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license: mit
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---
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---
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license: mit
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---
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+
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# Pythia 12B SFT
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<!-- Provide a quick summary of what the model is/does. -->
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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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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- **Developed by:** [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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Trainining data includes 2023-02-10 openassistant unfiltered conversation tree dump
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## Training Procedure
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```
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deepspeed trainer_sft.py --configs defaults pythia-80 --deepspeed
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```
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### Preprocessing [optional]
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[More Information Needed]
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### Training Hyperparameters
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deepspeed stage 2
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config are as follows:
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```
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defaults:
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learning_rate: 1e-5
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gradient_checkpointing: false
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gradient_accumulation_steps: 32
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per_device_train_batch_size: 2
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per_device_eval_batch_size: 2
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weight_decay: 0.00
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warmup_steps: 600
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eval_steps: 250
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save_steps: 250
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max_length: 512
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num_train_epochs: 2
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logging_steps: 10
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max_grad_norm: 2.0
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save_total_limit: 4
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fp16: true
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eval_accumulation_steps:
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freeze_layer:
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datasets:
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- gsm8k_hard
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- webgpt
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- squad_v2
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- adversarial_qa
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- private_tuning
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- oa_translated
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- prosocial_dialogue
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- math_qa
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- wikihow
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- joke
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- gsm8k
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- ted_trans_en-hi
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- ted_trans_de-ja
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- ted_trans_nl-en
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- ted_trans_en-ja
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- ted_trans_en-es
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- ted_trans_en-ms
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- xsum:
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fraction: 0.5
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- cnn_dailymail:
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fraction: 0.5
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- multi_news:
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fraction: 0.5
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- tldr_news:
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fraction: 0.5
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- scitldr:
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fraction: 0.5
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- samsum:
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fraction: 0.5
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- debate_sum:
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fraction: 0.5
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- billsum:
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fraction: 0.5
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- wmt2019_zh-en:
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fraction: 0.9
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- wmt2019_ru-en:
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fraction: 0.9
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- wmt2019_de-en:
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fraction: 0.9
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- wmt2019_fr-de:
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fraction: 0.9
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- essay_instruction
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- reddit_eli5
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- reddit_askh
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- reddit_asks
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loss_fn: CrossEntropyLoss
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log_dir: "base"
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quantization: false
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seq2seqmodel: false
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poly_eps: 1.0
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fuse_gelu: true
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log_wandb: true
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samples_mixing: true # uses collator that mixes samples in the batch to create a single sample with possible multiple tasks within
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verbose: false
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pythia-80:
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learning_rate: 5e-6
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model_name: EleutherAI/pythia-12b-deduped
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weight_decay: 0.01
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max_length: 520
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warmup_steps: 1000
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gradient_checkpointing: false
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gradient_accumulation_steps: 20
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per_device_train_batch_size: 6
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per_device_eval_batch_size: 6
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```
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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 Data 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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# Technical Specifications [optional]
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## Model Architecture and Objective
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Pythia 12B deduppped model
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## Compute Infrastructure
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Stability AWS Slurm Cluster
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### Hardware
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8 x A100 80G
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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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