Model Card for Model ID
Model Details
Model Description
- Developed by: hack337
- Model type: qwen2
- Finetuned from model: Qwen/Qwen2.5-3B-Instruct
Model Sources [optional]
- Repository: https://huggingface.co/Hack337/WavGPT-1.5
- Demo (WavGPT-1.0): https://huggingface.co/spaces/Hack337/WavGPT
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Hack337/WavGPT-1.5",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Hack337/WavGPT-1.5")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "Вы очень полезный помощник."},
{"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,
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]
Use the code below to get started with the model using NPU.
from transformers import AutoTokenizer, TextStreamer
from intel_npu_acceleration_library import NPUModelForCausalLM
import torch
# Load the NPU-optimized model without LoRA
model = NPUModelForCausalLM.from_pretrained(
"Hack337/WavGPT-1.5",
use_cache=True,
dtype=torch.float16 # Use float16 for the NPU
).eval()
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("Hack337/WavGPT-1.5")
tokenizer.pad_token_id = tokenizer.eos_token_id
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Prompt handling
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "Вы очень полезный помощник."},
{"role": "user", "content": prompt}
]
# Convert to a text format compatible with the model
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
prefix = tokenizer([text], return_tensors="pt")["input_ids"].to("npu")
# Generation configuration
generation_kwargs = dict(
input_ids=prefix,
streamer=streamer,
do_sample=True,
top_k=50,
top_p=0.9,
max_new_tokens=512,
)
# Run inference on the NPU
print("Run inference")
_ = model.generate(**generation_kwargs)
- PEFT 0.11.1