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README.md
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library_name: transformers
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<!-- Provide a quick summary of what the model is/does. -->
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## Model
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<!-- Provide a longer summary of what this model is. -->
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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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<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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### Direct Use
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### Downstream Use [optional]
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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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[More Information Needed]
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language:
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- en
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- jp
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- de
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- fr
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- it
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- pt
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- hi
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- es
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- th
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library_name: transformers
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license: llama3.2
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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tags:
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- facebook
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- meta
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- pytorch
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- llama
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- llama-3
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pipeline_tag: text-generation
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# AXCXEPT/EZO-Llama-3.2-3B-Instruct-dpoE
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<!-- Provide a quick summary of what the model is/does. -->
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/VXfxBWOGMiAsQIOM0GGFE.png)
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## モデル情報 / Model Information
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このモデルは、Meta AI の Llama 3.2 をベースに、日本語タスクでの性能を向上させるためにファインチューニングを行ったものです。
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ベースとなるLlama-3.2-3B-Instructをもとに、日英のデータセットで性能向上を行いました。
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## 法的通知 / Legal Notice
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This model is subject to the Llama 3.2 Community License Agreement. For detailed information, please refer to the official Llama license page: [Llama 3.2 License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
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このモデルは Llama 3.2 Community License Agreement に従います。詳細については、Llama の公式ライセンスページをご参照ください。
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## 使用方法 / Usage
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```python
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import torch
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from transformers import pipeline
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model_id = "AXCXEPT/EZO-Llama-3.2-3B-Instruct-dpoE"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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## ベンチマーク結果 / Benchmark Results
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/mxHX0fSlGrbRo1s-oTktP.png)
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### [Disclaimer]
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このモデルは研究開発のみを目的として提供されるものであり、実験的なプロトタイプとみなされるべきモデルです。
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商業的な使用やミッションクリティカルな環境への配備を意図したものではありません。
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本モデルの使用は、使用者の責任において行われるものとし、その性能および結果は保証されません。
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Axcxept株式会社は、直接的、間接的、特別、偶発的、結果的な損害、または本モデルの使用から生じるいかなる損失に対しても、得られた結果にかかわらず、一切の責任を負いません。
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利用者は、本モデルの使用に伴うリスクを十分に理解し、自己の判断で使用するものとします。
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### [Hardware]
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H100 × 8(Running in 3h)
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## クレジット / Credits
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This model is based on Meta AI's Llama 3.2. We acknowledge and thank the Meta AI team for their work on the base model.
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このモデルは Meta AI の Llama 3.2 をベースにしています。ベースモデルの開発に携わった Meta AI チームに感謝と尊敬の意を表します。
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