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Model: QuantFactory/Hermes-3-Llama-3.1-8B-GGUF Source: Original Platform
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---
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language:
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- en
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license: llama3
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tags:
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- Llama-3
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- instruct
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- finetune
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- chatml
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- gpt4
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- synthetic data
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- distillation
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- function calling
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- json mode
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- axolotl
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- roleplaying
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- chat
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base_model: meta-llama/Meta-Llama-3.1-8B
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widget:
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- example_title: Hermes 3
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messages:
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- role: system
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content: You are a sentient, superintelligent artificial general intelligence,
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here to teach and assist me.
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- role: user
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content: What is the meaning of life?
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model-index:
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- name: Hermes-3-Llama-3.1-70B
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results: []
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---
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# QuantFactory/Hermes-3-Llama-3.1-8B-GGUF
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This is quantized version of [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B) created using llama.cpp
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# Original Model Card
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# Hermes 3 - Llama-3.1 8B
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## Model Description
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Hermes 3 is the latest version of our flagship Hermes series of LLMs by Nous Research.
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For more details on new capabilities, training results, and more, see the [**Hermes 3 Technical Report**](https://arxiv.org/abs/2408.11857).
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Hermes 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.
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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.
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The Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.
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# Benchmarks
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Hermes 3 is competitive, if not superior, to Llama-3.1 Instruct models at general capabilities, with varying strengths and weaknesses attributable between the two.
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Full benchmark comparisons below:
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# Prompt Format
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Hermes 3 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
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System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
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This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
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This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
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Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
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```
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<|im_start|>system
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You are Hermes 3, a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
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<|im_start|>user
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Hello, who are you?<|im_end|>
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<|im_start|>assistant
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Hi there! My name is Hermes 3, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
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```
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This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
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`tokenizer.apply_chat_template()` method:
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```python
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messages = [
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{"role": "system", "content": "You are Hermes 3."},
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{"role": "user", "content": "Hello, who are you?"}
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]
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gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model.generate(**gen_input)
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```
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When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
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that the model continues with an assistant response.
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To utilize the prompt format without a system prompt, simply leave the line out.
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## Prompt Format for Function Calling
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Our model was trained on specific system prompts and structures for Function Calling.
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You should use the system role with this message, followed by a function signature json as this example shows here.
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```
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<|im_start|>system
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You are a function calling AI model. You are provided with function signatures within <tools></tools> 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. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
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<tool_call>
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{"arguments": <args-dict>, "name": <function-name>}
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</tool_call><|im_end|>
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```
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To complete the function call, create a user prompt that follows the above system prompt, like so:
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```
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<|im_start|>user
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Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
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```
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The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
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```
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<|im_start|>assistant
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<tool_call>
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{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
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</tool_call><|im_end|>
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```
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Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
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```
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<|im_start|>tool
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<tool_response>
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{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
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</tool_response>
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<|im_end|>
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```
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The assistant will then read in that data from the function's response, and generate a natural language response:
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```
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<|im_start|>assistant
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The stock fundamentals data for Tesla (TSLA) are as follows:
|
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- **Symbol**: TSLA
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- **Company Name**: Tesla, Inc.
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- **Sector**: Consumer Cyclical
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- **Industry**: Auto Manufacturers
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- **Market Capitalization**: $566,160,130,480
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- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
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- **Price-to-Book Ratio (PB Ratio)**: 9.04
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- **Dividend Yield**: N/A
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- **Trailing Earnings Per Share (EPS)**: $4.3
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- **Beta Value of the Stock**: 2.42
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- **52-Week High Price of the Stock**: $299.29
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- **52-Week Low Price of the Stock**: $152.37
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This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
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```
|
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## Prompt Format for JSON Mode / Structured Outputs
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Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
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Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
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```
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<|im_start|>system
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You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
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```
|
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Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
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# Inference
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Here is example code using HuggingFace Transformers to inference the model
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```python
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# Code to inference Hermes with HF Transformers
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# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
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import torch
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
|
||||
import bitsandbytes, flash_attn
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-3-Llama-3.1-8B', trust_remote_code=True)
|
||||
model = LlamaForCausalLM.from_pretrained(
|
||||
"NousResearch/Hermes-3-Llama-3.1-8B",
|
||||
torch_dtype=torch.float16,
|
||||
device_map="auto",
|
||||
load_in_8bit=False,
|
||||
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}")
|
||||
```
|
||||
|
||||
You can also run this model with vLLM, by running the following in your terminal after `pip install vllm`
|
||||
|
||||
`vllm serve NousResearch/Hermes-3-Llama-3.1-8B`
|
||||
|
||||
## Inference Code for Function Calling:
|
||||
|
||||
All code for utilizing, parsing, and building function calling templates is available on our github:
|
||||
[https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
|
||||
|
||||
|
||||
|
||||
|
||||
## Quantized Versions:
|
||||
|
||||
GGUF Quants: https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B-GGUF
|
||||
|
||||
# How to cite:
|
||||
|
||||
```bibtext
|
||||
@misc{teknium2024hermes3technicalreport,
|
||||
title={Hermes 3 Technical Report},
|
||||
author={Ryan Teknium and Jeffrey Quesnelle and Chen Guang},
|
||||
year={2024},
|
||||
eprint={2408.11857},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CL},
|
||||
url={https://arxiv.org/abs/2408.11857},
|
||||
}
|
||||
```
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{}
|
||||
Reference in New Issue
Block a user