Phi-4-ct2-AWQ

Ctranslate2-based version of Phi-4

1. First converted to AWQ format using the cosmopedia-100k dataset for calibration.
2. Converted to Ctranslate2-compatible format

For inference, the main difference is that you do not use the "compute_type" parameter.

Example Usage

<details><summary>Non-Streaming Example:</summary>

import ctranslate2
from transformers import AutoTokenizer

def generate_response(prompt: str, system_message: str, model_path: str) -> str:
    generator = ctranslate2.Generator(
        model_path,
        device="cuda",
    )
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    formatted_prompt = f"""<|im_start|>system<|im_sep|>{system_message}<|im_end|>
<|im_start|>user<|im_sep|>{prompt}<|im_end|>
<|im_start|>assistant<|im_sep|>"""
    tokens = tokenizer.tokenize(formatted_prompt)
    results = generator.generate_batch(
        [tokens],
        max_length=1024,
        sampling_temperature=0.7,
        include_prompt_in_result=False
    )
    response = tokenizer.decode(results[0].sequences_ids[0], skip_special_tokens=True)
    return response

if __name__ == "__main__":
    model_path = "path/to/your/phi-4-ct2-model"
    system_message = "You are a helpful AI assistant."
    user_prompt = "Write a short poem about a cat."
    response = generate_response(user_prompt, system_message, model_path)
    print("\nGenerated response:")
    print(response)

</details> <details><summary>Streaming Example:</summary>

import ctranslate2
from transformers import AutoTokenizer
import sys

def generate_response(prompt: str, system_message: str, model_path: str) -> None:
    generator = ctranslate2.Generator(model_path, device="cuda")
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    formatted_prompt = f"""<|im_start|>system<|im_sep|>{system_message}<|im_end|>
<|im_start|>user<|im_sep|>{prompt}<|im_end|>
<|im_start|>assistant<|im_sep|>"""
    tokens: List[str] = tokenizer.tokenize(formatted_prompt)
    for step in generator.generate_tokens([tokens], max_length=1024, sampling_temperature=0.7):
        token: str = step.token
        if token in tokenizer.eos_token or token in tokenizer.all_special_tokens:
            break
        decoded_token: str = tokenizer.decode([step.token_id])
        print(decoded_token, end="", flush=True)

if __name__ == "__main__":
    model_path = "path/to/your/phi-4-ct2-model"
    system_message = "You are a helpful AI assistant."
    user_prompt = "Write a short poem about a cat."
    print("\nGenerating response:")
    generate_response(user_prompt, system_message, model_path)

</details>