taozhiyuai / hermes-2-pro-llama-3

messages = [
{“role”: “system”, “content”: “您是赫尔墨斯2。”},
{“role”: “user”, “content”: “你好，你是谁？”}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors=“pt”)
model.generate(**gen_input)

在调用apply_chat_template()进行消息分词以生成时，设置add_generation_prompt=True。这将把<|im_start|>assistant\n追加到提示中，以确保模型继续以辅助响应方式继续。

如果不需要系统提示，只需删除该行。

功能调用提示格式

我们的模型经过特定系统提示和结构的功能调用的训练。

您应在这条消息中使用系统角色，然后跟上如以下示例所示的功能签名JSON。

<|im_start|>system
您是一个功能调用AI模型。您在XML标签内接收到功能签名。您可以调用一个或多个功能来帮助解决用户查询。不要对要插入到函数中的值做出假设。以下是可用的工具：{"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - 使用yfinance API获取给定股票符号的基本数据。\n\n 参数:\n symbol (str): 股票符号。\n\n 返回:\n dict: 包含基本数据的字典。\n 键:\n - 'symbol'：股票符号。\n - 'company_name'：公司的长名称。\n - 'sector'：公司所属的部门。\n - 'industry'：公司所属的行业。\n - 'market_cap'：公司的市值。\n - 'pe_ratio'：向前市盈率。\n - 'pb_ratio'：市净率。\n - 'dividend_yield'：股息收益率。\n - 'eps'：摊薄每股收益。\n - 'beta'：股票的beta值。\n - '52_week_high'：股票52周最高价。\n - '52_week_low'：股票52周最低价。”,"parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} 使用以下pydantic模型JSON模式进行每个工具调用：{"properties": {"arguments": {"title": "参数", "type": "object"}, "name": {"title": "名称", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"}} 每次功能调用返回一个带有XML标签的功能名称和参数的JSON对象，如下所示

{“arguments”: ， “name”: }
<|im_end|>

要完成功能调用，创建一个像以下这样的用户提示来跟随上述系统提示

<|im_start|>user
获取特斯拉（TSLA）的股票基本数据<|im_end|>

然后模型将生成一个工具调用，您的推理代码必须解析并将其插入到函数中（见此处示例推理代码：https://github.com/NousResearch/Hermes-Function-Calling):

<|im_start|>assistant

{“arguments”: {“symbol”: “TSLA”}, “name”: “get_stock_fundamentals”}
<|im_end|>

一旦您解析完工具调用，请调用API并获取调用返回的值，然后将它作为新角色传递回去，如下 Tool 类似

<|im_start|>tool

{“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}}

<|im_end|>

然后助手将从这个函数的响应中读取这些数据，并生成一个自然语言响应

<|im_start|>assistant
Tesla (TSLA) 的股票基本面数据如下
- 符号: TSLA
- 公司名称: Tesla, Inc.
- 行业: 消费周期
- 制造业: 汽车制造商
- 市值: $566,160,130,480 - **前市盈率 (PE 比率)**: 42.73 - **市净率 (PB 比率)**: 9.04 - **股息收益率**: N/A - **摊薄每股收益 (EPS)**: $4.3
- 股票的贝塔值: 2.42
- 52周最高价: \(299.29 - **52周最低价**:152.37

这些信息根据从 yfinance API 获得的根本数据，提供了特斯拉的财务状况和业绩的快照。它表明，与同行股票相比，特斯拉具有巨大的市值以及相对较高的市盈率和市净率。公司目前不支付股息，这体现在“股息收益率”为“无”。贝塔值表明，相对于市场，特斯拉的股价具有适中的波动性。52周最高价和最低价提供了过去一年中股价幅度的看法。当评估投资机会和做出投资决策时，这些数据可能很有用。

JSON 模式/结构化输出的提示格式

我们的模型还根据特定系统提示用于结构化输出的结构进行了训练，该提示应该以 JSON 对象响应，遵循特定的 JSON 架构。

您可以使用我们的代码库中的 pydantic 对象构建架构，standalone 脚本 jsonmode.py 可在此处找到：https://github.com/NousResearch/Hermes-Function-Calling/tree/main

<|im_start|>system
您是一个用 JSON 回答的有帮助的助手。以下是您必须遵守的 json 架构：

给定您提供的 {schema}，它应该遵循该 json 格式来创建其响应，您只需给定一个典型的用户提示，它就会以 JSON 格式进行响应。

基准

GPT4All

任务	版本	指标	值		Stderr
arc_challenge	0	acc	0.5520	±	0.0145
		acc_norm	0.5887	±	0.0144
arc_easy	0	acc	0.8350	±	0.0076
		acc_norm	0.8123	±	0.0080
boolq	1	acc	0.8584	±	0.0061
hellaswag	0	acc	0.6265	±	0.0048
		acc_norm	0.8053	±	0.0040
openbookqa	0	acc	0.3800	±	0.0217
		acc_norm	0.4580	±	0.0223
piqa	0	acc	0.8003	±	0.0093
		acc_norm	0.8118	±	0.0091
winogrande	0	acc	0.7490	±	0.0122

平均值: 72.62

AGIEval

任务	版本	指标	值		Stderr
agival_aqua_rat	0	acc	0.2520	±	0.0273
		acc_norm	0.2559	±	0.0274
agival_logiqa_en	0	acc	0.3548	±	0.0188
		acc_norm	0.3625	±	0.0189
agival_lsat_ar	0	acc	0.1826	±	0.0255
		acc_norm	0.1913	±	0.0260
agival_lsat_lr	0	acc	0.5510	±	0.0220
		acc_norm	0.5255	±	0.0221
agival_lsat_rc	0	acc	0.6431	±	0.0293
		acc_norm	0.6097	±	0.0298
agival_sat_en	0	acc	0.7330	±	0.0309
		acc_norm	0.7039	±	0.0319
agival_sat_en_without_passage	0	acc	0.4029	±	0.0343
		acc_norm	0.3689	±	0.0337
agival_sat_math	0	acc	0.3909	±	0.0330
		acc_norm	0.3773	±	0.0328

平均值: 42.44

BigBench

任务	版本	指标	值		Stderr
bigbench_causal_judgement	0	multiple_choice_grade	0.5737	±	0.0360
bigbench_date_understanding	0	multiple_choice_grade	0.6667	±	0.0246
bigbench_disambiguation_qa	0	multiple_choice_grade	0.3178	±	0.0290
bigbench_geometric_shapes	0	multiple_choice_grade	0.1755	±	0.0201
		exact_str_match	0.0000	±	0.0000
bigbench_logical_deduction_five_objects	0	multiple_choice_grade	0.3120	±	0.0207
bigbench_logical_deduction_seven_objects	0	multiple_choice_grade	0.2014	±	0.0152
bigbench_logical_deduction_three_objects	0	multiple_choice_grade	0.5500	±	0.0288
bigbench_movie_recommendation	0	multiple_choice_grade	0.4300	±	0.0222
bigbench_navigate	0	multiple_choice_grade	0.4980	±	0.0158
bigbench_reasoning_about_colored_objects	0	multiple_choice_grade	0.7010	±	0.0102
bigbench_ruin_names	0	multiple_choice_grade	0.4688	±	0.0236
bigbench_salient_translation_error_detection	0	multiple_choice_grade	0.1974	±	0.0126
bigbench_snarks	0	multiple_choice_grade	0.7403	±	0.0327
bigbench_sports_understanding	0	multiple_choice_grade	0.5426	±	0.0159
bigbench_temporal_sequences	0	multiple_choice_grade	0.5320	±	0.0158
bigbench_tracking_shuffled_objects_five_objects	0	multiple_choice_grade	0.2280	±	0.0119
bigbench_tracking_shuffled_objects_seven_objects	0	multiple_choice_grade	0.1531	±	0.0086
bigbench_tracking_shuffled_objects_three_objects	0	multiple_choice_grade	0.5500	±	0.0288

平均值: 43.55

TruthfulQA

任务	版本	指标	值		Stderr
truthfulqa_mc	1	mc1	0.410	±	0.0172
		mc2	0.578	±	0.0157

推理代码

以下是使用 HuggingFace Transformers 进行模型推理的示例代码（注意：在 4 位数中，它将需要大约 5GB 的 VRAM）

注意：要使用函数调用，您应该查看上面的github仓库。

用HF Transformers推理Hermes的代码

需要pytorch、transformers、bitsandbytes、sentencepiece、protobuf和flash-attn包

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
import bitsandbytes, flash_attn

tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Llama-3-8B', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
“NousResearch/Hermes-2-Pro-Llama-3-8B”,
torch_dtype=torch.float16,
device_map=“auto”,
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True
)

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}“)

函数调用推理代码

利用、解析和构建函数调用模板的所有代码均可在我们的github上找到： https://github.com/NousResearch/Hermes-Function-Calling

聊天接口

当模型量化版本发布时，我建议使用LM Studio与Hermes 2 Pro聊天。它不支持函数调用 - 对于该功能请使用我们的github仓库。它是一个使用llama.cpp后端和GGUF模型集成的GUI应用，为聊天提供类似ChatGPT的界面，并直接支持ChatML。在LM-Studio中，只需在设置侧边栏选择ChatML前缀

量化版本

可在此处获取GGUF版本： https://hf-mirror.com/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF

# Hermes 2 Pro - Llama-3 8B

import from https://hf-mirror.com/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF

![image.png](https://ollama.ac.cn/assets/taozhiyuai/hermes-2-pro-llama-3/fd5e3ad8-4173-428e-9634-dde38d4de74f)

# Model Description

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.

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.

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.

This version of Hermes 2 Pro adds several tokens to assist with agentic capabilities in parsing while streaming tokens - <tools>, <tool_call>, <tool_response> and their closing tags are single tokens now.

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

# 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.

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 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|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
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|>

This prompt is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method:

messages = [
    {"role": "system", "content": "You are Hermes 2."},
    {"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.

# Benchmarks

![image.png](https://ollama.ac.cn/assets/taozhiyuai/hermes-2-pro-llama-3/39053d20-cc9e-4e3b-acbb-89669c2b91eb)

## GPT4All:

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.5520|±  |0.0145|
|             |       |acc_norm|0.5887|±  |0.0144|
|arc_easy     |      0|acc     |0.8350|±  |0.0076|
|             |       |acc_norm|0.8123|±  |0.0080|
|boolq        |      1|acc     |0.8584|±  |0.0061|
|hellaswag    |      0|acc     |0.6265|±  |0.0048|
|             |       |acc_norm|0.8053|±  |0.0040|
|openbookqa   |      0|acc     |0.3800|±  |0.0217|
|             |       |acc_norm|0.4580|±  |0.0223|
|piqa         |      0|acc     |0.8003|±  |0.0093|
|             |       |acc_norm|0.8118|±  |0.0091|
|winogrande   |      0|acc     |0.7490|±  |0.0122|

Average: 72.62

## AGIEval:

|             Task             |Version| Metric |Value |   |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |0.2520|±  |0.0273|
|                              |       |acc_norm|0.2559|±  |0.0274|
|agieval_logiqa_en             |      0|acc     |0.3548|±  |0.0188|
|                              |       |acc_norm|0.3625|±  |0.0189|
|agieval_lsat_ar               |      0|acc     |0.1826|±  |0.0255|
|                              |       |acc_norm|0.1913|±  |0.0260|
|agieval_lsat_lr               |      0|acc     |0.5510|±  |0.0220|
|                              |       |acc_norm|0.5255|±  |0.0221|
|agieval_lsat_rc               |      0|acc     |0.6431|±  |0.0293|
|                              |       |acc_norm|0.6097|±  |0.0298|
|agieval_sat_en                |      0|acc     |0.7330|±  |0.0309|
|                              |       |acc_norm|0.7039|±  |0.0319|
|agieval_sat_en_without_passage|      0|acc     |0.4029|±  |0.0343|
|                              |       |acc_norm|0.3689|±  |0.0337|
|agieval_sat_math              |      0|acc     |0.3909|±  |0.0330|
|                              |       |acc_norm|0.3773|±  |0.0328|

Average: 42.44

## BigBench:

|                      Task                      |Version|       Metric        |Value |   |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5737|±  |0.0360|
|bigbench_date_understanding                     |      0|multiple_choice_grade|0.6667|±  |0.0246|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3178|±  |0.0290|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.1755|±  |0.0201|
|                                                |       |exact_str_match      |0.0000|±  |0.0000|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.3120|±  |0.0207|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2014|±  |0.0152|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.5500|±  |0.0288|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.4300|±  |0.0222|
|bigbench_navigate                               |      0|multiple_choice_grade|0.4980|±  |0.0158|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.7010|±  |0.0102|
|bigbench_ruin_names                             |      0|multiple_choice_grade|0.4688|±  |0.0236|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.1974|±  |0.0126|
|bigbench_snarks                                 |      0|multiple_choice_grade|0.7403|±  |0.0327|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.5426|±  |0.0159|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.5320|±  |0.0158|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2280|±  |0.0119|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1531|±  |0.0086|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.5500|±  |0.0288|

Average: 43.55

## TruthfulQA:

|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |0.410|±  |0.0172|
|             |       |mc2   |0.578|±  |0.0157|

# Inference Code

Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)

Note: To use function calling, you should see the github repo above.

## 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-2-Pro-Llama-3-8B', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
    "NousResearch/Hermes-2-Pro-Llama-3-8B",
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_8bit=False,
    load_in_4bit=True,
    use_flash_attention_2=True
)

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

![image.png](https://ollama.ac.cn/assets/taozhiyuai/hermes-2-pro-llama-3/a8fb5f04-4fc5-4232-97ee-923be7b5fc49)

## Chat Interfaces

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. In LM-Studio, simply select the ChatML Prefix on the settings side pane:

# Quantized Versions:

GGUF Versions Available Here: https://hf-mirror.com/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF

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