Model Card for Model ID
This model can be used to convert Japanese IPA back to normal text.
Model
Usage
For vLLM use this: Respair/Test_QwJP
in the terminal:
python -m vllm.entrypoints.openai.api_server --model Respair/Japanese_Phoneme_to_Grapheme_LLM--port 8000
now you can simply use it:
# pip install vllm
from openai import OpenAI
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
model_name = "Respair/Test_QwJP"
def p2g(param):
chat_response = client.chat.completions.create(
model=model_name,
max_tokens=512,
temperature=0.01,
messages=[
{"role": "user", "content": f"{prompt}"}]
)
return chat_response.choices[0].message.content
prompt= f""" Turn IPA to Japanese: geɴ'iɴ? sonna fɯɯ ni ɕiɽoi hebi no geŋkakɯ ga, omae no ɕɯɯi ni naɴ do mo naɴ do mo aɽawaɽerɯ, kiʔkake na no ka naɴ na no ka? mi ni oboeʔtsɯ no ka? """
result= p2g(prompt)
print(result)
...or simply through HF transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Respair/Japanese_Phoneme_to_Grapheme_LLM",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Respair/Japanese_Phoneme_to_Grapheme_LLM")
tokenizer.pad_token = "<|endoftext|>"
tokenizer.bos_token = "<|endoftext|>"
tokenizer.eos_token = "<|im_end|>"
prompt = "Turn IPA to Japanese: geɴ'iɴ? sonna fɯɯ ni ɕiɽoi hebi no geŋkakɯ ga, omae no ɕɯɯi ni naɴ do mo naɴ do mo aɽawaɽerɯ, kiʔkake na no ka naɴ na no ka? mi ni oboeʔtsɯ no ka?"
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
pad_token_id=tokenizer.pad_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id,
temperature=0.1,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
response
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