HERMES-3-LLAMA-3.1-405B represents a significant advancement in the field of large language models (LLMs). Developed by NousResearch, this model is built upon Meta AI's Llama-3.1 405B foundation, incorporating sophisticated fine-tuning techniques to enhance its capabilities across various domains. As a frontier-level language model, HERMES-3-LLAMA-3.1-405B pushes the boundaries of natural language processing, offering improved performance in areas such as agentic behavior, roleplaying, reasoning, and multi-turn conversations.
The training process for HERMES-3-LLAMA-3.1-405B involved a full parameter fine-tune, meaning all 405 billion parameters were adjusted during training. This comprehensive approach allows for nuanced improvements across all aspects of the model's performance. Key focus areas during training included:
HERMES-3-LLAMA-3.1-405B has demonstrated competitive performance across various benchmarks:
It's worth noting that while HERMES-3-LLAMA-3.1-405B shows improvements in many areas, there are some benchmarks where it may not outperform its base model or other competitors. This is often due to the specialized nature of the fine-tuning process, which prioritizes certain capabilities over others.
Enhanced Agentic Behavior: HERMES-3-LLAMA-3.1-405B exhibits sophisticated agentic capabilities, allowing it to act more autonomously in complex scenarios.
Improved Roleplaying: The model demonstrates enhanced abilities in assuming and maintaining different personas or roles.
Robust Reasoning: HERMES-3-LLAMA-3.1-405B shows improved logical reasoning skills, making it valuable for complex problem-solving tasks.
Multi-turn Conversation Proficiency: The model maintains coherence and context over extended dialogues more effectively.
Long Context Understanding: HERMES-3-LLAMA-3.1-405B can process and maintain relevance over long text passages more efficiently.
Structured Output Generation: The model excels in generating structured outputs like JSON, making it suitable for integration with various software systems.
HERMES-3-LLAMA-3.1-405B uses the ChatML format for prompts, which allows for more structured multi-turn dialogues. This format enables OpenAI endpoint compatibility and provides flexibility in steering the model's behavior through system prompts.
Example prompt structure for HERMES-3-LLAMA-3.1-405B:
<|im_start|>system
[System instruction here]
<|im_end|>
<|im_start|>user
[User message here]
<|im_end|>
<|im_start|>assistant
[Assistant response here]
<|im_end|>
For function calling, HERMES-3-LLAMA-3.1-405B requires a specific system prompt and function signature JSON. The model uses a pydantic model JSON schema for tool calls.
Example system prompt for function calling:
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.
[Function signatures here]
<|im_end|>
HERMES-3-LLAMA-3.1-405B supports a JSON mode for generating structured outputs. This mode requires a specific system prompt that includes the desired JSON schema.
Example system prompt for JSON mode:
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:
{schema}
<|im_end|>
HERMES-3-LLAMA-3.1-405B requires significant computational resources:
import torch
from transformers import AutoTokenizer, LlamaForCausalLM
import bitsandbytes, flash_attn
tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-3-Llama-3.1-405B', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
"NousResearch/Hermes-3-Llama-3.1-405B",
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=True,
use_flash_attention_2=True
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.
<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.
<|im_end|>
<|im_start|>assistant"""
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(
input_ids,
max_new_tokens=750,
temperature=0.8,
repetition_penalty=1.1,
do_sample=True,
eos_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
HERMES-3-LLAMA-3.1-405B shows improvements in various areas compared to its base Llama-3.1 405B model, particularly in agentic capabilities, roleplaying, and structured output generation. However, it's important to note that in some benchmarks, especially those focused on math and reasoning (like MMLU-PRO), there might be a drop in performance compared to the base model.
When compared to other fine-tunes and models:
HERMES-3-LLAMA-3.1-405B is the latest flagship model in the Hermes series by NousResearch. It builds upon the successes of previous versions, incorporating lessons learned and advanced training techniques. Compared to its predecessors, HERMES-3-LLAMA-3.1-405B offers:
Given its advanced capabilities, HERMES-3-LLAMA-3.1-405B is suitable for a wide range of applications:
Advanced Chatbots and Virtual Assistants: The model's proficiency in multi-turn conversations and context understanding makes it ideal for creating sophisticated chatbots and virtual assistants.
Creative Writing and Content Generation: Its improved roleplaying abilities and creative capabilities make HERMES-3-LLAMA-3.1-405B an excellent tool for generating diverse and engaging content.
Code Generation and Analysis: The model's structured output capabilities and reasoning skills make it valuable for code-related tasks, including generation, explanation, and documentation.
Complex Problem Solving: Its enhanced reasoning capabilities make HERMES-3-LLAMA-3.1-405B suitable for tackling complex logical and analytical problems.
Data Analysis and Interpretation: The model can be used to analyze and interpret large volumes of data, providing insights and summaries.
Educational Tools: HERMES-3-LLAMA-3.1-405B's broad knowledge base and ability to explain complex concepts make it a powerful tool for creating educational content and tutoring systems.
Research Assistance: The model can aid researchers by summarizing papers, generating hypotheses, and assisting in literature reviews.
While HERMES-3-LLAMA-3.1-405B offers impressive capabilities, it's important to be aware of its limitations:
Resource Intensity: The model's size makes it challenging to deploy in resource-constrained environments without significant quantization.
Potential for Biases: Like all large language models, HERMES-3-LLAMA-3.1-405B may exhibit biases present in its training data.
Hallucination: The model can sometimes generate plausible-sounding but incorrect information, especially when asked about topics beyond its training data.
Contextual Limitations: While improved, the model still has limits in maintaining context over very long interactions or documents.
Ethical Considerations: The model's advanced capabilities raise ethical questions about its potential misuse, requiring careful consideration in its application.
HERMES-3-LLAMA-3.1-405B represents a significant step forward in the development of large language models. Its advanced capabilities in agentic behavior, roleplaying, reasoning, and structured output generation open up new possibilities for AI applications across various domains.
As the field of AI continues to evolve, we can expect further refinements and improvements to models like HERMES-3-LLAMA-3.1-405B. Future developments may focus on:
The open-source nature of HERMES-3-LLAMA-3.1-405B also encourages collaboration and innovation within the AI community, potentially leading to new breakthroughs and applications we have yet to imagine.
