metadata
base_model: Qwen/Qwen2.5-0.5B
library_name: peft
datasets:
- heliosbrahma/mental_health_chatbot_dataset
language:
- en
metrics:
- code_eval
pipeline_tag: text-generation
tags:
- medical
- Model perplexity: 1.9
- Loss: 0.6
Use this model with PEFT
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_id = "Qwen/Qwen2.5-0.5B"
lora_adapter_id = "meomeo163/QWEN2.5_chatbot_health_care"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model = PeftModel.from_pretrained(base_model, lora_adapter_id)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
prompt = input("Human: ")
# Mã hóa input và chuyển đến thiết bị phù hợp
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=500) # Adjusted max_new_tokens for shorter response
print(tokenizer.decode(outputs[0], skip_special_tokens=True))