README.md

---
library_name: transformers
tags:
- nlp
- phi
- phi-2
- instruct
license: mit
datasets:
- Open-Orca/SlimOrca
- prince-canuma/TinyOrca
language:
- en
---

# Model Summary

<!-- Provide a quick summary of what the model is/does. -->
This model is a instruction-tuned version of Phi-2, a Transformer model with 2.7 billion parameters from Microsoft. 
The model has undergone further training to better follow specific user instructions, enhancing its ability to perform tasks as directed and improve its interaction with users. 
This additional training helps the model to understand context better, generate more accurate and relevant responses, and adapt to a wide range of language-based tasks such as:
- Questions and Answers,
- Data Extraction,
- Structured Outputs (i.e., JSON outputs),
- And providing explanations,

## 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:** [Prince Canuma](https://huggingface.co/prince-canuma)
- **Model type:** Transformer
- **License:** MIT
- **Finetuned from model:** [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)



## 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]


### 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.

```python
from transformers import pipeline, Conversation

chatbot = pipeline("conversational", model="prince-canuma/Damysus-2.7B-Chat")
conversation = Conversation("I'm looking for a movie - what's your favourite one?")
output = chatbot(conversation)

print(output)
```
Output:
```shell
Conversation id: 5dad71bd-a24a-425a-80aa-95f56924f8c7

user: I'm looking for a movie - what's your favourite one?

assistant:
  My favorite movie is "The Shawshank Redemption."

  It's a powerful and inspiring story about hope, friendship, and redemption.
  The performances by Tim Robbins and Morgan Freeman are exceptional,
  and the film's themes and messages are timeless.

  I highly recommend it to anyone who enjoys a well-crafted and emotionally engaging story.
```

Or you can instatiate the model and tokenizer directly
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prince-canuma/Damysus-2.7B-Chat")
model = AutoModelForCausalLM.from_pretrained("prince-canuma/Damysus-2.7B-Chat")

inputs = tokenizer.apply_chat_template(
    [
        {"content":"","role":"system"},
        {"content":"""I'm looking for a movie - what's your favourite one?""","role":"user"},
    ], add_generation_prompt=True, return_tensors="pt",
).to("cuda")

outputs = model.generate(inputs, do_sample=False, max_new_tokens=256)

input_length = inputs.shape[1]
print(tokenizer.batch_decode(outputs[:, input_length:], skip_special_tokens=True)[0])
```
Output:
```shell
My favorite movie is "The Shawshank Redemption."

It's a powerful and inspiring story about hope, friendship, and redemption.
The performances by Tim Robbins and Morgan Freeman are exceptional,
and the film's themes and messages are timeless.

I highly recommend it to anyone who enjoys a well-crafted and emotionally engaging story.
```



## 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. -->
I used [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) dataset, a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of the [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca), while only including ~500k GPT-4 completions.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
[TODO]

#### Preprocessing

1. Convert dataset to chatML format
2. Remove all samples with more than 2048 tokens (Phi-2 context size)
3. Mask instructions (System and User) at training time.



#### Training Hyperparameters

  - **Training regime:** bf16 mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->


## 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. -->

[TODO]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[TODO]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[TODO]

### Results

[TODO]

## Limitations of Phi-2
This model inherits some of the base model's limitations, such as:
- Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
- Limited Scope for code: Majority of Phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
- Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.

## Technical Specifications

### Compute Infrastructure

- Modal Labs

#### Hardware

- OS: Linux
- GPU: A10G

#### Libraries

- TRL
- Transformers
- PEFT
- Datasets
- Accelerate
- torch
- Wandb
- Bitsandbytes
- Plotly

## Citation 

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```bibtex
@misc{Damysus-2.7B-Chat,
      title={Damysus-2.7B-Chat} , 
      author={Prince Canuma},
      year={2024},
}
```