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# Model Card for Model ID
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###
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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[More Information Needed]
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### Training Procedure
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<!-- 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]
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#### 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. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[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. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## 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. -->
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[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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language:
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- ja
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tags:
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- text-generation
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- LoRA
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- Transformers
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license: apache-2.0
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base_model:
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- llm-jp/llm-jp-3-13b
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datasets:
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- ichikara-instruction-003-001-1
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model-index:
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- name: llm-jp-3-13b-finetune
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results: []
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---
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# CoC0A/llm-jp-3-13b-finetune
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## 1. モデル概要
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このモデルは 大規模言語モデル講座の最終課題のために、ELYZA-tasks-100-TVを解くことを目的として作成されました。
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学習データには “ichikara-instruction-003-001-1.json” を使用し、Hugging FaceのTransformersライブラリを活用しています。
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### 特徴
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- **タスク**: テキスト生成
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- **使用データセット**: ichikara-instruction-003-001-1.json
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- **出力形式**: JSON Lines形式(jsonl)
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---
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## 2. 使用方法
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このモデルをロードして推論するには、以下のコードを使います。
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```
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# 必要なライブラリをインストール
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%%capture
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!pip install unsloth
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!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
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!pip install -U torch
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!pip install -U peft
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# 必要なライブラリを読み込み
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from unsloth import FastLanguageModel
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from peft import PeftModel
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import torch
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import json
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from tqdm import tqdm
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import re
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# ベースとなるモデルと学習したLoRAのアダプタ(Hugging FaceのIDを指定)。
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model_id = "llm-jp/llm-jp-3-13b"
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adapter_id = "CoC0A/llm-jp-3-13b-finetune"
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# Hugging Face Token を指定。
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# 下記の URL から Hugging Face Token を取得できますので下記の HF_TOKEN に入れてください。
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# https://huggingface.co/settings/tokens
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HF_TOKEN = "" #@param {type:"string"}
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# unslothのFastLanguageModelで元のモデルをロード。
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dtype = None # Noneにしておけば自動で設定
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load_in_4bit = True # 今回は13Bモデルを扱うためTrue
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name=model_id,
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dtype=dtype,
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load_in_4bit=load_in_4bit,
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trust_remote_code=True,
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)
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# 元のモデルにLoRAのアダプタを統合。
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model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
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# タスクとなるデータの読み込み。
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# 事前にデータをアップロードしてください。
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datasets = []
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#データのパスは個人の環境に合わせて適宜変更してください。
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with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
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item = ""
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for line in f:
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line = line.strip()
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item += line
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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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# モデルを用いてタスクの推論。
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# 推論するためにモデルのモードを変更
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FastLanguageModel.for_inference(model)
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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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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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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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# 結果をjsonlで保存。
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# ここではadapter_idを元にファイル名を決定しているが、ファイル名は任意で問題なし。
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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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```
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### 参考文献
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このREADMEは大規模言語モデル講座、最終課題におけるサンプルコードを参考にしました。
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