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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- sft |
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pipeline_tag: text-generation |
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widget: |
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- text: <prefix>You are a helpful assistant model trained by LAION called Aki</prefix><human>Hi, how are you?<bot> |
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- text: <human>What's the Earth total population<bot> |
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- text: <human>你好<bot> |
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- text: <human>안녕하세요<bot> |
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- text: <human>こんにちは<bot> |
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- text: <human>Write a story about future of AI development<bot> |
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--- |
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# Pythia 1.2B SFT model |
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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:** Open Assistant |
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- **Model type:** Pythia |
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- **Language(s) (NLP):** English |
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- **License:** Apache-2.0 |
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## Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [Open Assistant](https://github.com/LAION-AI/Open-Assistant) |
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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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See the example on the right |
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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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[just read pythia](https://huggingface.co/EleutherAI/pythia-12b#out-of-scope-use) |
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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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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "theblackcat102/pythia-1b-deduped-sft" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name).half().eval().cuda() |
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input_text = "<human>What's the earth population?<bot>" |
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inputs = tokenizer(input_text, return_tensors="pt", padding=True).to(0) |
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outputs = model.generate( |
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**inputs, |
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early_stopping=True, |
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max_new_tokens=args.max_new_tokens, |
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do_sample=True, |
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top_k=args.top_k, |
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temperature=args.temperature, |
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pad_token_id=tokenizer.eos_token_id, |
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# dialogue_collator.py line 36 |
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) |
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output = tokenizer.decode(outputs[0], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"]) |
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print(output) |
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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 Data 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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## Training Procedure |
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``` |
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deepspeed trainer_sft.py --configs defaults pythia-1b --deepspeed |
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``` |
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This model was trained for 1000 iterations. |
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### Training Hyperparameters |
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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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cache_dir: /fsx/home-theblackcat02/.cache |
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loss_fn: CrossEntropyLoss |
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eval_size: |
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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-1b: |
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learning_rate: 5e-6 |
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model_name: EleutherAI/pythia-1b-deduped |
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weight_decay: 0.01 |
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max_length: 540 |
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fp16: true |
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warmup_steps: 1000 |
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gradient_accumulation_steps: 20 |
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per_device_train_batch_size: 20 |
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per_device_eval_batch_size: 2 |
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eval_steps: 500 |
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save_steps: 500 |
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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 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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# 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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# Acknowledgements |
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- [LAION](https://laion.ai/) & EleutherAI |
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- [Stability.ai](https://stability.ai/) : this project wouldn't be possible without their compute resource |
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- [Teams and contributors at Open Assistant](https://github.com/LAION-AI/Open-Assistant/graphs/contributors) : who put their time after their day job or whatever into this project |
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- [Huggingface](https://huggingface.co/) : For the storage and spaces here |
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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] |
