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- library_name: transformers
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- tags: []
 
 
 
 
 
 
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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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- ## 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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- ### 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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- ### Compute Infrastructure
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- #### Hardware
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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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- **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 [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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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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+ tags:
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+ - japanese
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+ - causal-lm
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+ inference: false
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  ---
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+ # CyberAgentLM3-22B-Chat (CALM3-22B-Chat)
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+ ## Model Description
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+ CyberAgentLM3-22B is a decoder-only language model pre-trained on 2.0 trillion tokens from scratch.
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+ CyberAgentLM3-22B-Chat is a fine-tuned model specialized for dialogue use cases.
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+ ## Usage
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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+ model = AutoModelForCausalLM.from_pretrained("cyberagent/calm3-22b-chat", device_map="auto", torch_dtype="auto")
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+ tokenizer = AutoTokenizer.from_pretrained("cyberagent/calm3-22b-chat")
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+ streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ messages = [
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+ {"role": "system", "content": "あなたは親切なAIアシスタントです。"},
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+ {"role": "user", "content": "AIによって私たちの暮らしはどのように変わりますか?"}
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+ ]
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+ input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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+ output_ids = model.generate(input_ids,
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+ max_new_tokens=1024,
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+ temperature=0.5,
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+ streamer=streamer)
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+ ```
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+ ## Prompt Format
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+ CALM3-22B-Chat uses ChatML as the prompt format.
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+ ```
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+ <|im_start|>system
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+ あなたは親切なAIアシスタントです。<|im_end|>
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+ <|im_start|>user
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+ AIによって私たちの暮らしはどのように変わりますか?<|im_end|>
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+ <|im_start|>assistant
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+ ```
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+ ## Model Details
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ * **Model size**: 22B
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+ * **Context length**: 16384
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+ * **Model type**: Transformer-based Language Model
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+ * **Language(s)**: Japanese, English
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+ * **Developed by**: [CyberAgent, Inc.](https://www.cyberagent.co.jp/)
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+ * **License**: Apache-2.0
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+ ## Author
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+ [Ryosuke Ishigami](https://huggingface.co/rishigami)
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+ ## How to cite
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+ ```tex
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+ @misc{cyberagent-calm3-22b-chat,
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+ title={cyberagent/calm3-22b-chat},
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+ url={https://huggingface.co/cyberagent/calm3-22b-chat},
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+ author={Ryosuke Ishigami},
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+ year={2024},
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+ }
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+ ```