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--- |
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base_model: llm-jp/llm-jp-3-13b |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# Uploaded model |
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- **Developed by:** IchiShira |
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- **License:** apache-2.0 |
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- **Finetuned from model :** llm-jp/llm-jp-3-13b |
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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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# 出力方法 |
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""" python """ |
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from tqdm import tqdm |
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# 推論 |
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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"""### 指示\n{input}\n### 回ç”\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### 回ç”')[-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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