Update README.md

README.md

@@ -38,6 +38,8 @@ model-index:
 
 DeepHermes 3 Preview is the latest version of our flagship Hermes series of LLMs by Nous Research, featuring better function calling, more capabilities around LLM as a Judge, and a mixed, toggleable LongCoT Reasoning mode. 
 
+DeepHermes 3 Preview is 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.
@@ -49,8 +51,6 @@ The ethos of the Hermes series of models is focused on aligning LLMs to the user
 Full benchmark comparisons below:
 {TODO}
 
-# Deep Thinking Mode
-{TODO}
 
 # Prompt Format
 
@@ -58,81 +58,94 @@ DeepHermes 3 now uses Llama-Chat format as the prompt format, opening up a more
 
 System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
 
-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.
-
-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.
+## Deep Thinking Mode - Deep Hermes Preview can activate long chain of thought with a system prompt.
 
-Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
 ```
-<|im_start|>system
-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|>
-<|im_start|>user
-Hello, who are you?<|im_end|>
-<|im_start|>assistant
-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|>
+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 <think> </think> tags, and then provide your solution or response to the problem.
 ```
 
-This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
-`tokenizer.apply_chat_template()` method:
-
+For an example of using deep reasoning mode with HuggingFace Transformers:
 ```python
-messages = [
-    {"role": "system", "content": "You are Hermes 3."},
-    {"role": "user", "content": "Hello, who are you?"}
-]
-gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
-model.generate(**gen_input)
-```
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import flash_attn
+import time
+
+tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-RC4-e4")
 
-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
-that the model continues with an assistant response.
+model = AutoModelForCausalLM.from_pretrained(
+    "NousResearch/DeepHermes-3-RC4-e4",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    attn_implementation="flash_attention_2",
+    trust_remote_code=True
+)
 
-To utilize the prompt format without a system prompt, simply leave the line out.
+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 <think> </think> 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.
 
-# Inference
+## Standard "Intuitive" Response Mode
 
-Here is example code using HuggingFace Transformers to inference the model
+Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
 
 ```python
-# Code to inference Hermes with HF Transformers
-# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
-
 import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
-import bitsandbytes, flash_attn
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import flash_attn
+import time
+
+tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-RC4-e4")
 
-tokenizer = AutoTokenizer.from_pretrained('NousResearch/DeepHermes-3-Llama-3.1-8B-Preview', trust_remote_code=True)
-model = LlamaForCausalLM.from_pretrained(
-    "NousResearch/DeepHermes-3-Llama-3.1-8B-Preview",
+model = AutoModelForCausalLM.from_pretrained(
+    "NousResearch/DeepHermes-3-RC4-e4",
     torch_dtype=torch.float16,
     device_map="auto",
-    load_in_8bit=False,
-    load_in_4bit=True,
-    use_flash_attention_2=True
+    attn_implementation="flash_attention_2",
+    trust_remote_code=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""",
-    ]
+messages = [
+    {
+        "role": "system",
+        "content": "You are Hermes, an AI assistant"
+    },
+    {
+        "role": "user",
+        "content": "What are the most interesting things to do in Paris?"
+    }
+]
 
-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}")
+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/Hermes-3-Llama-3.1-8B`
 
+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
 
@@ -140,39 +153,35 @@ Our model was trained on specific system prompts and structures for Function Cal
 
 You should use the system role with this message, followed by a function signature json as this example shows here.
 ```
-<|im_start|>system
+<|start_header_id|>system<|end_header_id|>
 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:
 <tool_call>
 {"arguments": <args-dict>, "name": <function-name>}
-</tool_call><|im_end|>
+</tool_call><|eot_id|><|start_header_id|>user<|end_header_id|>
 ```
 
 To complete the function call, create a user prompt that follows the above system prompt, like so:
 ```
-<|im_start|>user
-Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
+Fetch the stock fundamentals data for Tesla (TSLA)<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 ```
 
 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):
 ```
-<|im_start|>assistant
 <tool_call>
 {"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
-</tool_call><|im_end|>
+</tool_call><|eot_id|><|start_header_id|>tool<|end_header_id|>
 ```
 
 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:
 ```
-<|im_start|>tool
 <tool_response>
 {"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}}
 </tool_response>
-<|im_end|>
+<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 ```
 
 The assistant will then read in that data from the function's response, and generate a natural language response:
 ```
-<|im_start|>assistant
 The stock fundamentals data for Tesla (TSLA) are as follows:
 - **Symbol**: TSLA
 - **Company Name**: Tesla, Inc.
@@ -187,7 +196,7 @@ The stock fundamentals data for Tesla (TSLA) are as follows:
 - **52-Week High Price of the Stock**: $299.29
 - **52-Week Low Price of the Stock**: $152.37
 
-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|>
+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.<|eot_id|><|start_header_id|>user<|end_header_id|>
 ```