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README.md
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library_name: transformers
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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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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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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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#### Hardware
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[More Information Needed]
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#### Software
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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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## 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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## Model Card Contact
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[More Information Needed]
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license: llama3.2
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language:
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- en
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- ja
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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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pipeline_tag: text-generation
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base_model: meta-llama/Llama-3.2-3B
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datasets:
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- ryota39/izumi-lab-dpo-45k
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- Aratako/Magpie-Tanuki-8B-97k
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- kunishou/databricks-dolly-15k-ja
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- kunishou/oasst1-89k-ja
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tags:
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- llama3.2
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![chibi-img](./chibi.png)
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## Preface
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The importance of a small parameter large language model (LLM) lies in its ability to balance performance and efficiency. As LLMs grow increasingly sophisticated, the trade-off between model size and computational resource demands becomes critical. A smaller parameter model offers significant advantages, such as reduced memory usage, faster inference times, and lower energy consumption, all while retaining a high level of accuracy and contextual understanding. These models are particularly valuable in real-world applications where resources like processing power and storage are limited, such as on mobile devices, edge computing, or low-latency environments.
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## Llama 3.2 Chibi 3B
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This experimental model is the result from continual pre-training of [Meta's Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on a small mixture of japanese datasets.
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## Architecture
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[Llama 3.2 3B](https://huggingface.co/meta-llama/Llama-3.2-3B)
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## Training
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The model has been trained with a following mixture of datasets:
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- [ryota39/izumi-lab-dpo-45k](https://huggingface.co/ryota39/izumi-lab-dpo-45k)
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- [Aratako/Magpie-Tanuki-8B-97k](https://huggingface.co/Aratako/Magpie-Tanuki-8B-97k)
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- [kunishou/databricks-dolly-15k-ja](https://huggingface.co/kunishou/databricks-dolly-15k-ja)
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- [kunishou/oasst1-89k-ja](https://huggingface.co/kunishou/oasst1-89k-ja)
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## Contributors
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- [Hammaam](https://huggingface.co/AELLM)
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## How to use
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Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
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Make sure to update your transformers installation via pip install --upgrade transformers.
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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 = "AELLM/Llama-3.2-Chibi-3B"
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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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pipe("人生の鍵は")
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```
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# License
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Refer to [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
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# References
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```bibtex
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@inproceedings{zheng2024llamafactory,
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title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
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author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma},
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booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
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address={Bangkok, Thailand},
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publisher={Association for Computational Linguistics},
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year={2024},
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url={http://arxiv.org/abs/2403.13372}
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}
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```
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