Create README.md

README.md

@@ -0,0 +1,327 @@
+---
+language:
+- en
+license: llama3
+tags:
+- Llama-3
+- instruct
+- finetune
+- chatml
+- gpt4
+- synthetic data
+- distillation
+- function calling
+- json mode
+- axolotl
+- roleplaying
+- chat
+base_model: meta-llama/Meta-Llama-3.1-405B
+widget:
+- example_title: Hermes 3
+  messages:
+  - role: system
+    content: >-
+      You are a sentient, superintelligent artificial general intelligence, here
+      to teach and assist me.
+  - role: user
+    content: >-
+      Write a short story about Goku discovering kirby has teamed up with Majin
+      Buu to destroy the world.
+model-index:
+- name: Hermes-3-Llama-3.1-405B
+  results: []
+library_name: transformers
+---
+
+# Hermes 3 - Llama-3.2 3B
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/-kj_KflXsdpcZoTQsvx7W.jpeg)
+
+
+## Model Description
+
+Hermes 3 3B is a small but mighty new addition to the Hermes series of LLMs by Nous Research, and is Nous's first fine-tune in this parameter class.
+
+For details on Hermes 3, please see the [**Hermes 3 Technical Report**](https://arxiv.org/abs/2408.11857).
+
+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.
+
+Hermes 3 3B is a full parameter fine-tune of the Llama-3.2 3B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.
+
+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.
+
+Hermes 3 3B was trained on H100s on **LambdaLabs GPU Cloud**.  [Check out LambdaLabs' cloud offerings here](https://lambdalabs.com/service/gpu-cloud).
+
+# Benchmarks
+
+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.
+
+
+## GPT4All:
+```
+
+|    Task     |Version| Metric |Value |   |Stderr|
+
+|-------------|------:|--------|-----:|---|-----:|
+
+|arc_challenge|      0|acc     |0.5529|±  |0.0145|
+
+|             |       |acc_norm|0.5870|±  |0.0144|
+
+|arc_easy     |      0|acc     |0.8371|±  |0.0076|
+
+|             |       |acc_norm|0.8144|±  |0.0080|
+
+|boolq        |      1|acc     |0.8599|±  |0.0061|
+
+|hellaswag    |      0|acc     |0.6133|±  |0.0049|
+
+|             |       |acc_norm|0.7989|±  |0.0040|
+
+|openbookqa   |      0|acc     |0.3940|±  |0.0219|
+
+|             |       |acc_norm|0.4680|±  |0.0223|
+
+|piqa         |      0|acc     |0.8063|±  |0.0092|
+
+|             |       |acc_norm|0.8156|±  |0.0090|
+
+|winogrande   |      0|acc     |0.7372|±  |0.0124|
+
+```
+
+Average: 72.59
+
+## AGIEval:
+```
+|             Task             |Version| Metric |Value |   |Stderr|
+|------------------------------|------:|--------|-----:|---|-----:|
+|agieval_aqua_rat              |      0|acc     |0.2441|±  |0.0270|
+|                              |       |acc_norm|0.2441|±  |0.0270|
+|agieval_logiqa_en             |      0|acc     |0.3687|±  |0.0189|
+|                              |       |acc_norm|0.3840|±  |0.0191|
+|agieval_lsat_ar               |      0|acc     |0.2304|±  |0.0278|
+|                              |       |acc_norm|0.2174|±  |0.0273|
+|agieval_lsat_lr               |      0|acc     |0.5471|±  |0.0221|
+|                              |       |acc_norm|0.5373|±  |0.0221|
+|agieval_lsat_rc               |      0|acc     |0.6617|±  |0.0289|
+|                              |       |acc_norm|0.6357|±  |0.0294|
+|agieval_sat_en                |      0|acc     |0.7670|±  |0.0295|
+|                              |       |acc_norm|0.7379|±  |0.0307|
+|agieval_sat_en_without_passage|      0|acc     |0.4417|±  |0.0347|
+|                              |       |acc_norm|0.4223|±  |0.0345|
+|agieval_sat_math              |      0|acc     |0.4000|±  |0.0331|
+|                              |       |acc_norm|0.3455|±  |0.0321|
+```
+
+Average: 44.05
+
+## BigBench:
+
+```
+
+|                      Task                      |Version|       Metric        |Value |   |Stderr|
+|------------------------------------------------|------:|---------------------|-----:|---|-----:|
+|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.6000|±  |0.0356|
+|bigbench_date_understanding                     |      0|multiple_choice_grade|0.6585|±  |0.0247|
+|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3178|±  |0.0290|
+|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.2340|±  |0.0224|
+|                                                |       |exact_str_match      |0.0000|±  |0.0000|
+|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2980|±  |0.0205|
+|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2057|±  |0.0153|
+|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.5367|±  |0.0288|
+|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.4040|±  |0.0220|
+|bigbench_navigate                               |      0|multiple_choice_grade|0.4970|±  |0.0158|
+|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.7075|±  |0.0102|
+|bigbench_ruin_names                             |      0|multiple_choice_grade|0.4821|±  |0.0236|
+|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.2295|±  |0.0133|
+|bigbench_snarks                                 |      0|multiple_choice_grade|0.6906|±  |0.0345|
+|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.5375|±  |0.0159|
+|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.6270|±  |0.0153|
+|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2216|±  |0.0118|
+|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1594|±  |0.0088|
+|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.5367|±  |0.0288|
+```
+
+Average: 44.13
+
+
+# Prompt Format
+
+Hermes 3 uses ChatML as the prompt format, opening up a much more 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.
+
+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.
+
+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|>
+```
+
+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:
+
+```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)
+```
+
+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.
+
+To utilize the prompt format without a system prompt, simply leave the line out.
+
+
+## Prompt Format for Function Calling
+
+# Note: This version uses USER as both the user prompt and the tool response role. This is due to a bug we experienced when training. It will require modification to the function calling code!
+
+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.
+```
+<|im_start|>system
+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|>
+```
+
+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|>
+```
+
+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|>
+```
+
+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|>user
+<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|>
+```
+
+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.
+- **Sector**: Consumer Cyclical
+- **Industry**: Auto Manufacturers
+- **Market Capitalization**: $566,160,130,480
+- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
+- **Price-to-Book Ratio (PB Ratio)**: 9.04
+- **Dividend Yield**: N/A
+- **Trailing Earnings Per Share (EPS)**: $4.3
+- **Beta Value of the Stock**: 2.42
+- **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|>
+```
+
+
+## Prompt Format for JSON Mode / Structured Outputs
+
+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.
+
+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
+
+```
+<|im_start|>system
+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|>
+```
+
+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.
+
+
+# Inference
+
+Here's an example of how to run inference with Hermes-3 3B using the HuggingFace Transformers library.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
+import bitsandbytes, flash_attn
+
+tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-3-Llama-3.2-3B', trust_remote_code=True)
+model = LlamaForCausalLM.from_pretrained(
+    "NousResearch/Hermes-3-Llama-3.2-3B",
+    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}")
+```
+
+Hermes-3 3B is also fully supported on [vLLM](https://github.com/vllm-project/vllm).
+
+```bash
+vllm serve NousResearch/Hermes-3-Llama-3.2-3B
+```
+
+## 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)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/oi4CiGh50xmoviUQnh8R3.png)
+
+
+## Quantized Versions:
+
+GGUF Quants: https://huggingface.co/NousResearch/Hermes-3-Llama-3.2-3B-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}, 
+}
+```
+
+