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--- |
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license: cc-by-nc-4.0 |
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datasets: |
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- yahma/alpaca-cleaned |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- llama-2 |
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- alpaca |
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--- |
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# Model Card for Llama-2-7b-alpaca-cleaned |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model checkpoint is the Llama-2-7b fine-tuned on [alpaca-cleaned dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned) with the original Alpaca fine-tuning hyper-parameters. |
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## Model Details |
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### Model Description |
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This model checkpoint is the Llama-2-7b fine-tuned on [alpaca-cleaned dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned) with the original Alpaca fine-tuning hyper-parameters. \ |
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The original Alpaca model is fine-tuned on Llama with the alpaca dataset by researchers from Stanford University |
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- **Developed by:** NEU Human-centered AI Lab |
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- **Shared by [optional]:** NEU Human-centered AI Lab |
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- **Model type:** Text-generation |
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- **Language(s) (NLP):** English |
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- **License:** cc-by-nc-4.0 (comply with the alpaca-cleaned dataset) |
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- **Finetuned from model [optional]:** [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://huggingface.co/meta-llama/Llama-2-7b |
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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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The model is intended to be used for research purposes only in English, complying with [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca). \ |
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The model has been fine-tuned on the [alpaca-cleaned dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned) for assistant-like chat and general natural language generation tasks. \ |
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The use of this model should also comply with the restrictions from [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b). |
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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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The out-of-Scope use of this model should also comply with [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca) and [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b). |
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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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{{ bias_risks_limitations | default("[More Information Needed]", true)}} |
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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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``` |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("NEU-HAI/Llama-2-7b-alpaca-cleaned") |
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model = AutoModelForCausalLM.from_pretrained("NEU-HAI/Llama-2-7b-alpaca-cleaned") |
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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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We use the [alpaca-cleaned dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is the cleaned version of the original [alpaca dataset](https://huggingface.co/datasets/tatsu-lab/alpaca) created by researchers from Stanford University. |
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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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We follow the same training procedure and mostly same hyper-parameters to fine-tune the original Alpaca model on Llama. The procedure can be found in [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca). |
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#### Training Hyperparameters |
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``` |
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--bf16 True \ |
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--num_train_epochs 3 \ |
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--per_device_train_batch_size 4 \ |
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--per_device_eval_batch_size 4 \ |
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--gradient_accumulation_steps 8 \ |
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--evaluation_strategy "no" \ |
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--save_strategy "steps" \ |
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--save_steps 2000 \ |
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--save_total_limit 1 \ |
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--learning_rate 2e-5 \ |
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--weight_decay 0. \ |
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--warmup_ratio 0.03 \ |
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--lr_scheduler_type "cosine" \ |
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--logging_steps 1 \ |
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--fsdp "full_shard auto_wrap" \ |
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--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \ |
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--tf32 True |
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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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N/A |
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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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N/A |
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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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N/A |
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### Results |
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N/A |
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#### Summary |
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N/A |
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<!-- |
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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:** {{ hardware | default("[More Information Needed]", true)}} |
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- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}} |
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- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}} |
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- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}} |
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- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}} |
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--> |
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## Citation |
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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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Please cite the [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca) |
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``` |
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@misc{alpaca, |
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author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, |
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title = {Stanford Alpaca: An Instruction-following LLaMA model}, |
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year = {2023}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, |
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} |
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``` |
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## Model Card Authors |
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Northeastern Human-centered AI Lab |
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## Model Card Contact |
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