fix README

README.md

@@ -26,13 +26,13 @@ datasets:
 - Model creator: [Nous Research](https://huggingface.co/NousResearch)
 - Original model: [Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
 
-- Quantization code: [justinthelaw's GitHub](https://github.com/justinthelaw/quantization-pipeline-experiments)
-- Quantization creator: [Justin Law](https://huggingface.co/justinthelaw)
+- Quantization code: [GitHub Repository](https://github.com/justinthelaw/quantization-pipeline-experiments)
+- Quantization creator: [Defense Unicorns](https://huggingface.co/defenseunicorns), [Justin Law](https://huggingface.co/justinthelaw)
 
 <!-- description start -->
 ## Description
 
-This repo contains GPTQ 4-bit, 32g Group Size, quantized model files for the Nous Research [Hermes-2-Pro-Mistral-7B](https://huggingface.co/justinthelaw/Hermes-2-Pro-Mistral-7B-4bit-128g-instruct) fine-tune of the [Mistral-7b-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) model.
+This repo contains GPTQ 4-bit, 32g Group Size, quantized model files from the Nous Research [Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) fine-tune of the [Mistral-7b-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) model.
 
 <!-- README_GPTQ.md-provided-files start -->
 ## GPTQ parameters
@@ -45,8 +45,11 @@ Models are released as sharded safetensors files.
 
 <!-- README_GPTQ.md-provided-files end -->
 
+## Original Model Card Below
 
-# Hermes 2 Pro - Mistral 7B
+---
+
+## Hermes 2 Pro - Mistral 7B
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ggO2sBDJ8Bhc6w-zwTx5j.png)
 
@@ -62,17 +65,17 @@ Hermes Pro takes advantage of a special system prompt and multi-turn function ca
 
 This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI
 
-Learn more about the function calling system for this model on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling
+Learn more about the function calling system for this model on our github repo here: <https://github.com/NousResearch/Hermes-Function-Calling>
 
-## Thank you to Latitude.sh for sponsoring compute for this model!
+## Thank you to Latitude.sh for sponsoring compute for this model
 
 ## Example Outputs
 
-### Explaining Problems with Quantum Gravity:
+### Explaining Problems with Quantum Gravity
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/y_hPafyyvPb32efC5N4Es.png)
 
-### Roleplaying as a Cosmic Super Intelligence:
+### Roleplaying as a Cosmic Super Intelligence
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/m6d6Saf7M6Luu9QnXYYAP.png)
 
@@ -80,7 +83,6 @@ Learn more about the function calling system for this model on our github repo h
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/MOLybxs7_dLjVys54imO3.png)
 
-
 # Prompt Format
 
 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.
@@ -92,6 +94,7 @@ This is a more complex format than alpaca or sharegpt, where special tokens were
 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 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|>
@@ -120,9 +123,10 @@ 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. 
+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:
@@ -132,12 +136,14 @@ You are a function calling AI model. You are provided with function signatures w
 ```
 
 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):
+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>
@@ -146,6 +152,7 @@ The model will then generate a tool call, which your inference code must parse,
 ```
 
 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>
@@ -155,6 +162,7 @@ Once you parse the tool call, call the api and get the returned values for the c
 ```
 
 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:
@@ -178,7 +186,7 @@ This information provides a snapshot of Tesla's financial position and performan
 
 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
+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
@@ -187,10 +195,10 @@ You are a helpful assistant that answers in JSON. Here's the json schema you mus
 
 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.
 
-
 # Benchmarks
 
-## GPT4All:
+## GPT4All
+
 ```
 |    Task     |Version| Metric |Value |   |Stderr|
 |-------------|------:|--------|-----:|---|-----:|
@@ -207,9 +215,11 @@ Given the {schema} that you provide, it should follow the format of that json to
 |             |       |acc_norm|0.7998|±  |0.0093|
 |winogrande   |      0|acc     |0.7230|±  |0.0126|
 ```
+
 Average: 71.19
 
-## AGIEval:
+## AGIEval
+
 ```
 |             Task             |Version| Metric |Value |   |Stderr|
 |------------------------------|------:|--------|-----:|---|-----:|
@@ -230,9 +240,11 @@ Average: 71.19
 |agieval_sat_math              |      0|acc     |0.3818|±  |0.0328|
 |                              |       |acc_norm|0.3545|±  |0.0323|
 ```
+
 Average: 44.52
 
-## BigBench:
+## BigBench
+
 ```
 |                      Task                      |Version|       Metric        |Value |   |Stderr|
 |------------------------------------------------|------:|---------------------|-----:|---|-----:|
@@ -256,9 +268,11 @@ Average: 44.52
 |bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1737|±  |0.0091|
 |bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4900|±  |0.0289|
 ```
+
 Average: 41.65
 
-## TruthfulQA:
+## TruthfulQA
+
 ```
 |    Task     |Version|Metric|Value |   |Stderr|
 |-------------|------:|------|-----:|---|-----:|
@@ -279,12 +293,11 @@ We worked with Fireworks.AI on evaluations by starting off with their Function C
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/8H2iyjh5wyP2FtLq2LCed.png)
 
 Run the evaluator yourself using @interstellarninja's codebase here:
-https://github.com/interstellarninja/function-calling-eval
+<https://github.com/interstellarninja/function-calling-eval>
 
 You can find the evaluation datasets here:
-https://huggingface.co/datasets/NousResearch/func-calling-eval
-https://huggingface.co/datasets/NousResearch/json-mode-eval
-
+<https://huggingface.co/datasets/NousResearch/func-calling-eval>
+<https://huggingface.co/datasets/NousResearch/json-mode-eval>
 
 # Inference Code
 
@@ -327,8 +340,7 @@ for chat in prompts:
     print(f"Response: {response}")
 ```
 
-
-## Inference Code for Function Calling:
+## 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)
@@ -342,12 +354,11 @@ In LM-Studio, simply select the ChatML Prefix on the settings side pane:
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png)
 
+## Quantized Versions
 
-## Quantized Versions:
+GGUF Versions Available Here: <https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF>
 
-GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF
-
-# How to cite:
+# How to cite
 
 ```bibtext
 @misc{Hermes-2-Pro-Mistral-7B, 
@@ -356,4 +367,3 @@ GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-M
       author={"interstellarninja", "Teknium", "theemozilla", "karan4d", "huemin_art"}
 }
 ```
-