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README.md
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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# 鍑哄姏鏂规硶
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""" python """
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from tqdm import tqdm
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# 鎺ㄨ珫
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results = []
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for dt in tqdm(data):
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input = dt["input"]
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prompt = f"""### 鎸囩ず\n{input}\n### 鍥炵瓟\n"""
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inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
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prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 鍥炵瓟')[-1]
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results.append({"task_id": data["task_id"], "input": input, "output": output})
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with open(f"/content/{model_name}_output.jsonl", 'w', encoding='utf-8') as f:
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for result in results:
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json.dump(result, f, ensure_ascii=False)
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f.write('\n')
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"""
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