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  library_name: transformers
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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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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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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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- ## 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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- - **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 [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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+ language:
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+ - en
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+ license: mit
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  library_name: transformers
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+ tags:
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+ - chat
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+ - text-generation
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+ - persona
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+ - phi-2
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+ - llm
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+ - persona-grounded
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+ datasets:
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+ - nazlicanto/persona-based-chat
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  ---
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+ ## Phi 2 Persona-Chat
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+ Phi 2 Persona-Chat is a LoRA fine-tuned version of the base [Phi 2](https://huggingface.co/microsoft/phi-2) model using the [nazlicanto/persona-based-chat](https://huggingface.co/datasets/nazlicanto/persona-based-chat) dataset. This dataset consists of over 64k conversations between *Persona A* and *Persona B*, for which a list of persona facts are provided.
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+ The model is trained using Supervised Fine-tuning Trainer using the `reference` responses as target outputs. For the training and inference code and the full list of dependencies, you can refer to the Github [repo](https://github.com/alaradirik/finetune-phi-2).
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+ ## Running the Model
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+ Please note that, at the moment, trust_remote_code=True is required for running the Phi 2 model. For best results, use a prompt that includes the persona facts, followed by a minimum of one conversational turn.
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+ ```
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+ from random import randrange
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+ import torch
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+ from datasets import load_dataset
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ prompt = f"""
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+ Person B has the following Persona information.
 
 
 
 
 
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+ Persona of Person B: My name is David and I'm a 35 year old math teacher.
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+ Persona of Person B: I like to hike and spend time in the nature.
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+ Persona of Person B: I'm married with two kids.
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+ Instruct: Person A and Person B are now having a conversation. Following the conversation below, write a response that Person B would say base on the above Persona information. Please carefully consider the flow and context of the conversation below, and use the Person B's Persona information appropriately to generate a response that you think are the most appropriate replying for Person B.
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+ Persona A: Morning! I think I saw you at the parent meeting, what's your name?
 
 
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+ Output:
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+ """
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+ # load base LLM model, LoRA params and tokenizer
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+ model = AutoModelForCausalLM.from_pretrained("nazlicanto/phi-2-persona-chat", trust_remote_code=True)
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+ model.to("cuda")
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+ tokenizer = AutoTokenizer.from_pretrained("nazlicanto/phi-2-persona-chat", trust_remote_code=True)
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+ # tokenize input prompt
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+ input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
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+ # inference
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+ with torch.inference_mode():
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+ outputs = model.generate(
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+ input_ids=input_ids,
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+ max_new_tokens=50,
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+ do_sample=True,
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+ top_p=0.1,
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+ temperature=0.7
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+ )
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+ # decode output tokens
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+ outputs = outputs.detach().cpu().numpy()
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+ outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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+ output = outputs[0][len(prompt):]
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+ print(output)
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+ ```
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+ This model is trained by [nazlicanto](https://huggingface.co/nazlicanto) and [adirik](https://huggingface.co/adirik).