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vicky4s4s
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Hermes-2-Pro-Mistral-7B

Text Generation
Transformers
Safetensors
English
mistral
Mistral
instruct
finetune
chatml
DPO
RLHF
gpt4
synthetic data
distillation
function calling
json mode
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
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  1. README.md +342 -0
  2. added_tokens.json +34 -0
  3. config.json +26 -0
  4. generation_config.json +7 -0
  5. model.safetensors.index.json +298 -0
  6. special_tokens_map.json +30 -0
  7. tokenizer.model +3 -0
  8. tokenizer_config.json +301 -0
README.md ADDED
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+ ---
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+ base_model: mistralai/Mistral-7B-v0.1
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+ tags:
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+ - Mistral
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+ - instruct
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+ - finetune
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+ - chatml
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+ - DPO
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+ - RLHF
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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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+ model-index:
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+ - name: Hermes-2-Pro-Mistral-7B
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+ results: []
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+ license: apache-2.0
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+ language:
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+ - en
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+ datasets:
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+ - teknium/OpenHermes-2.5
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+ widget:
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+ - example_title: Hermes 2 Pro
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+ messages:
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+ - role: system
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+ content: You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.
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+ - role: user
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+ content: Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.
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+ ---
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+
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+ # Hermes 2 Pro - Mistral 7B
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ggO2sBDJ8Bhc6w-zwTx5j.png)
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+
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+ ## Model Description
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+
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+ Hermes 2 Pro on Mistral 7B is the new flagship 7B Hermes!
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+
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+ Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.
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+
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+ This new version of Hermes maintains its excellent general task and conversation capabilities - but also excels at Function Calling, JSON Structured Outputs, and has improved on several other metrics as well, scoring a 90% on our function calling evaluation built in partnership with Fireworks.AI, and an 84% on our structured JSON Output evaluation.
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+
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+ Hermes Pro takes advantage of a special system prompt and multi-turn function calling structure with a new chatml role in order to make function calling reliable and easy to parse. Learn more about prompting below.
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+
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+ This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI
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+
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+ Learn more about the function calling system for this model on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling
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+
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+ ## Thank you to Latitude.sh for sponsoring compute for this model!
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+
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+ ## Example Outputs
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+
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+ ### Explaining Problems with Quantum Gravity:
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/y_hPafyyvPb32efC5N4Es.png)
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+
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+ ### Roleplaying as a Cosmic Super Intelligence:
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/m6d6Saf7M6Luu9QnXYYAP.png)
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+
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+ ### Detailing the Theory of AI Consciousness in JSON
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/MOLybxs7_dLjVys54imO3.png)
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+
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+
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+ # Prompt Format
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+
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+ Hermes 2 Pro 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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+
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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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+
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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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+
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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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+
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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 2", 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 2, 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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+
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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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+
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+ ```python
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+ messages = [
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+ {"role": "system", "content": "You are Hermes 2."},
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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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+
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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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+
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+ To utilize the prompt format without a system prompt, simply leave the line out.
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+
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+ ## Prompt Format for Function Calling
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+
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+ Our model was trained on specific system prompts and structures for Function Calling.
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ ## Prompt Format for JSON Mode / Structured Outputs
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+
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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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+
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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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+ ```
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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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+
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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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+
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+
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+ # Benchmarks
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+
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+ ## GPT4All:
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+ ```
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+ | Task |Version| Metric |Value | |Stderr|
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+ |-------------|------:|--------|-----:|---|-----:|
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+ |arc_challenge| 0|acc |0.5461|± |0.0145|
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+ | | |acc_norm|0.5623|± |0.0145|
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+ |arc_easy | 0|acc |0.8157|± |0.0080|
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+ | | |acc_norm|0.7934|± |0.0083|
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+ |boolq | 1|acc |0.8688|± |0.0059|
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+ |hellaswag | 0|acc |0.6272|± |0.0048|
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+ | | |acc_norm|0.8057|± |0.0039|
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+ |openbookqa | 0|acc |0.3360|± |0.0211|
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+ | | |acc_norm|0.4300|± |0.0222|
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+ |piqa | 0|acc |0.7954|± |0.0094|
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+ | | |acc_norm|0.7998|± |0.0093|
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+ |winogrande | 0|acc |0.7230|± |0.0126|
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+ ```
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+ Average: 71.19
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+
195
+ ## AGIEval:
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+ ```
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+ | Task |Version| Metric |Value | |Stderr|
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+ |------------------------------|------:|--------|-----:|---|-----:|
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+ |agieval_aqua_rat | 0|acc |0.2047|± |0.0254|
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+ | | |acc_norm|0.2283|± |0.0264|
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+ |agieval_logiqa_en | 0|acc |0.3779|± |0.0190|
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+ | | |acc_norm|0.3932|± |0.0192|
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+ |agieval_lsat_ar | 0|acc |0.2652|± |0.0292|
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+ | | |acc_norm|0.2522|± |0.0287|
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+ |agieval_lsat_lr | 0|acc |0.5216|± |0.0221|
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+ | | |acc_norm|0.5137|± |0.0222|
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+ |agieval_lsat_rc | 0|acc |0.5911|± |0.0300|
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+ | | |acc_norm|0.5836|± |0.0301|
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+ |agieval_sat_en | 0|acc |0.7427|± |0.0305|
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+ | | |acc_norm|0.7184|± |0.0314|
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+ |agieval_sat_en_without_passage| 0|acc |0.4612|± |0.0348|
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+ | | |acc_norm|0.4466|± |0.0347|
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+ |agieval_sat_math | 0|acc |0.3818|± |0.0328|
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+ | | |acc_norm|0.3545|± |0.0323|
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+ ```
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+ Average: 44.52
217
+
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+ ## BigBench:
219
+ ```
220
+ | Task |Version| Metric |Value | |Stderr|
221
+ |------------------------------------------------|------:|---------------------|-----:|---|-----:|
222
+ |bigbench_causal_judgement | 0|multiple_choice_grade|0.5579|± |0.0361|
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+ |bigbench_date_understanding | 0|multiple_choice_grade|0.6694|± |0.0245|
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+ |bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3333|± |0.0294|
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+ |bigbench_geometric_shapes | 0|multiple_choice_grade|0.2061|± |0.0214|
226
+ | | |exact_str_match |0.2256|± |0.0221|
227
+ |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3120|± |0.0207|
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+ |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2114|± |0.0154|
229
+ |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4900|± |0.0289|
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+ |bigbench_movie_recommendation | 0|multiple_choice_grade|0.3600|± |0.0215|
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+ |bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
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+ |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6660|± |0.0105|
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+ |bigbench_ruin_names | 0|multiple_choice_grade|0.4420|± |0.0235|
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+ |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2766|± |0.0142|
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+ |bigbench_snarks | 0|multiple_choice_grade|0.6630|± |0.0352|
236
+ |bigbench_sports_understanding | 0|multiple_choice_grade|0.6653|± |0.0150|
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+ |bigbench_temporal_sequences | 0|multiple_choice_grade|0.3190|± |0.0147|
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+ |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2128|± |0.0116|
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+ |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1737|± |0.0091|
240
+ |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4900|± |0.0289|
241
+ ```
242
+ Average: 41.65
243
+
244
+ ## TruthfulQA:
245
+ ```
246
+ | Task |Version|Metric|Value | |Stderr|
247
+ |-------------|------:|------|-----:|---|-----:|
248
+ |truthfulqa_mc| 1|mc1 |0.4100|± |0.0172|
249
+ | | |mc2 |0.5911|± |0.0158|
250
+ ```
251
+
252
+ # Function Calling Evaluations
253
+
254
+ We worked with Fireworks.AI on evaluations by starting off with their Function Calling eval dataset, fixing some unsolveable ones, and generating a second eval dataset for JSON mode.
255
+
256
+ ## Function Calling Accuracy: 91%
257
+
258
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/XF3Zii4-QhE2yjWwHr_v4.png)
259
+
260
+ ## JSON Mode Accuracy: 84%
261
+
262
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/8H2iyjh5wyP2FtLq2LCed.png)
263
+
264
+ Run the evaluator yourself using @interstellarninja's codebase here:
265
+ https://github.com/interstellarninja/function-calling-eval
266
+
267
+ You can find the evaluation datasets here:
268
+ https://huggingface.co/datasets/NousResearch/func-calling-eval
269
+ https://huggingface.co/datasets/NousResearch/json-mode-eval
270
+
271
+
272
+ # Inference Code
273
+
274
+ Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
275
+
276
+ Note: To use function calling, you should see the github repo above.
277
+
278
+ ```python
279
+ # Code to inference Hermes with HF Transformers
280
+ # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
281
+
282
+ import torch
283
+ from transformers import AutoTokenizer, AutoModelForCausalLM
284
+ from transformers import LlamaTokenizer, MistralForCausalLM
285
+ import bitsandbytes, flash_attn
286
+
287
+ tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Mistral-7B', trust_remote_code=True)
288
+ model = MistralForCausalLM.from_pretrained(
289
+ "NousResearch/Hermes-2-Pro-Mistral-7B",
290
+ torch_dtype=torch.float16,
291
+ device_map="auto",
292
+ load_in_8bit=False,
293
+ load_in_4bit=True,
294
+ use_flash_attention_2=True
295
+ )
296
+
297
+ prompts = [
298
+ """<|im_start|>system
299
+ You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
300
+ <|im_start|>user
301
+ Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
302
+ <|im_start|>assistant""",
303
+ ]
304
+
305
+ for chat in prompts:
306
+ print(chat)
307
+ input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
308
+ 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)
309
+ response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
310
+ print(f"Response: {response}")
311
+ ```
312
+
313
+
314
+ ## Inference Code for Function Calling:
315
+
316
+ All code for utilizing, parsing, and building function calling templates is available on our github:
317
+ [https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
318
+
319
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/oi4CiGh50xmoviUQnh8R3.png)
320
+
321
+ # Chat Interfaces
322
+
323
+ When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
324
+ In LM-Studio, simply select the ChatML Prefix on the settings side pane:
325
+
326
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png)
327
+
328
+
329
+ ## Quantized Versions:
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+
331
+ GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF
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+
333
+ # How to cite:
334
+
335
+ ```bibtext
336
+ @misc{Hermes-2-Pro-Mistral-7B,
337
+ url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B]https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)},
338
+ title={Hermes-2-Pro-Mistral-7B},
339
+ author={"interstellarninja", "Teknium", "theemozilla", "karan4d", "huemin_art"}
340
+ }
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+ ```
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+
added_tokens.json ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
2
+ "<|im_end|>": 32000,
3
+ "<|im_start|>": 32001,
4
+ "<pad2>": 32002,
5
+ "<pad3>": 32003,
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+ "<pad4>": 32004,
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+ "<pad5>": 32005,
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+ "<pad6>": 32006,
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+ "<pad7>": 32007,
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+ "<pad8>": 32008,
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+ "<pad9>": 32009,
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+ "<pad10>": 32010,
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+ "<pad11>": 32011,
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+ "<pad12>": 32012,
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272
+ "lstrip": false,
273
+ "normalized": false,
274
+ "rstrip": false,
275
+ "single_word": false,
276
+ "special": false
277
+ },
278
+ "32031": {
279
+ "content": "<pad31>",
280
+ "lstrip": false,
281
+ "normalized": false,
282
+ "rstrip": false,
283
+ "single_word": false,
284
+ "special": false
285
+ }
286
+ },
287
+ "additional_special_tokens": [],
288
+ "bos_token": "<s>",
289
+ "chat_template": "{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
290
+ "clean_up_tokenization_spaces": true,
291
+ "eos_token": "<|im_end|>",
292
+ "legacy": true,
293
+ "model_max_length": 1000000000000000019884624838656,
294
+ "pad_token": "</s>",
295
+ "sp_model_kwargs": {},
296
+ "spaces_between_special_tokens": false,
297
+ "tokenizer_class": "LlamaTokenizer",
298
+ "unk_token": "<unk>",
299
+ "use_default_system_prompt": false,
300
+ "use_fast": true
301
+ }