llama-8b-chineseAMR-multitask

Model fine-tuned with morph resolution and morph explanation

Example

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model and tokenizer
base_model_path = "meta-llama/Llama-3.1-8B"  # or your local path
adapter_path = "dawang123/llama-8b-chineseAMR-multitask"  

tokenizer = AutoTokenizer.from_pretrained(base_model_path)
model = AutoModelForCausalLM.from_pretrained(
    base_model_path,
    device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter_path, device_map="auto")

# Input text with variant words
input_text = "小糖人都是可以吃的。"  

# Instruction for the task
instruction = "请将以下句子中的变体词进行还原。注意，只修改变体词，其他内容（包括可能的语音识别错误）保持不变。"

# Format as chat template
messages = [
    {"role": "user", "content": f"{instruction}\n{input_text}"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)

# Decode and clean response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)


print("Input:", input_text)
print("Corrected output:", generated_text)