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README.md
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library_name: transformers
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##
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<!-- Provide a longer summary of what this model is. -->
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[More Information Needed]
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[
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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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[More Information Needed]
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---
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library_name: transformers
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license: apache-2.0
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language:
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- ja
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- en
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---
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## Model Merge
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Gakki-7B was build by [Chat Vector](https://arxiv.org/abs/2310.04799)
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A recipe shows as below
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```
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Rakuten/RakutenAI-7B-instruct + (prometheus-eval/prometheus-7b-v2.0 - mistralai/Mistral-7B-Instruct-v0.2)
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```
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## Source Code
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```python
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import torch
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from transformers import AutoModelForCausalLM
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def build_chat_vector_model(
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base_model_name,
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inst_model_name,
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target_model_name,
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skip_layers,
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):
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base_model = AutoModelForCausalLM.from_pretrained(
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base_model_name,
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torch_dtype=torch.bfloat16,
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device_map="cpu",
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)
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inst_model = AutoModelForCausalLM.from_pretrained(
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inst_model_name,
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torch_dtype=torch.bfloat16,
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device_map="cpu",
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)
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target_model = AutoModelForCausalLM.from_pretrained(
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target_model_name,
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torch_dtype=torch.bfloat16,
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device_map="cuda",
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)
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# 英語ベースモデル
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for k, v in base_model.state_dict().items():
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print(k, v.shape)
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# 日本語継続事前学習モデル
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for k, v in target_model.state_dict().items():
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print(k, v.shape)
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# 除外対象
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skip_layers = ["model.embed_tokens.weight", "lm_head.weight"]
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for k, v in target_model.state_dict().items():
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# layernormも除外
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if (k in skip_layers) or ("layernorm" in k):
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continue
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chat_vector = inst_model.state_dict()[k] - base_model.state_dict()[k]
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new_v = v + chat_vector.to(v.device)
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v.copy_(new_v)
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target_model.save_pretrained("./Gakki-7B")
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return
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if __name__ == '__main__':
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base_model_name = "mistralai/Mistral-7B-Instruct-v0.2"
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inst_model_name = "prometheus-eval/prometheus-7b-v2.0"
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target_model_name = "Rakuten/RakutenAI-7B-instruct"
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skip_layers = ["model.embed_tokens.weight", "lm_head.weight"]
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build_chat_vector_model(
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base_model_name=base_model_name,
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inst_model_name=inst_model_name,
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target_model_name=target_model_name,
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skip_layers=skip_layers
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)
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```
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