dyck-deepseek-r1-lora

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

model_id = "akashdutta1030/dyck-deepseek-r1-lora"
base_id = "unsloth/DeepSeek-R1-Distill-Qwen-1.5B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, model_id)
model.eval()

prompt = """Complete the following Dyck language sequence by adding the minimal necessary closing brackets.

Sequence: <[{( 

Rules:
- Add only the closing brackets needed to match all unmatched opening brackets
- Do not add any extra bracket pairs beyond what is required

Provide only the complete valid sequence."""

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=2048).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.1,
    pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)  # e.g. ")}>]"