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- ---
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- library_name: transformers
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- tags: []
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- ---
 
 
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
 
 
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- <!-- Provide a longer summary of what this model is. -->
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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:** [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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-
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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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-
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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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-
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- ## Training Details
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-
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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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-
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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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- #### 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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+ ```