## Guide to Hugging Face

### Model Description
The Nous Hermes 2 Mixtral 8x7B SFT is the supervised finetune only version of the Nous Research model trained over the Mixtral 8x7B MoE LLM. It was trained on over 1,000,000 entries of primarily GPT-4 generated data, achieving state of the art performance on a variety of tasks.

We are grateful to Together.ai for sponsoring our compute during the many experiments both training Mixtral and working on DPO.

#### Example Outputs
– Writing Code for Data Visualization
– Writing Cyberpunk Psychedelic Poems
– Performing Backtranslation to Create Prompts from Input Text

### Benchmark Results
The Nous-Hermes 2 on Mixtral 8x7B SFT is the base model for major improvements on many benchmarks compared to the base Mixtral model. Results are provided for GPT4All, AGIEval, and BigBench benchmarks.

### Prompt Format
Nous Hermes 2 uses ChatML as the prompt format, providing a structured system for engaging the LLM in multi-turn chat dialogue.

### Inference Example Code
Use example code provided to inference the model, specifying system prompts and user input.

### Quantized Models
Quantized versions of the model are available in different sizes and from various authors, such as NousResearch, TheBloke, MLX community, and Exllama2.

For more information on Hugging Face and the Nous Hermes 2 Mixtral 8x7B SFT, you can refer to the provided links.

Source link
Manual: HuggingFace Nous Hermes 2 Mixtral 8x7B SFT

Introduction
Nous Hermes 2 Mixtral 8x7B SFT is a powerful model developed by Nous Research for natural language processing tasks. Trained over the Mixtral 8x7B MoE LLM, this model provides state-of-the-art performance across a variety of tasks. This manual will provide you with an overview of the model’s capabilities, example outputs, benchmark results, prompt format, inference example code, and information on quantized models.

Model Description
The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape. The SFT only version of Mixtral Hermes 2 is available along with an SFT+DPO version. You can find these versions using the provided links.

Example Outputs
This section provides example outputs from the model across various tasks, including writing code for data visualization, writing cyberpunk psychedelic poems, and performing backtranslation to create prompts from input text.

Benchmark Results
Nous-Hermes 2 on Mixtral 8x7B SFT has shown impressive results on benchmark tests. The average performance across different tasks is detailed in this section.

Prompt Format
Nous Hermes 2 uses ChatML as the prompt format, which provides a structured system for engaging the LLM in multi-turn chat dialogue. It allows for steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.

Inference Example Code
This section provides example code using HuggingFace Transformers to perform inference with the model. It includes instructions for tokenizing messages and utilizing the prompt format for generation.

Quantized Models
Quantized versions of the model are available in various forms. This includes SFT+DPO, SFT Only, GPTQ, and AWQ versions. We have provided links to access these quantized versions.

Conclusion
The HuggingFace Nous Hermes 2 Mixtral 8x7B SFT model is a powerful tool for natural language processing tasks. With its state-of-the-art performance and availability of quantized versions, it offers a wide range of applications. For more information and detailed capabilities, please refer to the official documentation and resources provided.

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Model description

Nous Hermes 2 Mixtral 8x7B SFT is the supervised finetune only version of our new flagship Nous Research model trained over the Mixtral 8x7B MoE LLM.

The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks.

This is the SFT only version of Mixtral Hermes 2, we have also released an SFT+DPO version, for people to find which works best for them, which can be found here: https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO



We are grateful to Together.ai for sponsoring our compute during the many experiments both training Mixtral and working on DPO!

  1. Example Outputs
  2. Benchmark Results
    • GPT4All
    • AGIEval
    • BigBench
    • Comparison to Mixtral-Instruct
  3. Prompt Format
  4. Inference Example Code
  5. Quantized Models



Example Outputs



Writing Code for Data Visualization

image/png



Writing Cyberpunk Psychedelic Poems

image/png



Performing Backtranslation to Create Prompts from Input Text

image/png



Benchmark Results

Nous-Hermes 2 on Mixtral 8x7B SFT is the bedrock for major improvements on many of the benchmarks below compared to the base Mixtral model, and is the SFT only version of our first model to beat the flagship Mixtral Finetune by MistralAI (the DPO version).



GPT4All:

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.5904|±  |0.0144|
|             |       |acc_norm|0.6323|±  |0.0141|
|arc_easy     |      0|acc     |0.8594|±  |0.0071|
|             |       |acc_norm|0.8607|±  |0.0071|
|boolq        |      1|acc     |0.8783|±  |0.0057|
|hellaswag    |      0|acc     |0.6592|±  |0.0047|
|             |       |acc_norm|0.8434|±  |0.0036|
|openbookqa   |      0|acc     |0.3400|±  |0.0212|
|             |       |acc_norm|0.4660|±  |0.0223|
|piqa         |      0|acc     |0.8324|±  |0.0087|
|             |       |acc_norm|0.8379|±  |0.0086|
|winogrande   |      0|acc     |0.7569|±  |0.0121|

Average: 75.36



AGIEval:

|             Task             |Version| Metric |Value |   |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |0.2441|±  |0.0270|
|                              |       |acc_norm|0.2598|±  |0.0276|
|agieval_logiqa_en             |      0|acc     |0.4025|±  |0.0192|
|                              |       |acc_norm|0.3978|±  |0.0192|
|agieval_lsat_ar               |      0|acc     |0.2391|±  |0.0282|
|                              |       |acc_norm|0.2043|±  |0.0266|
|agieval_lsat_lr               |      0|acc     |0.5353|±  |0.0221|
|                              |       |acc_norm|0.5098|±  |0.0222|
|agieval_lsat_rc               |      0|acc     |0.6617|±  |0.0289|
|                              |       |acc_norm|0.5948|±  |0.0300|
|agieval_sat_en                |      0|acc     |0.7961|±  |0.0281|
|                              |       |acc_norm|0.7816|±  |0.0289|
|agieval_sat_en_without_passage|      0|acc     |0.4757|±  |0.0349|
|                              |       |acc_norm|0.4515|±  |0.0348|
|agieval_sat_math              |      0|acc     |0.4818|±  |0.0338|
|                              |       |acc_norm|0.3909|±  |0.0330|

Average: 44.89



BigBench:

|                      Task                      |Version|       Metric        |Value |   |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5789|±  |0.0359|
|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7154|±  |0.0235|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.5388|±  |0.0311|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.4680|±  |0.0264|
|                                                |       |exact_str_match      |0.0000|±  |0.0000|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.3260|±  |0.0210|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2443|±  |0.0163|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.5233|±  |0.0289|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.3700|±  |0.0216|
|bigbench_navigate                               |      0|multiple_choice_grade|0.5000|±  |0.0158|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.6665|±  |0.0105|
|bigbench_ruin_names                             |      0|multiple_choice_grade|0.6317|±  |0.0228|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.2505|±  |0.0137|
|bigbench_snarks                                 |      0|multiple_choice_grade|0.7127|±  |0.0337|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.6592|±  |0.0151|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.6860|±  |0.0147|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2200|±  |0.0117|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1503|±  |0.0085|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.5233|±  |0.0289|

Average: 48.69



GPT4All

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AGI-Eval

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BigBench Reasoning Test

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Nous Hermes 2 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(message, 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.

When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. 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:

image/png

Here is example code using HuggingFace Transformers to inference the model (note: even in 4bit, it will require more than 24GB of VRAM)




import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import LlamaTokenizer, MixtralForCausalLM
import bitsandbytes, flash_attn

tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', trust_remote_code=True)
model = MixtralForCausalLM.from_pretrained(
    "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
    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}")



All sizes of GGUF Quantizations are available here:



SFT+DPO Version – https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF



SFT Only Version – https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF

(Note: If you have issues with these GGUF’s try TheBloke’s)



TheBloke has also quantized Hermes Mixtral in various forms:



SFT+DPO GGUF: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF



SFT GGUF: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF



SFT+DPO GPTQ: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GPTQ



SFT GPTQ: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ



SFT+DPO AWQ: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-AWQ



SFT AWQ: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ



There is also an MLX version available:


https://huggingface.co/mlx-community/Nous-Hermes-2-Mixtral-8x7B-DPO-4bit



Exllama2 quants available here:



https://huggingface.co/qeternity/Nous-Hermes-2-Mixtral-8x7B-SFT-4bpw-h6-exl2

(other sizes available in Qeternity’s repos)

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2024-01-20T18:29:58+01:00