Update README.md

README.md

@@ -11,192 +11,123 @@ tags: []
 <!-- Provide a quick summary of what the model is/does. -->
 
 
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
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-### Results
-
-[More Information Needed]
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-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
-
-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
+# Code
+```python
+# 必要なライブラリをインストール
+# python 3.10.12環境を前提としています
+!pip install -U pip
+!pip install -U transformers
+!pip install -U bitsandbytes
+!pip install -U accelerate
+!pip install -U datasets
+!pip install -U peft
+!pip install -U trl
+!pip install -U wandb
+!pip install ipywidgets --upgrade
+
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+from peft import PeftModel
+import torch
+from tqdm import tqdm
+import json
+
+# Hugging Faceで取得したTokenを設定
+# Hugging Face Hubの[Settings > Access Tokens]で新規トークンを作成してください。
+from google.colab import userdata
+HF_TOKEN = userdata.get('HF_TOKEN')
+
+# モデルIDとアダプタIDを指定
+model_id = "llm-jp/llm-jp-3-13b"
+adapter_id = "totsukash/llm-jp-3-13b-finetune"
+
+# QLoRA設定
+# 量子化（4bit）を行い、効率的なメモリ使用を実現
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+# モデルをロード
+# device_map="auto"で、利用可能なGPUやCPUに自動的に割り当て
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    token = HF_TOKEN
+)
+
+# トークナイザーをロード
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, token = HF_TOKEN)
+
+# LoRAアダプタを統合
+model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
+
+# データセットの読み込み
+# 評価用データはjsonl形式（各行がJSONオブジェクト）で保存されている必要があります。
+datasets = []
+with open("/content/elyza-tasks-100-TV_0.jsonl", "r") as f:
+    item = ""
+    for line in f:
+      line = line.strip()
+      item += line
+      if item.endswith("}"):  # JSONオブジェクトの終了を検出
+        datasets.append(json.loads(item))
+        item = ""
+
+# 推論処理
+# 各タスクの入力をモデルに渡し、生成された出力を収集します。
+results = []
+for data in tqdm(datasets):
+    input = data["input"]
+    prompt = f"""### 指示
+    {input}
+    ### 回答
+    """
+    
+    # 入力トークンを生成してモデルに入力
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
+    outputs = model.generate(input_ids, max_new_tokens=512, do_sample=False, repetition_penalty=1.2)
+    output = tokenizer.decode(outputs[0][input_ids.size(1):], skip_special_tokens=True)
+    
+    results.append({"task_id": data["task_id"], "input": input, "output": output})
+
+# # llmjp
+# results = []
+# for data in tqdm(datasets):
+
+#   input = data["input"]
+
+#   prompt = f"""### 指示
+#   {input}
+#   ### 回答
+#   """
+
+#   tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
+#   attention_mask = torch.ones_like(tokenized_input)
+#   with torch.no_grad():
+#       outputs = model.generate(
+#           tokenized_input,
+#           attention_mask=attention_mask,
+#           max_new_tokens=100,
+#           do_sample=False,
+#           repetition_penalty=1.2,
+#           pad_token_id=tokenizer.eos_token_id
+#       )[0]
+#   output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
+#   
+#   results.append({"task_id": data["task_id"], "input": input, "output": output})
+
+# 推論結果をJSONL形式で保存
+# ファイル名はアダプタIDに基づいて作成
+import re
+jsonl_id = re.sub(".*/", "", adapter_id)
+with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)  # 日本語対応
+        f.write('\n')
+```