Welcome to the realm of advanced AI coding assistance! Today, we are diving into the latest innovation from the Smaug series: the Dracarys2-72B-Instruct. This model brings forth significant improvements in coding performance, especially in generating code that meets your requirements. In this article, we will walk through how to use this powerful tool while providing some troubleshooting tips along the way.
Understanding Dracarys2-72B-Instruct
Dracarys2-72B-Instruct has been finetuned from the impressive Qwen2.5-72B-Instruct. Developed by Abacus.AI, it showcases improved LiveCodeBench scores, meaning it excels in generating and executing code efficiently. The prompt format for this model remains unchanged from its predecessor, allowing for ease of use.
How to Use Dracarys2-72B-Instruct with Transformers
python
import transformers
import torch
model_id = "abacusai/Dracarys2-72B-Instruct"
pipeline = transformers.pipeline(
task="text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16, "device_map": "auto"},
)
messages = [
{"role": "system", "content": "You are a data science coding assistant that generates Python code using Pandas and Numpy."},
{"role": "user", "content": "Write code to select rows from the dataframe df having the maximum temp for each city."},
]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids('eot_id')
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]['generated_text'][len(prompt):])
Breaking Down the Code
Think of using Dracarys2-72B-Instruct like preparing a gourmet meal. Each ingredient in the recipe serves a distinct purpose:
- Imports: Just as you bring out your best pots and pans, importing
transformersandtorchlibraries sets the foundation for your cooking—enabling the use of the model to generate code. - Model ID: Specifying the model ID is like selecting a specific recipe. It directs your cooking to the correct Brisket (or model) you want.
- Pipeline: Creating a text-generation pipeline initializes the kitchen equipment necessary for cooking (or code generation).
- Messages: This is where you outline your instructions, akin to preparing a detailed recipe step-by-step.
- Prompt: The prompt serves as your final mix before serving, guiding the model to know what you’re aiming to achieve.
Evaluation Results
The performance evaluation showcases the capabilities of Dracarys2-72B-Instruct compared to its predecessor:
- Code Generation: Dracarys scored 53.80 vs. Qwen’s 53.03.
- Code Execution (COT): Dracarys achieved 89.12 vs. Qwen’s 88.72.
Troubleshooting Tips
If you encounter any hiccups while using Dracarys2-72B-Instruct, consider the following troubleshooting steps:
- Make sure you have the latest version of
transformersandtorchinstalled in your environment. - Verify your model ID and ensure it matches the one specified.
- If outputs aren’t generating as expected, experiment with the parameters like
temperatureandtop_pfor varied results. - Check your Python version; compatibility may affect functionality.
- Lastly, for more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
The Dracarys2-72B-Instruct model stands as a testament to what’s achievable when powerful models are finetuned for specific tasks. Happy coding!
