NousResearch/DeepHermes-3-Llama-3-3B-Preview

DeepHermes 3 - Llama-3 3B Preview

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Model Description

DeepHermes 3 Preview is the latest version of our flagship Hermes series of LLMs by Nous Research, and one of the first models in the world to unify Reasoning (long chains of thought that improve answer accuracy) and normal LLM response modes into one model. We have also improved LLM annotation, judgement, and function calling.

DeepHermes 3 Preview is a hybrid reasoning model, and one of the first LLM models to unify both "intuitive", traditional mode responses and long chain of thought reasoning responses into a single model, toggled by a system prompt.

Hermes 3, the predecessor of DeepHermes 3, is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.

The ethos of the Hermes series of models is focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.

*This is a preview Hermes with early reasoning capabilities, distilled from R1 across a variety of tasks that benefit from reasoning and objectivity. Some quirks may be discovered! Please let us know any interesting findings or issues you discover!*

Note: To toggle REASONING ON, you must use the following system prompt:

You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside tags, and then provide your solution or response to the problem.

Benchmarks

Reasoning Benchmarks, with Reasoning ON and OFFF

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Comparison to Llama-3.2-3B-Instruct:

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Prompt Format

DeepHermes 3 now uses Llama-Chat format as the prompt format, opening up a more unified, structured system for engaging the LLM in multi-turn chat dialogue.

System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.

Deep Thinking Mode - Deep Hermes Preview can activate long chain of thought with a system prompt.

You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside tags, and then provide your solution or response to the problem.

For an example of using deep reasoning mode with HuggingFace Transformers:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import flash_attn
import time

tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-Llama-3-3B-Preview")

model = AutoModelForCausalLM.from_pretrained(
"NousResearch/DeepHermes-3-Llama-3-3B-Preview",
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="flash_attention_2",
)

messages = [
{
"role": "system",
"content": "You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside tags, and then provide your solution or response to the problem."
},
{
"role": "user",
"content": "What is y if y=2*2-4+(3*2)"
}
]

input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=2500, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
print(f"Generated Tokens: {generated_ids.shape[-1:]}")
response = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")

Please note, for difficult problems DeepHermes can think using as many as 13,000 tokens. You may need to increase max_new_tokens to be much larger than 2500 for difficult problems.

Standard "Intuitive" Response Mode

Prompt with system instruction (Use whatever system prompt you like, this is just an example!):

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import flash_attn
import time

tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-Llama-3-3B-Preview")

model = AutoModelForCausalLM.from_pretrained(
"NousResearch/DeepHermes-3-Llama-3-3B-Preview",
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="flash_attention_2",
)

messages = [
{
"role": "system",
"content": "You are Hermes, an AI assistant"
},
{
"role": "user",
"content": "What are the most interesting things to do in Paris?"
}
]

input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=2500, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
print(f"Generated Tokens: {generated_ids.shape[-1:]}")
response = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")

VLLM Inference

You can also run this model with vLLM, by running the following in your terminal after pip install vllm

vllm serve NousResearch/DeepHermes-3-Llama-3-3B-Preview

You may then use the model over API using the OpenAI library just like you would call OpenAI's API.

Prompt Format for Function Calling

Our model was trained on specific system prompts and structures for Function Calling.

You should use the system role with this message, followed by a function signature json as this example shows here.

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…