Microsoft Phi 4 GPTQ Int4

🚀 Transformers Library

This library is built upon the transformers framework, leveraging the microsoft/phi-4 base model to provide powerful natural language processing capabilities.

🚀 Quick Start

Requirements

pip install -U transformers optimum auto-gptq

Transformers inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
device = "auto"

model_name = "jakiAJK/microsoft-phi-4_GPTQ-int4"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map= device, trust_remote_code= True, torch_dtype= dtype)

model.eval()

chat = [
    { "role": "user", "content": "List any 5 country capitals." },
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

input_tokens = tokenizer(chat, return_tensors="pt").to('cuda')

output = model.generate(**input_tokens, 
                        max_new_tokens=100)

output = tokenizer.batch_decode(output)

print(output)

📦 Installation

To install the necessary dependencies, run the following command:

pip install -U transformers optimum auto-gptq

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
device = "auto"

model_name = "jakiAJK/microsoft-phi-4_GPTQ-int4"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map= device, trust_remote_code= True, torch_dtype= dtype)

model.eval()

chat = [
    { "role": "user", "content": "List any 5 country capitals." },
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

input_tokens = tokenizer(chat, return_tensors="pt").to('cuda')

output = model.generate(**input_tokens, 
                        max_new_tokens=100)

output = tokenizer.batch_decode(output)

print(output)

Advanced Usage

The current example demonstrates basic inference. For more advanced usage, such as fine - tuning the model or integrating it into a larger application, you can refer to the official transformers documentation.