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Update README.md

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@@ -42,7 +42,7 @@ import json
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  Specify the base model and the adapter for LoRA fine-tuning. Replace <adapter_id> and <HF_TOKEN> with appropriate values.
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  ``` python
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  # Base model
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- model_name = "kkkeee/llm-jp-3-13b-it"
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  # Hugging Face Token
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  HF_TOKEN = "<your_hf_token>" # Obtain token from https://huggingface.co/settings/tokens
@@ -65,7 +65,7 @@ FastLanguageModel.for_inference(model)
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  ```
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  # Step 4: Load Dataset
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  Prepare your dataset in .jsonl format and upload it to your environment.
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-
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  # Load task data
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  datasets = []
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  with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
@@ -76,34 +76,33 @@ with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
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  if item.endswith("}"):
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  datasets.append(json.loads(item))
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  item = ""
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-
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  # Step 5: Perform Inference
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  Set the model to inference mode and generate predictions.
 
 
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- # Set model to inference mode
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- FastLanguageModel.for_inference(model)
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-
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  results = []
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- for dt in tqdm(datasets):
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- input = dt["input"]
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-
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- prompt = f"""### 指示\n{input}\n### 回答\n"""
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- inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
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- outputs = model.generate(
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- **inputs, max_new_tokens=512, use_cache=True, do_sample=False, repetition_penalty=1.2
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- )
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- prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
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- results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
 
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  # Step 6: Save Results
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  Save the inference results to a .jsonl file. Replace <adapter_id> with the appropriate identifier.
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-
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  # Save results to JSONL
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  json_file_id = re.sub(".*/", "", adapter_id)
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  with open(f"/content/{json_file_id}_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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  Specify the base model and the adapter for LoRA fine-tuning. Replace <adapter_id> and <HF_TOKEN> with appropriate values.
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  ``` python
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  # Base model
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+ model_name = "kkkeee/llm-jp-3-13b-it15"
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  # Hugging Face Token
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  HF_TOKEN = "<your_hf_token>" # Obtain token from https://huggingface.co/settings/tokens
 
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  ```
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  # Step 4: Load Dataset
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  Prepare your dataset in .jsonl format and upload it to your environment.
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+ ```python
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  # Load task data
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  datasets = []
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  with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
 
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  if item.endswith("}"):
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  datasets.append(json.loads(item))
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  item = ""
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+ ```
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  # Step 5: Perform Inference
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  Set the model to inference mode and generate predictions.
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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 data in tqdm(datasets):
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+ input = data["input"]
 
 
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+ prompt = f"""### 指示\n{input}\n### 回答\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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+ output = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
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+ results.append({"task_id": data["task_id"], "input": input, "output": output})
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+ ```
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  # Step 6: Save Results
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  Save the inference results to a .jsonl file. Replace <adapter_id> with the appropriate identifier.
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+ ```python
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  # Save results to JSONL
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  json_file_id = re.sub(".*/", "", adapter_id)
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  with open(f"/content/{json_file_id}_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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+ ```