metadata

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

Model Summary

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

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
Model type: Transformer
License: MIT
Finetuned from model: microsoft/phi-2

Uses

Direct Use

You can use this model to build local/cloud RAG applications. It can serve as the:

Answer synthesizer,
Summarizer
Or query rewriter model.

Limitations

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.

How to Get Started with the Model

Use the code below to get started with the model.

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)

Or you can instatiate the model and tokenizer directly

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":"You are an helpful AI assistant","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:

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

I used 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, while only including ~500k GPT-4 completions.

Training Procedure

[TODO]

Preprocessing

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

Training Hyperparameters

Training regime: bf16 mixed precision

Evaluation

Testing Data, Factors & Metrics

Testing Data

[TODO]

Factors

[TODO]

Metrics

[TODO]

Results

[TODO]

Technical Specifications

Compute Infrastructure

Modal Labs

Hardware

OS: Linux
GPU: A10G

Libraries

TRL
Transformers
PEFT
Datasets
Accelerate
torch
Wandb
Bitsandbytes
Plotly

Citation

BibTeX:

@misc{Damysus-2.7B-Chat,
      title={Damysus-2.7B-Chat} , 
      author={Prince Canuma},
      year={2024},
}