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- library_name: peft
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- base_model: microsoft/phi-1_5
 
 
 
 
 
 
 
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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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- - **Developed by:** [More Information Needed]
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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:** [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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- <!-- 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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- [More Information Needed]
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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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- #### 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 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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- #### 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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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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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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- **APA:**
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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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ## Training procedure
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  - PEFT 0.6.2
 
 
 
 
 
 
 
 
 
 
 
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+ license: apache-2.0
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+ language:
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+ - en
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+ pipeline_tag: summarization
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+ widget:
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+ - text: What is the peak phase of T-eV?
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+ example_title: Question Answering
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+ tags:
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+ - arxiv
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  ---
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+ # Table of Contents
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+ 0. [TL;DR](#TL;DR)
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+ 1. [Model Details](#model-details)
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+ 2. [Usage](#usage)
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+ 3. [Uses](#uses)
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+ 4. [Citation](#citation)
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+ # TL;DR
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+ This is a Phi-1_5 model trained on [camel-ai/biology](https://huggingface.co/datasets/camel-ai/physics). This model is for research purposes only and ***should not be used in production settings***.
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+ ## Model Description
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+ - **Model type:** Language model
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+ - **Language(s) (NLP):** English
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+ - **License:** Apache 2.0
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+ - **Related Models:** [Phi-1_5](https://huggingface.co/microsoft/phi-1_5)
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+ # Usage
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+ Find below some example scripts on how to use the model in `transformers`:
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+ ## Using the Pytorch model
 
 
 
 
 
 
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+ ```python
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+ from huggingface_hub import notebook_login
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+ from datasets import load_dataset, Dataset
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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+ model = "ArtifactAI/phi-physics"
 
 
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+ model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code= True)
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+ tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
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+ def generate(prompt):
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+ inputs = tokenizer(f'''Below is an instruction that describes a task. Write a response that appropriately completes the request If you are adding additional white spaces, stop writing".\n\n### Instruction:\n{prompt}.\n\n### Response:\n ''', return_tensors="pt", return_attention_mask=False)
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+ streamer = TextStreamer(tokenizer, skip_prompt= True)
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+ _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500)
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+
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+ generate("What are the common techniques used in identifying a new species, and how can scientists accurately categorize it within the existing taxonomy system?")
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+ ```
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+ ## Training Data
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+ The model was trained on [camel-ai/phi-physics](https://huggingface.co/datasets/camel-ai/physics), a dataset of question/answer pairs.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training procedure
 
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  - PEFT 0.6.2
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+ # Citation
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+ ```
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+ @misc{phi-math,
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+ title={phi-biology},
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+ author={Matthew Kenney},
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+ year={2023}
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+ }
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+ ```