MiniCPM-2B-RAFT-lora-hotpotqa-dev

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Model Details

Model Description

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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.

• Developed by: Isaac Chung

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• License: [Apache 2.0]
• Finetuned from model [optional]: openbmb/MiniCPM-2B-sft-bf16

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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.

How to Get Started with the Model

Use the code below to get started with the model.

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("isaacchung/MiniCPM-2B-RAFT-lora-hotpotqa-dev", trust_remote_code=True)

Training Details

Training Data

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isaacchung/hotpotqa-dev-raft-subset

Training Procedure

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Training Hyperparameters

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See https://github.com/isaac-chung/MiniCPM/commit/213282b679eb8eb054bb13f02af71b9d71ad3721.

Speeds, Sizes, Times [optional]

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• train_runtime: 4607.6477
• train_samples_per_second: 5.209
• train_steps_per_second: 0.651
• train_loss: 0.5153028686841329
• epoch: 9.52

Training Loss

From the last epoch:

'loss': 0.4504, 'grad_norm': 2.259155507591921, 'learning_rate': 2.7586206896551725e-06, 'epoch': 9.02}                                   
{'loss': 0.431, 'grad_norm': 1.7071411656099411, 'learning_rate': 2.586206896551724e-06, 'epoch': 9.05}                                    
{'loss': 0.4627, 'grad_norm': 1.7915555416805786, 'learning_rate': 2.413793103448276e-06, 'epoch': 9.08}                                   
{'loss': 0.4528, 'grad_norm': 1.9988269942330565, 'learning_rate': 2.2413793103448275e-06, 'epoch': 9.11}                                  
{'loss': 0.445, 'grad_norm': 1.8423666856380017, 'learning_rate': 2.0689655172413796e-06, 'epoch': 9.14}                                   
{'loss': 0.4424, 'grad_norm': 1.7539963730934427, 'learning_rate': 1.896551724137931e-06, 'epoch': 9.17}                                   
{'loss': 0.3817, 'grad_norm': 1.755668315740134, 'learning_rate': 1.724137931034483e-06, 'epoch': 9.21}                                    
{'loss': 0.4012, 'grad_norm': 1.8214703589809635, 'learning_rate': 1.5517241379310346e-06, 'epoch': 9.24}                                  
{'loss': 0.4567, 'grad_norm': 1.6490771602855827, 'learning_rate': 1.3793103448275862e-06, 'epoch': 9.27}                                  
{'loss': 0.491, 'grad_norm': 1.5838108179327266, 'learning_rate': 1.206896551724138e-06, 'epoch': 9.3}                                     
{'loss': 0.516, 'grad_norm': 1.7848893180960532, 'learning_rate': 1.0344827586206898e-06, 'epoch': 9.33}                                   
{'loss': 0.3674, 'grad_norm': 1.6589815898285354, 'learning_rate': 8.620689655172415e-07, 'epoch': 9.37}                                   
{'loss': 0.455, 'grad_norm': 1.6377170040397837, 'learning_rate': 6.896551724137931e-07, 'epoch': 9.4}                                     
{'loss': 0.4322, 'grad_norm': 1.7061632686271986, 'learning_rate': 5.172413793103449e-07, 'epoch': 9.43}                                   
{'loss': 0.3934, 'grad_norm': 1.784527156508834, 'learning_rate': 3.4482758620689656e-07, 'epoch': 9.46}                                   
{'loss': 0.4457, 'grad_norm': 1.5131773700813846, 'learning_rate': 1.7241379310344828e-07, 'epoch': 9.49}                                  
{'loss': 0.4026, 'grad_norm': 1.8239453129182908, 'learning_rate': 0.0, 'epoch': 9.52}

<!-- ## Evaluation --> <!-- This section describes the evaluation protocols and provides the results. --> <!-- ### Testing Data, Factors & Metrics --> <!-- #### Testing Data --> <!-- This should link to a Dataset Card if possible. --> <!-- [More Information Needed] --> <!-- #### Factors --> <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> <!-- [More Information Needed] --> <!-- #### Metrics --> <!-- These are the evaluation metrics being used, ideally with a description of why. --> <!-- [More Information Needed] --> <!-- ### Results --> <!-- [More Information Needed] --> <!-- #### Summary --> <!-- ## Model Examination [optional] --> <!-- Relevant interpretability work for the model goes here --> <!-- [More Information Needed] --> <!-- ## Environmental Impact --> <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> <!-- 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). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] -->

Technical Specifications [optional]

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Compute Infrastructure

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Hardware

• 1x NVIDIA RTX 6000 Ada

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Model Card Authors

Isaac Chung

Model Card Contact

Isaac Chung