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1
+
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+
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - Llama-3
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+ - instruct
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+ - finetune
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+ - chatml
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+ - DPO
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+ - RLHF
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+ - gpt4
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+ - synthetic data
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+ - distillation
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+ - function calling
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+ - json mode
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+ - axolotl
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+ - GGUF
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+ base_model: NousResearch/Meta-Llama-3-8B
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+ datasets:
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+ - teknium/OpenHermes-2.5
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+ widget:
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+ - example_title: Hermes 2 Pro
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+ messages:
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+ - role: system
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+ content: You are a sentient, superintelligent artificial general intelligence,
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+ here to teach and assist me.
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+ - role: user
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+ content: Write a short story about Goku discovering kirby has teamed up with Majin
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+ Buu to destroy the world.
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+ model-index:
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+ - name: Hermes-2-Pro-Llama-3-8B
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+ results: []
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+ quantized_by: andrijdavid
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+ ---
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+ # Hermes-2-Pro-Llama-3-8B-GGUF
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+ - Original model: [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B)
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+
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+ <!-- description start -->
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+ ## Description
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+
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+ This repo contains GGUF format model files for [Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
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+
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+ <!-- description end -->
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+ <!-- README_GGUF.md-about-gguf start -->
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+ ### About GGUF
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+ GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
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+ Here is an incomplete list of clients and libraries that are known to support GGUF:
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+ * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
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+ * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
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+ * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications​
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+ * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
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+ * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
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+ * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
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+ * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
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+ * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
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+ * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
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+ * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
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+ * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
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+ * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.
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+ <!-- README_GGUF.md-about-gguf end -->
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+
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+ <!-- compatibility_gguf start -->
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+ ## Explanation of quantisation methods
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+ <details>
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+ <summary>Click to see details</summary>
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+ The new methods available are:
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+
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+ * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
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+ * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
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+ * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
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+ * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
74
+ * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.
75
+ </details>
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+ <!-- compatibility_gguf end -->
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+
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+ <!-- README_GGUF.md-how-to-download start -->
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+ ## How to download GGUF files
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+
81
+ **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.
82
+
83
+ The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
84
+
85
+ * LM Studio
86
+ * LoLLMS Web UI
87
+ * Faraday.dev
88
+
89
+ ### In `text-generation-webui`
90
+
91
+ Under Download Model, you can enter the model repo: LiteLLMs/Hermes-2-Pro-Llama-3-8B-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf.
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+
93
+ Then click Download.
94
+
95
+ ### On the command line, including multiple files at once
96
+
97
+ I recommend using the `huggingface-hub` Python library:
98
+
99
+ ```shell
100
+ pip3 install huggingface-hub
101
+ ```
102
+
103
+ Then you can download any individual model file to the current directory, at high speed, with a command like this:
104
+
105
+ ```shell
106
+ huggingface-cli download LiteLLMs/Hermes-2-Pro-Llama-3-8B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
107
+ ```
108
+
109
+ <details>
110
+ <summary>More advanced huggingface-cli download usage (click to read)</summary>
111
+
112
+ You can also download multiple files at once with a pattern:
113
+
114
+ ```shell
115
+ huggingface-cli download LiteLLMs/Hermes-2-Pro-Llama-3-8B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
116
+ ```
117
+
118
+ For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
119
+
120
+ To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
121
+
122
+ ```shell
123
+ pip3 install huggingface_hub[hf_transfer]
124
+ ```
125
+
126
+ And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
127
+
128
+ ```shell
129
+ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Hermes-2-Pro-Llama-3-8B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
130
+ ```
131
+
132
+ Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
133
+ </details>
134
+ <!-- README_GGUF.md-how-to-download end -->
135
+ <!-- README_GGUF.md-how-to-run start -->
136
+ ## Example `llama.cpp` command
137
+
138
+ Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
139
+
140
+ ```shell
141
+ ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
142
+ ```
143
+
144
+ Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
145
+
146
+ Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
147
+
148
+ If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
149
+
150
+ For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
151
+
152
+ ## How to run in `text-generation-webui`
153
+
154
+ Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
155
+
156
+ ## How to run from Python code
157
+
158
+ You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
159
+
160
+ ### How to load this model in Python code, using llama-cpp-python
161
+
162
+ For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
163
+
164
+ #### First install the package
165
+
166
+ Run one of the following commands, according to your system:
167
+
168
+ ```shell
169
+ # Base ctransformers with no GPU acceleration
170
+ pip install llama-cpp-python
171
+ # With NVidia CUDA acceleration
172
+ CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
173
+ # Or with OpenBLAS acceleration
174
+ CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
175
+ # Or with CLBLast acceleration
176
+ CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
177
+ # Or with AMD ROCm GPU acceleration (Linux only)
178
+ CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
179
+ # Or with Metal GPU acceleration for macOS systems only
180
+ CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
181
+ # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
182
+ $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
183
+ pip install llama-cpp-python
184
+ ```
185
+
186
+ #### Simple llama-cpp-python example code
187
+
188
+ ```python
189
+ from llama_cpp import Llama
190
+ # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
191
+ llm = Llama(
192
+ model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first
193
+ n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
194
+ n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
195
+ n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
196
+ )
197
+ # Simple inference example
198
+ output = llm(
199
+ "<PROMPT>", # Prompt
200
+ max_tokens=512, # Generate up to 512 tokens
201
+ stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
202
+ echo=True # Whether to echo the prompt
203
+ )
204
+ # Chat Completion API
205
+ llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
206
+ llm.create_chat_completion(
207
+ messages = [
208
+ {"role": "system", "content": "You are a story writing assistant."},
209
+ {
210
+ "role": "user",
211
+ "content": "Write a story about llamas."
212
+ }
213
+ ]
214
+ )
215
+ ```
216
+
217
+ ## How to use with LangChain
218
+
219
+ Here are guides on using llama-cpp-python and ctransformers with LangChain:
220
+
221
+ * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
222
+ * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
223
+
224
+ <!-- README_GGUF.md-how-to-run end -->
225
+
226
+ <!-- footer end -->
227
+
228
+ <!-- original-model-card start -->
229
+ # Original model card: Hermes-2-Pro-Llama-3-8B
230
+
231
+
232
+ # Hermes 2 Pro - Llama-3 8B
233
+
234
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ggO2sBDJ8Bhc6w-zwTx5j.png)
235
+
236
+ ## Model Description
237
+
238
+ 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.
239
+
240
+ 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.
241
+
242
+ 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.
243
+
244
+ 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.
245
+
246
+ This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI
247
+
248
+ Learn more about the function calling system for this model on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling
249
+
250
+ ## Example Outputs
251
+
252
+ ### Ask for a structured JSON output:
253
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ll2j2wkQffCsiSwUjfRUq.png)
254
+
255
+ ### Write the plot for a story where anime became real life:
256
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/h_7aXGXdm2p2ONYuDF4Ii.png)
257
+
258
+ ### Coding Assistance
259
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/bBd0hyAb8w5rKUiN2w1I6.png)
260
+
261
+ # Prompt Format
262
+
263
+ 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.
264
+
265
+ System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
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+
267
+ 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.
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+
269
+ 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.
270
+
271
+ Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
272
+ ```
273
+ <|im_start|>system
274
+ 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|>
275
+ <|im_start|>user
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+ Hello, who are you?<|im_end|>
277
+ <|im_start|>assistant
278
+ 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|>
279
+ ```
280
+
281
+ This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
282
+ `tokenizer.apply_chat_template()` method:
283
+
284
+ ```python
285
+ messages = [
286
+ {"role": "system", "content": "You are Hermes 2."},
287
+ {"role": "user", "content": "Hello, who are you?"}
288
+ ]
289
+ gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
290
+ model.generate(**gen_input)
291
+ ```
292
+
293
+ 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
294
+ that the model continues with an assistant response.
295
+
296
+ To utilize the prompt format without a system prompt, simply leave the line out.
297
+
298
+ ## Prompt Format for Function Calling
299
+
300
+ Our model was trained on specific system prompts and structures for Function Calling.
301
+
302
+ You should use the system role with this message, followed by a function signature json as this example shows here.
303
+ ```
304
+ <|im_start|>system
305
+ 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:
306
+ <tool_call>
307
+ {"arguments": <args-dict>, "name": <function-name>}
308
+ </tool_call><|im_end|>
309
+ ```
310
+
311
+ To complete the function call, create a user prompt that follows the above system prompt, like so:
312
+ ```
313
+ <|im_start|>user
314
+ Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
315
+ ```
316
+
317
+ 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):
318
+ ```
319
+ <|im_start|>assistant
320
+ <tool_call>
321
+ {"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
322
+ </tool_call><|im_end|>
323
+ ```
324
+
325
+ 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:
326
+ ```
327
+ <|im_start|>tool
328
+ <tool_response>
329
+ {"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}}
330
+ </tool_response>
331
+ <|im_end|>
332
+ ```
333
+
334
+ The assistant will then read in that data from the function's response, and generate a natural language response:
335
+ ```
336
+ <|im_start|>assistant
337
+ The stock fundamentals data for Tesla (TSLA) are as follows:
338
+ - **Symbol**: TSLA
339
+ - **Company Name**: Tesla, Inc.
340
+ - **Sector**: Consumer Cyclical
341
+ - **Industry**: Auto Manufacturers
342
+ - **Market Capitalization**: $566,160,130,480
343
+ - **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
344
+ - **Price-to-Book Ratio (PB Ratio)**: 9.04
345
+ - **Dividend Yield**: N/A
346
+ - **Trailing Earnings Per Share (EPS)**: $4.3
347
+ - **Beta Value of the Stock**: 2.42
348
+ - **52-Week High Price of the Stock**: $299.29
349
+ - **52-Week Low Price of the Stock**: $152.37
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+
351
+ 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|>
352
+ ```
353
+
354
+ ## Prompt Format for JSON Mode / Structured Outputs
355
+
356
+ 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.
357
+
358
+ 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
359
+
360
+ ```
361
+ <|im_start|>system
362
+ 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|>
363
+ ```
364
+
365
+ 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.
366
+
367
+
368
+ # Benchmarks
369
+
370
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/vOYv9wJUMn1Xrf4BvmO_x.png)
371
+
372
+ ## GPT4All:
373
+ ```
374
+ | Task |Version| Metric |Value | |Stderr|
375
+ |-|:|--||--:|--:|
376
+ |agieval_aqua_rat | 0|acc |0.2520|± |0.0273|
377
+ | | |acc_norm|0.2559|± |0.0274|
378
+ |agieval_logiqa_en | 0|acc |0.3548|± |0.0188|
379
+ | | |acc_norm|0.3625|± |0.0189|
380
+ |agieval_lsat_ar | 0|acc |0.1826|± |0.0255|
381
+ | | |acc_norm|0.1913|± |0.0260|
382
+ |agieval_lsat_lr | 0|acc |0.5510|± |0.0220|
383
+ | | |acc_norm|0.5255|± |0.0221|
384
+ |agieval_lsat_rc | 0|acc |0.6431|± |0.0293|
385
+ | | |acc_norm|0.6097|± |0.0298|
386
+ |agieval_sat_en | 0|acc |0.7330|± |0.0309|
387
+ | | |acc_norm|0.7039|± |0.0319|
388
+ |agieval_sat_en_without_passage| 0|acc |0.4029|± |0.0343|
389
+ | | |acc_norm|0.3689|± |0.0337|
390
+ |agieval_sat_math | 0|acc |0.3909|± |0.0330|
391
+ | | |acc_norm|0.3773|± |0.0328|
392
+ ```
393
+ Average: 42.44
394
+
395
+ ## BigBench:
396
+ ```
397
+ | Task |Version| Metric |Value | |Stderr|
398
+ ||:|--:|--:|
399
+ |bigbench_causal_judgement | 0|multiple_choice_grade|0.5737|± |0.0360|
400
+ |bigbench_date_understanding | 0|multiple_choice_grade|0.6667|± |0.0246|
401
+ |bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3178|± |0.0290|
402
+ |bigbench_geometric_shapes | 0|multiple_choice_grade|0.1755|± |0.0201|
403
+ | | |exact_str_match |0.0000|± |0.0000|
404
+ |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3120|± |0.0207|
405
+ |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2014|± |0.0152|
406
+ |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.5500|± |0.0288|
407
+ |bigbench_movie_recommendation | 0|multiple_choice_grade|0.4300|± |0.0222|
408
+ |bigbench_navigate | 0|multiple_choice_grade|0.4980|± |0.0158|
409
+ |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7010|± |0.0102|
410
+ |bigbench_ruin_names | 0|multiple_choice_grade|0.4688|± |0.0236|
411
+ |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.1974|± |0.0126|
412
+ |bigbench_snarks | 0|multiple_choice_grade|0.7403|± |0.0327|
413
+ |bigbench_sports_understanding | 0|multiple_choice_grade|0.5426|± |0.0159|
414
+ |bigbench_temporal_sequences | 0|multiple_choice_grade|0.5320|± |0.0158|
415
+ |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2280|± |0.0119|
416
+ |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1531|± |0.0086|
417
+ |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.5500|± |0.0288|
418
+ ```
419
+ Average: 43.55
420
+
421
+ ## TruthfulQA:
422
+ ```
423
+ | Task |Version|Metric|Value| |Stderr|
424
+ |-|:|||-----:|
425
+ |truthfulqa_mc| 1|mc1 |0.410|± |0.0172|
426
+ | | |mc2 |0.578|± |0.0157|
427
+ ```
428
+
429
+
430
+ # Inference Code
431
+
432
+ Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
433
+
434
+ Note: To use function calling, you should see the github repo above.
435
+
436
+ ```python
437
+ # Code to inference Hermes with HF Transformers
438
+ # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
439
+
440
+ import torch
441
+ from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
442
+ import bitsandbytes, flash_attn
443
+
444
+ tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Llama-3-8B', trust_remote_code=True)
445
+ model = LlamaForCausalLM.from_pretrained(
446
+ "NousResearch/Hermes-2-Pro-Llama-3-8B",
447
+ torch_dtype=torch.float16,
448
+ device_map="auto",
449
+ load_in_8bit=False,
450
+ load_in_4bit=True,
451
+ use_flash_attention_2=True
452
+ )
453
+
454
+ prompts = [
455
+ """<|im_start|>system
456
+ You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
457
+ <|im_start|>user
458
+ Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
459
+ <|im_start|>assistant""",
460
+ ]
461
+
462
+ for chat in prompts:
463
+ print(chat)
464
+ input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
465
+ 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)
466
+ response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
467
+ print(f"Response: {response}")
468
+ ```
469
+
470
+
471
+ ## Inference Code for Function Calling:
472
+
473
+ All code for utilizing, parsing, and building function calling templates is available on our github:
474
+ [https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
475
+
476
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/oi4CiGh50xmoviUQnh8R3.png)
477
+
478
+ # Chat Interfaces
479
+
480
+ 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.
481
+ In LM-Studio, simply select the ChatML Prefix on the settings side pane:
482
+
483
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png)
484
+
485
+
486
+ ## Quantized Versions:
487
+
488
+ GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF
489
+
490
+ # How to cite:
491
+
492
+ ```bibtext
493
+ @misc{Hermes-2-Pro-Llama-3-8B,
494
+ url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B]https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B)},
495
+ title={Hermes-2-Pro-Llama-3-8B},
496
+ author={"Teknium", "interstellarninja", "theemozilla", "karan4d", "huemin_art"}
497
+ }
498
+ ```
499
+
500
+ <!-- original-model-card end -->