library_name: transformers
license: mit
datasets:
- teknium/OpenHermes-2.5
- HuggingFaceH4/ultrafeedback_binarized
- argilla/distilabel-intel-orca-dpo-pairs
- jondurbin/py-dpo-v0.1
- argilla/distilabel-math-preference-dpo
pipeline_tag: text-generation
Phi-1.5
The language model Phi-1.5 is a Transformer with 1.3 billion parameters. It was trained using the same data sources as phi-1, augmented with a new data source that consists of various NLP synthetic texts. When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-1.5 demonstrates a nearly state-of-the-art performance among models with less than 10 billion parameters.
Phi-1_5-Instruct-v0.1
The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for instruction following. I used the trl library and a single A100 40GB GPU during both the SFT and DPO steps.
Supervised Fine-Tuning
- Used 128,000 instruction, response pairs from the teknium/OpenHermes-2.5 dataset
Direct Preference Optimization (DPO)
- Used a combination of the following preference datasets
How to use
Chat Format
Given the nature of the training data, the Phi-1.5 Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow:
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Question?<|im_end|>
<|im_start|>assistant
For example:
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
How to explain Internet for a medieval knight?<|im_end|>
<|im_start|>assistant
where the model generates the text after <|im_start|>assistant
. In case of few-shots prompt, the prompt can be formatted as the following:
Sample inference code
This code snippets show how to get quickly started with running the model on a GPU:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model_id = "rasyosef/Phi-1_5-Instruct-v0.1"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
Note: If you want to use flash attention, call AutoModelForCausalLM.from_pretrained() with attn_implementation="flash_attention_2"