The Higgs-Llama-3-70B model is an advanced artificial intelligence tool, fine-tuned for role-playing scenarios while also maintaining a strong performance in general instruction-following and reasoning tasks. In this blog, you will learn how to effectively use this model, as well as troubleshoot issues you may encounter. Let’s get started!

Understanding the Model’s Design

Think of Higgs-Llama-3-70B as a highly skilled actor on a stage, capable of taking on numerous roles. This model has undergone a special training process that allows it to embody roles (like its human counterparts), all while being adept at following instructions and reasoning. The methodology behind it includes:

• Supervised fine-tuning with chat datasets.
• Creating preference pairs with a semi-automated pipeline involving human labels and private LLMs.
• Iterative preference optimization to ensure the performance aligns closely with the desired role.

Just like teaching an actor to play a role in a play, the model is trained to effectively adjust its responses based on the scenario, all underpinned by the guidance of feedback loops. This iterative approach ensures that the model stays true to its role in a conversation.

Performance Evaluation

Higgs-Llama-3-70B has been benchmarked against several notable models such as GPT-4o and Llama-3-70B-Instruct. Here’s a snapshot of its performance:

MMLU-Pro Benchmark

Model	MMLU-Pro
GPT-4o	72.6
Higgs-Llama-3-70B	63.2
Llama-3-70B-Instruct	56.2

Arena-Hard Benchmark

Model	Arena-Hard
GPT-4o	79.5
Higgs-Llama-3-70B	49.6
Llama-3-70B-Instruct	41.1

How to Use Higgs-Llama-3-70B

Using Higgs-Llama-3-70B is a straightforward process, thanks to the familiar prompting format derived from its predecessor, Meta-Llama-3-70B-Instruct. To utilize this model with Transformers, follow the code snippet below:

import transformers
import torch

model_id = "bosonai/Higgs-Llama-3-70B"
pipeline = transformers.pipeline(
  "text-generation",
  model=model_id,
  model_kwargs={"torch_dtype": torch.bfloat16},
  device_map="auto",
)

messages = [
  {"role": "system", "content": "You are an AI assistant that speaks in the style of Sheldon Cooper. You are arguing with the user and is trying to prove the opposite of what the user said."},
  {"role": "user", "content": "The earth is round."},
]

prompt = pipeline.tokenizer.apply_chat_template(
  messages,
  tokenize=False,
  add_generation_prompt=True
)

outputs = pipeline(
  prompt,
  max_new_tokens=256,
  eos_token_id=[
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>"),
    pipeline.tokenizer.eos_token_id,
  ],
  do_sample=True,
  temperature=1.0,
  top_p=0.95,
)

print(outputs[0]["generated_text"][len(prompt):])

Troubleshooting Tips

While running the transformer pipeline, you may encounter certain issues. Here are some common problems and tips to help you resolve them:

• Model Not Loading: Ensure that you have the correct model ID and your environment meets the necessary dependencies.
• Out of Memory Errors: Try reducing the batch size or allocating more memory to your process.
• Unexpected Output: Check your prompt structure. Try adjusting the system message to provide clearer guidance for expected responses.

In case of persistent problems or for further assistance, remember that you can connect for help. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

The Higgs-Llama-3-70B model represents a significant stride in AI performance for role-playing and instruction-following tasks. Utilizing its sophisticated training process allows for effective interactions that tightly adhere to specified roles. By following the setup instructions and troubleshooting tips provided above, you can harness the capabilities of this powerful model in your applications.

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.