Model Summary
EpistemeAI/Iceball-Phi-3.5-mini-instruct-shareGPT-v1.0 is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family and supports 128K token context length. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
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EpistemeAI/Iceball-Phi-3.5-mini-instruct-shareGPT-v1.0 - Phi-3.5: [mini-instruct]; [MoE-instruct] ; [vision-instruct]
Intended Uses
Primary Use Cases
The model is intended for commercial and research use in multiple languages. The model provides uses for general purpose AI systems and applications which require:
- Memory/compute constrained environments
- Latency bound scenarios
- Strong reasoning (especially code, math and logic)
Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.
Usage
Requirements
Phi-3 family has been integrated in the 4.43.0 version of transformers. The current transformers version can be verified with: pip list | grep transformers.
Examples of required packages:
flash_attn==2.5.8
torch==2.3.1
accelerate==0.31.0
transformers==4.43.0
Phi-3.5-mini-instruct is also available in Azure AI Studio
Tokenizer
Phi-3.5-mini-Instruct supports a vocabulary size of up to 32064 tokens. The tokenizer files already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.
Input Formats
Given the nature of the training data, the Phi-3.5-mini-instruct model is best suited for prompts using the chat format as follows:
<|system|>
You are a helpful assistant.<|end|>
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>
Loading the model locally
After obtaining the Phi-3.5-mini-instruct model checkpoint, users can use this sample code for inference.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"EpistemeAI/Iceball-Phi-3.5-mini-instruct-shareGPT-v1.0",
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("EpistemeAI/Iceball-Phi-3.5-mini-instruct-shareGPT-v1.0")
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'])
Notes: If you want to use flash attention, call AutoModelForCausalLM.from_pretrained() with attn_implementation="flash_attention_2"
Uploaded model
- Developed by: EpistemeAI
- License: apache-2.0
- Finetuned from model : unsloth/phi-3.5-mini-instruct-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.