---
base_model: Qwen/Qwen2-0.5B-Instruct
dataset: timdettmers/openassistant-guanaco
library_name: peft
---

# Model Card for Model ID

Qwen2-0.5B fine-tuned with qlora using the openassistant-guanaco dataset


# quickstart
In google colab on the free-tier GPU


```python

!pip install transformers accelerate bitsandbytes peft

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

config = PeftConfig.from_pretrained("SeppeHousen/qwen-0.5B-qlora-guanaco")
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "SeppeHousen/qwen-0.5B-qlora-guanaco")
tokenizer = AutoTokenizer.from_pretrained("SeppeHousen/qwen-0.5B-qlora-guanaco")

messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who is always happy to help",
    },
    {"role": "user", "content": "Arrr, tell me what the weather be like today!"},
]


input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt").to('cuda')
model.to('cuda')


outputs = model.generate(
        input_ids=input_ids,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.7,
        top_k=50,
        top_p=0.95
)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
```