# Use Qwen2.5-Coder-32B-Instruct By transformers
The most significant but also the simplest usage of Qwen2.5-Coder-32B-Instruct is using the `transformers` library. In this document, we show how to chat with Qwen2.5-Coder-32B-Instruct in either streaming mode or not.

## Basic Usage
You can just write several lines of code with `transformers` to chat with Qwen2.5-Coder-32B-Instruct. Essentially, we build the tokenizer and the model with `from_pretrained` method, and we use generate method to perform chatting with the help of chat template provided by the tokenizer. Below is an example of how to chat with Qwen2.5-Coder-32B-Instruct:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen2.5-Coder-32B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "write a quick sort algorithm."
messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    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]
```

The `apply_chat_template()` function is used to convert the messages into a format that the model can understand. 
The `add_generation_prompt` argument is used to add a generation prompt, which refers to `<|im_start|>assistant\n` to the input. Notably, we apply ChatML template for chat models following our previous practice. 
The `max_new_tokens` argument is used to set the maximum length of the response. The `tokenizer.batch_decode()` function is used to decode the response. In terms of the input, the above messages is an example to show how to format your dialog history and system prompt.

## Processing Long Texts
The current `config.json` is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.

For supported frameworks, you could add the following to `config.json` to enable YaRN:
```json
{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}
```


## Streaming Mode

With the help of `TextStreamer`, you can modify your chatting with CodeQwen to streaming mode. Below we show you an example of how to use it:


```python
# Repeat the code above before model.generate()
# Starting here, we add streamer for text generation.
from transformers import TextStreamer
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# This will print the output in the streaming mode.
generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=2048,
    streamer=streamer,
)
```

Besides using `TextStreamer`, we can also use `TextIteratorStreamer` which stores print-ready text in a queue, to be used by a downstream application as an iterator:

```python
# Repeat the code above before model.generate()
# Starting here, we add streamer for text generation.
from transformers import TextIteratorStreamer
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

from threading import Thread
generation_kwargs = dict(inputs=model_inputs.input_ids, streamer=streamer, max_new_tokens=2048)
thread = Thread(target=model.generate, kwargs=generation_kwargs)

thread.start()
generated_text = ""
for new_text in streamer:
    generated_text += new_text
    print(new_text, end="")
```