Upload test.py

test.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
+import torch
+
+def generate_response(model, tokenizer, instruction, input_text, temperature, top_p, top_k, repeat_penalty):
+    PROMPT = f'''### Instruction: 
+{instruction}
+### Input:
+{input_text}
+
+### Response:'''
+
+    input_ids = tokenizer.encode(PROMPT, return_tensors='pt')
+    max_length = len(input_ids[0]) + 50  # Example, you can set your preferred value
+
+    # Set generation parameters within given ranges
+    gen_parameters = {
+        'temperature': temperature,
+        'top_p': top_p,
+        'top_k': top_k,
+        'repetition_penalty': repeat_penalty,
+        'max_length': max_length,
+        'max_new_tokens': 50 # Example, you can set your preferred value
+    }
+
+    output = model.generate(input_ids, **gen_parameters)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    return response[len(PROMPT):]  # Removing the prompt part
+
+
+def main():
+    MODEL_NAME = 'Yoko-7B-Japanese-v1 ' # Replace with your model's　file path or name
+
+    # Load pre-trained model and tokenizer
+    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+    tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME)
+
+    instruction = '次の問題を回答してください。'
+    # instruction = 'Please answer following question.'
+    input_text = '東京は何国の都市ですか？'
+
+    # Example parameter values, you can modify these within the ranges you provided
+    temperature = 0.6
+    top_p = 0.7
+    top_k = 40
+    repeat_penalty = 1.1
+
+    response = generate_response(model, tokenizer, instruction, input_text, temperature, top_p, top_k, repeat_penalty)
+    print('response'+response)
+
+if __name__ == '__main__':
+    main()