Update README.md

README.md

@@ -9,213 +9,41 @@ tags:
 - llama
 - meta
 - facebook
-- unsloth
+- osllmai
+- Phoenix
+- Phoenix_cli
 - transformers
 ---
 
-# Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
 
-We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
+**osllm.ai  Models Highlights Program**
 
-## ✨ Finetune for Free
+**We believe there's no need to pay a token if you have a GPU on your computer.**
 
-All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
+Highlighting new and noteworthy models from the community. Join the conversation on Discord.
 
-| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
-|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
-| **Llama-3.1 8b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |
-| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing)               | 2x faster | 50% less |
-| **Gemma-2 9b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)               | 2.4x faster | 58% less |
 
-# Hermes 3 - Llama-3.1 70B
 
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/-kj_KflXsdpcZoTQsvx7W.jpeg)
 
+<p align="center">
+  <a href="https://osllm.ai">Official Website</a> &bull; <a href="https://docs.osllm.ai/index.html">Documentation</a> &bull; <a href="https://discord.gg/2fftQauwDD">Discord</a>
+</p>
 
-## Model Description
 
-Hermes 3 405B is the latest flagship model in the Hermes series of LLMs by Nous Research, and the first full parameter finetune since the release of Llama-3.1 405B. 
 
-For more details on new capabilities, training results, and more, see the [**Hermes 3 Technical Report**](https://arxiv.org/abs/2408.11857).
+<p align="center">
+  <b>NEW:</b> <a href="https://docs.google.com/forms/d/1CQXJvxLUqLBSXnjqQmRpOyZqD6nrKubLz2WTcIJ37fU/prefill">Subscribe to our mailing list</a> for updates and news!
+</p>
 
-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 405B is a frontier level, full parameter finetune of the Llama-3.1 405B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.
+Email: support@osllm.ai
 
-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.
 
-Special thanks to **LambdaLabs** for their 1-Click Cluster access provided to us to train this model! [Check out LambdaLabs' 1CC Here](https://lambdalabs.com/service/gpu-cloud/1-click-clusters).
+**Disclaimers** 
 
-# Benchmarks
+[osllm.ai](https://osllm.ai) is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. osllm.ai does not endorse, support, represent, or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate, or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. osllm.ai may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. osllm.ai disclaims all warranties or guarantees about the accuracy, reliability, or benefits of the Community Models. osllm.ai further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted, or available at any time or location, or error-free, virus-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through [osllm.ai](https://osllm.ai).
 
-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.
 
-Full benchmark comparisons below:
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/DIMca3M0U-ArWwtyIbF-k.png)
-
-
-# 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
-
-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|>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|>
-```
-
-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
-
-The Hermes 405B model requires over 800GB of VRAM to load in FP16, to remedy this, we have utilized NeuralMagic's FP8 quantization method to provide a pre-quantized model that fits only 430~GB of VRAM, and is compatible with the `VLLM` inference engine.
-
-You can also load this FP16 model in `bitsandbytes` 8bit or 4bit with bitsandbytes using HuggingFace Transformers (not recommended, as it is slower), by setting load_in_4bit or 8bit like so:
-
-```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
-
-tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-3-Llama-3.1-405B', trust_remote_code=True)
-model = LlamaForCausalLM.from_pretrained(
-    "NousResearch/Hermes-3-Llama-3.1-405B",
-    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}")
-```
-
-
-## 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:
-
-NeuralMagic FP8 Quantization (for use with VLLM): https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-405B-FP8
 
 
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