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README.md

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+
 ---
-library_name: transformers
-tags: []
+base_model: google/gemma-2-9b
+library_name: peft
 ---
 
-# Model Card for Model ID
-
-<!-- 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. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+# モデルカード: google/gemma-2-9b
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+## モデル概要
 
-## Evaluation
+このモデルは、松尾研LLM講座の終了課題の提出用のモデルです。
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+| 項目 | 内容 |
+|------|------|
+| 開発者 | masakiai |
+| ファインチューニング元モデル | [google/gemma-2-9b] |
+| 対応言語 | 日本語 |
+| ライセンス | [apache-2.0]|
 
-### Testing Data, Factors & Metrics
 
-#### Testing Data
+## モデルソース
 
-<!-- This should link to a Dataset Card if possible. -->
+- **リポジトリ:** [https://huggingface.co/masakiai/gemma-2-9b-finetune]
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### 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).
-
-- **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]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+---
 
-[More Information Needed]
+## 使用方法
+
+### 以下は、elyza-tasks-100-TV-0.jsonlの回答のためのコードです
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import json
+from tqdm import tqdm
+import os
+import re   
+
+# 環境変数の設定
+HF_TOKEN = os.getenv("HF_TOKEN")
+model_name = "masakiai/llm-jp-gemma-2-9b-finetune"
+ELYZA_TASKS_100_TV_0_JSONL_PATH = "./elyza-tasks-100-TV_0.jsonl"
+
+# 8ビット量子化の設定
+bnb_config = BitsAndBytesConfig(
+    load_in_8bit=True
+)
+# モデルの読み込み
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+
+# トークナイザーの読み込み
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+# データセットの読み込み
+datasets = []
+with open(ELYZA_TASKS_100_TV_0_JSONL_PATH , "r") as f:
+    item = ""
+    for line in f:
+        line = line.strip()
+        item += line
+        if item.endswith("}"):
+            datasets.append(json.loads(item))
+            item = ""
+
+# 推論の実行
+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})
+
+
+# ファイルの保存
+jsonl_id = re.sub(".*/", "", model_name)
+with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f:
+    for result in results:
+        json.dump(result, f, ensure_ascii=False)
+        f.write('\n')
+```
+
+### 直接的な使用
+
+このモデルは以下のような日本語タスクに使用できます:
+- テキスト生成
+- 質問応答
+- 翻訳
+- 要約
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+model_name = "masakiai/gemma-2-9b-finetune"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+text = "日本の文化について教えてください。"
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+output = model.generate(input_ids, max_length=50)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+---