Update README.md

README.md

@@ -11,6 +11,111 @@ tags:
 - trl
 - sft
 ---
+``` Python
+# Question
+
+if False:
+    from unsloth import FastLanguageModel
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING
+        max_seq_length = max_seq_length,
+        dtype = dtype,
+        load_in_4bit = load_in_4bit,
+    )
+    FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+# alpaca_prompt = You MUST copy from above!
+
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "Eu preciso treinar uma IA gpt2 do hugginface em meu computador", # instruction
+        "", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 4096)
+
+# Answer
+
+<s> Below is an instruction that describes a task related to Python programming or computer science concepts. Provide a detailed, accurate, and helpful response that addresses the query or solves the problem presented. Follow these guidelines:
+
+1. If the task involves coding, provide a complete, working Python solution with explanations.
+2. If it's a conceptual question, explain thoroughly, using examples where appropriate.
+3. Break down complex problems or concepts into steps or components.
+4. If you're unsure about any part of your response, clearly state your level of confidence and any assumptions you're making.
+5. If the query is ambiguous or lacks necessary information, ask for clarification before proceeding.
+6. After providing your response, briefly summarize the key points or solution.
+
+Remember, accuracy and helpfulness are crucial. Avoid making unfounded claims or providing incorrect information.
+
+### Input:
+Eu preciso treinar uma IA gpt2 do hugginface em meu computador
+
+### Output:
+ 
+# Importações necessárias
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from transformers import TrainingArguments, Trainer
+from transformers.data import DataCollatorForLanguageModeling
+from transformers.data.processors import DataProcessor
+
+# Configurações
+training_args = TrainingArguments(
+   output_dir='./resultados',
+   evaluation_strategy='epoch',
+   learning_rate=2e-5,
+   per_device_train_batch_size=8,
+   eval_steps=1000,
+   num_warmupSteps=500,
+   weighted_metric='accuracy',
+)
+
+# Carregamento do tokenizador
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+
+# Carregamento do modelo
+modelo = GPT2LMHeadModel.from_pretrained('gpt2')
+
+# Configurações do tracker
+tracker = DataProcessor(
+   input_class=DataProcessor,
+   batch_size=1,
+   max_length=100,
+   padding=True,
+   truncation=True,
+)
+
+# Treinando o modelo
+trainer = Trainer(
+   args=training_args,
+   train_loop=tracker,
+   trainable_pipelines=[],
+)
+
+# Treinando o modelo
+trainer.train()
+
+# Evaluação do modelo
+evaluador = DataCollatorForLanguageModeling(
+   tokenizer=tokenizer,
+   model=modelo,
+   padding=True,
+   max_length=100,
+   batch_size=8,
+)
+
+# Evalua o modelo
+resultados = trainer.evaluate()
+
+# Imprimir os resultados
+for name, loss, acc in resultados:
+   print(f'{name}: {loss}, {acc:.2f}%')</s>
+
+```
 
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