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README.md
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#
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- Provide the basic links for the model. -->
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- **Repository:** [
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- **Paper
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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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##
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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 Data
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###
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[
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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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#### 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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[More Information Needed]
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#### Hardware
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[More Information Needed]
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##
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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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[More Information Needed]
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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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##
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[More Information Needed]
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# EvoLLM-JP
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<!-- Provide a quick summary of what the model is/does. -->
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EvoLLM-JP is a Japanese math LLM merged a Japanese LLM and English math LLMs using evolutionary optimization.
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [Sakana AI](https://sakana.ai/)
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- **Model type:** Autoregressive Language Model
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- **Language(s):** Japanese
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- **License:** [MICROSOFT RESEARCH LICENSE TERMS](./LICENSE)
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- **Base models for merge:**
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- [augmxnt/shisa-gamma-7b-v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1)
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- [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
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- [GAIR/Abel-7B-002](https://huggingface.co/GAIR/Abel-7B-002)
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### Model Sources
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- **Repository:** [SakanaAI/evolving-merged-models](https://github.com/SakanaAI/evolving-merged-models)
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- **Paper:** TODO
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- **Blog:** TODO
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## Usage
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Use the code below to get started with the model.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# 1. load model
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device = "cuda" if torch.cuda.is_available() else "CPU"
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repo_id = "SakanaAI/EvoLLM-JP"
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model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained(repo_id)
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model.to(device)
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# 2. prepare inputs
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template = """以下に、あるタスクを説明する指示があります。リクエストを適切に完了するための回答を日本語で記述してください。一歩一歩考えましょう。
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### 指示:
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{input}
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### 応答:"""
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text = "ミシュカは半ズボンを3本、長ズボンを3本、靴を3足買いました。半ズボンは1本$16.50でした。長ズボンは1本$22.50で、靴は1足$42でした。すべての衣類にいくら使いましたか?"
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inputs = tokenizer(template.format(input=text), return_tensors="pt")
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# 3. generate
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output_ids = model.generate(**inputs.to(device))
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output_ids = output_ids[:, inputs.input_ids.shape[1] :]
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generated_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
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print(generated_text)
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```
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## Evaluation
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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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### 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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## Citation
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```bibtex
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```
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