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--- |
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library_name: transformers |
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license: apache-2.0 |
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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:** [Isaac Chung](https://huggingface.co/isaacchung) |
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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:** [Apache 2.0] |
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- **Finetuned from model [optional]:** [openbmb/MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) |
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<!-- ### Model Sources [optional] --> |
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<!-- Provide the basic links for the model. --> |
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<!-- |
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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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--> |
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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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```python |
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# Load model directly |
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from transformers import AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained("isaacchung/MiniCPM-2B-RAFT-lora-hotpotqa-dev", trust_remote_code=True) |
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``` |
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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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[isaacchung/hotpotqa-dev-raft-subset](https://huggingface.co/datasets/isaacchung/hotpotqa-dev-raft-subset) |
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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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See https://github.com/isaac-chung/MiniCPM/commit/213282b679eb8eb054bb13f02af71b9d71ad3721. |
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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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- train_runtime: 4607.6477 |
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- train_samples_per_second: 5.209 |
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- train_steps_per_second: 0.651 |
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- train_loss: 0.5153028686841329 |
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- epoch: 9.52 |
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#### Training Loss |
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From the last epoch: |
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``` |
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'loss': 0.4504, 'grad_norm': 2.259155507591921, 'learning_rate': 2.7586206896551725e-06, 'epoch': 9.02} |
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{'loss': 0.431, 'grad_norm': 1.7071411656099411, 'learning_rate': 2.586206896551724e-06, 'epoch': 9.05} |
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{'loss': 0.4627, 'grad_norm': 1.7915555416805786, 'learning_rate': 2.413793103448276e-06, 'epoch': 9.08} |
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{'loss': 0.4528, 'grad_norm': 1.9988269942330565, 'learning_rate': 2.2413793103448275e-06, 'epoch': 9.11} |
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{'loss': 0.445, 'grad_norm': 1.8423666856380017, 'learning_rate': 2.0689655172413796e-06, 'epoch': 9.14} |
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{'loss': 0.4424, 'grad_norm': 1.7539963730934427, 'learning_rate': 1.896551724137931e-06, 'epoch': 9.17} |
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{'loss': 0.3817, 'grad_norm': 1.755668315740134, 'learning_rate': 1.724137931034483e-06, 'epoch': 9.21} |
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{'loss': 0.4012, 'grad_norm': 1.8214703589809635, 'learning_rate': 1.5517241379310346e-06, 'epoch': 9.24} |
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{'loss': 0.4567, 'grad_norm': 1.6490771602855827, 'learning_rate': 1.3793103448275862e-06, 'epoch': 9.27} |
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{'loss': 0.491, 'grad_norm': 1.5838108179327266, 'learning_rate': 1.206896551724138e-06, 'epoch': 9.3} |
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{'loss': 0.516, 'grad_norm': 1.7848893180960532, 'learning_rate': 1.0344827586206898e-06, 'epoch': 9.33} |
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{'loss': 0.3674, 'grad_norm': 1.6589815898285354, 'learning_rate': 8.620689655172415e-07, 'epoch': 9.37} |
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{'loss': 0.455, 'grad_norm': 1.6377170040397837, 'learning_rate': 6.896551724137931e-07, 'epoch': 9.4} |
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{'loss': 0.4322, 'grad_norm': 1.7061632686271986, 'learning_rate': 5.172413793103449e-07, 'epoch': 9.43} |
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{'loss': 0.3934, 'grad_norm': 1.784527156508834, 'learning_rate': 3.4482758620689656e-07, 'epoch': 9.46} |
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{'loss': 0.4457, 'grad_norm': 1.5131773700813846, 'learning_rate': 1.7241379310344828e-07, 'epoch': 9.49} |
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{'loss': 0.4026, 'grad_norm': 1.8239453129182908, 'learning_rate': 0.0, 'epoch': 9.52} |
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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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<!-- [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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--> |
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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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- 1x NVIDIA RTX 6000 Ada |
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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 |
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[Isaac Chung](https://huggingface.co/isaacchung) |
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## Model Card Contact |
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[Isaac Chung](https://huggingface.co/isaacchung) |
