CTRL paper
+              do_sample=True,
+              pad_token_id=tokenizer.eos_token_id,
+              eos_token_id=tokenizer.eos_token_id,
+          )
+outputs = model.generate(
+                         generation_config=generation_config,
+                         input_ids=inputs,)
+# tokenizer.decode(outputs[0], skip_special_tokens=False) #True
+print(tokenizer.decode(outputs[0], skip_special_tokens=False))
 ```