nazlicanto
/

phi-2-persona-chat

@@ -1,199 +1,75 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+language:
+- en
+license: mit
 library_name: transformers
+tags:
+- chat
+- text-generation
+- persona
+- phi-2
+- llm
+- persona-grounded
+datasets:
+- nazlicanto/persona-based-chat
 ---
+## Phi 2 Persona-Chat
+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.
+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).
+## Running the Model
+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.
+```
+from random import randrange
+import torch
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM
+prompt = f"""
+Person B has the following Persona information.
+Persona of Person B: My name is David and I'm a 35 year old math teacher.
+Persona of Person B: I like to hike and spend time in the nature.
+Persona of Person B: I'm married with two kids.
+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.
+Persona A: Morning! I think I saw you at the parent meeting, what's your name?
+Output:
+"""
+# load base LLM model, LoRA params and tokenizer
+model = AutoModelForCausalLM.from_pretrained("nazlicanto/phi-2-persona-chat", trust_remote_code=True)
+model.to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("nazlicanto/phi-2-persona-chat", trust_remote_code=True)
+# tokenize input prompt
+input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
+# inference
+with torch.inference_mode():
+    outputs = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=50,
+        do_sample=True,
+        top_p=0.1,
+        temperature=0.7
+    )
+# decode output tokens
+outputs = outputs.detach().cpu().numpy()
+outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+output = outputs[0][len(prompt):]
+print(output)
+```
+This model is trained by [nazlicanto](https://huggingface.co/nazlicanto) and [adirik](https://huggingface.co/adirik).