Adapt Pred.py For Non-Chat Models: A Step-by-Step Guide

by Pedro Alvarez 56 views

Hey guys! So, you've probably noticed that the pred.py script is set up for chat models, right? But what if you want to use a regular, non-chat model like Llama2-7b? It can be a bit tricky, but don't worry, we're going to break it down step by step. This guide will help you adapt the pred.py script to work seamlessly with non-chat models, ensuring you get those predictions flowing without any null hiccups.

Understanding the Challenge

The main issue here is the difference in how chat models and non-chat models expect their input and format their output. Chat models are designed to handle conversations, so they use a specific message format with roles like "user" and "assistant." Non-chat models, on the other hand, are simpler and expect a direct text prompt. When you try to feed a non-chat model a chat-style message, it gets confused, and you end up with those pesky null predictions. So, let's dive into how to fix this!

The Initial Setup

First off, you've already done some crucial groundwork. Adding llama2 to the JSON files in the config folder is a great start. This tells your system that you want to use Llama2. You also tweaked the generation code, which is exactly the right approach. The original code looked something like this:

completion = client.chat.completions.create(
 model=model,
 messages=[{"role": "user", "content": prompt}],
 temperature=temperature,
 max_tokens=max_new_tokens,
 stream=False
 return completion.choices[0].message.content

And you modified it to this:

completion = client.completions.create(
 model=model,
 prompt=prompt,
 temperature=temperature,
 max_tokens=max_new_tokens,
 stream=False
 )
 return completion.choices[0].text

This is the core of adapting the script, but there are a few more tweaks we need to make to ensure everything runs smoothly. Remember, the devil is in the details, so let's get those details right!

Why the null Predictions?

You're seeing null predictions even though vllm isn't throwing errors, which can be super frustrating. This usually means the model is running, but the output isn't being correctly processed or returned. The key is to ensure the prompt is correctly formatted for the non-chat model and that the output is being extracted in the right way. We've already addressed the prompt format in the code snippet above, but let's double-check the output handling.

Step-by-Step Guide to Adapting pred.py

Let's walk through the changes you need to make to get your non-chat model working perfectly with pred.py. We'll cover everything from the code adjustments to the configuration tweaks.

1. Verify Model Configuration

First, make sure your model configuration is spot-on. Double-check the JSON files in your config folder. You need to ensure that the model key correctly points to your Llama2-7b model. This is crucial because it tells the script which model to load and use. If there's a typo or incorrect path here, the script might not load the model correctly, leading to unexpected behavior.

  • Check the model name: Ensure the model name in your configuration file exactly matches the model name you're using in your command-line arguments (e.g., llama-2-7b-hf).
  • Verify the path: If you're using a local model, ensure the path to the model files is correct. An incorrect path will prevent the model from loading.
  • Confirm the model type: Explicitly specify the model type as a non-chat model in your configuration. This helps the script handle the model correctly.

2. Adjust the Prompt Formatting

The way you format your prompt is super important for non-chat models. They don't expect the {"role": "user", "content": prompt} structure used for chat models. Instead, they need a direct text prompt. You've already adjusted the code to use prompt=prompt, which is excellent. However, let's dig deeper into the prompt itself.

  • Remove chat-specific formatting: Make sure there are no chat-specific prefixes or suffixes in your prompt. These models don't understand things like User: or Assistant:. Just stick to the raw text.
  • Add necessary context: Non-chat models often benefit from a bit of context at the beginning of the prompt. For example, if you're doing question answering, you might start with something like, "Answer the following question:" followed by the actual question.
  • Input length: Be mindful of the input length. Non-chat models, like all models, have a maximum input length. Make sure your prompt isn't exceeding this limit. If it is, you might need to truncate it or use a model with a larger context window.

3. Refine Output Handling

You've already changed the code to extract the text using completion.choices[0].text, which is the right move. But let's make sure we're handling the output perfectly. Sometimes, models can return extra text or special tokens that you don't want. Cleaning up the output can significantly improve the quality of your results.

  • Remove special tokens: Models often include special tokens like <EOS> (End of Sentence) or <PAD> (Padding). You'll want to strip these out to get a clean answer. You can use Python's string manipulation functions like replace() or regular expressions to do this.
  • Strip whitespace: Extra spaces or newline characters can clutter your output. Use strip() to remove leading and trailing whitespace.
  • Handle edge cases: Sometimes, models might return empty strings or unexpected results. Add some checks to handle these cases gracefully. For example, if the output is empty, you might return a default message or log an error.

4. Debugging with Logs

Your logs are your best friend when debugging. The log snippet you shared is helpful, but let's use logs more strategically to pinpoint issues. Add more logging statements to your code to see exactly what's happening at each step.

  • Log the prompt: Before sending the prompt to the model, log it. This helps you verify that the prompt is correctly formatted and contains the information you expect.
  • Log the raw output: Log the raw output from the model before you process it. This lets you see exactly what the model is returning and identify any unexpected tokens or formatting.
  • Log the cleaned output: After cleaning the output, log the final result. This confirms that your cleaning steps are working as expected.

5. Adjusting the pred.py Script

Let's look at a more detailed code snippet that incorporates these changes. This example assumes you're using the transformers library, which is common for working with models like Llama2.

from transformers import AutoModelForCausalLM, AutoTokenizer

def generate_response(model_name, prompt, temperature=0.1, max_new_tokens=128):
 print(f"Prompt being sent to model: {prompt}")
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)

 input_ids = tokenizer(prompt, return_tensors="pt").input_ids

 generation_output = model.generate(
 input_ids=input_ids,
 max_new_tokens=max_new_tokens,
 temperature=temperature,
 )

 generated_text = tokenizer.decode(generation_output[0])
 print(f"Raw output from model: {generated_text}")

 # Clean up the output
 cleaned_text = generated_text.replace("<EOS>", "").replace("<PAD>", "").strip()
 print(f"Cleaned output: {cleaned_text}")

 return cleaned_text

In this example:

  • We load the model and tokenizer using transformers.
  • We tokenize the prompt and pass it to the model.
  • We decode the generated output.
  • We clean the output by removing special tokens and stripping whitespace.
  • We've added print statements to log the prompt, raw output, and cleaned output.

6. Command-Line Arguments and Execution

Your command-line arguments look good, but let's make sure everything is aligned. The key is to ensure that the model name you pass to pred.py matches the model name you used when starting the vllm server.

vllm serve llama-2-7b-hf \
 --max-model-len 65536 \
 --gpu-memory-utilization 0.98

python3 pred.py --model llama-2-7b-hf
  • Consistency is key: Ensure that llama-2-7b-hf is the exact name used in both commands.
  • Check server status: Before running pred.py, make sure your vllm server is up and running. If the server isn't running, pred.py won't be able to connect to the model.
  • Port configuration: If you've changed the default port for the vllm server, make sure pred.py is configured to use the same port.

7. Testing and Iteration

Once you've made these changes, it's time to test your setup. Start with a simple prompt and see if you get a sensible response. If not, go back to your logs and look for clues. Debugging is an iterative process, so don't get discouraged if it takes a few tries to get everything working perfectly.

  • Start simple: Use a basic prompt to test the model's functionality. For example, ask it a simple question or give it a short instruction.
  • Gradually increase complexity: Once you have a basic setup working, try more complex prompts to test the model's capabilities.
  • Monitor performance: Keep an eye on the model's performance. If it's running slowly or consuming too much memory, you might need to adjust your settings or use a more powerful machine.

Example Scenario: Question Answering

Let's consider a practical example. Suppose you want to use Llama2-7b for question answering. Your prompt might look like this:

Answer the following question based on the context provided.

Context: Those corridors and he could have killed Frank without realising he’d got the wrong man. As it happens, we only have Derek’s word for it that Stefan ever went into the room.

Question: Who murdered Frank Parris in your deduction?

In your pred.py script, you would format this prompt and send it to the model. The model's response might be something like:

The correct answer is (C) Stefan Codrescu.

By following the steps outlined above, you can adapt your pred.py script to handle this type of prompt and get accurate answers.

Troubleshooting Common Issues

Even with the best preparation, you might run into some issues. Here are a few common problems and how to solve them:

  • null predictions: We've covered this extensively, but double-check your prompt formatting, output handling, and model configuration.
  • CUDA out of memory errors: If you're running out of GPU memory, try reducing the max_model_len or gpu-memory-utilization.
  • Slow performance: If the model is running slowly, try increasing the number of GPUs or using a more powerful machine.
  • Unexpected output: If the model is generating gibberish or unrelated text, double-check your prompt and make sure you're providing enough context.

Conclusion

Adapting pred.py for non-chat models might seem daunting at first, but by understanding the differences between chat and non-chat models and making the right adjustments, you can get it working smoothly. Remember, the key is to focus on prompt formatting, output handling, and thorough debugging. By following this guide, you'll be well on your way to using Llama2-7b and other non-chat models effectively. Keep experimenting, keep learning, and you'll become a pro in no time! Good luck, and happy coding!