transformers llama text-generation llama-2 self-instruct distillation synthetic instruction en license:other autotrain_compatible region:us
---
language:
- en
license: other
tags:
- llama-2
- self-instruct
- distillation
- synthetic instruction
inference: false
model_type: llama
---

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# Nous Research's Nous Hermes Llama 2 13B GPTQ

These files are GPTQ model files for [Nous Research's Nous Hermes Llama 2 13B](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b).

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.


## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Nous-Hermes-Llama2-GGML)
* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b)

## Prompt template: Alpaca

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {prompt}

### Response:
```

## Provided files

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch.  See below for instructions on fetching from different branches.

| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| main | 4 | 128 | False | 7.26 GB | True | AutoGPTQ | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. | 
| gptq-4bit-32g-actorder_True | 4 | 32 | True | 8.00 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. | 
| gptq-4bit-64g-actorder_True | 4 | 64 | True | 7.51 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | 
| gptq-4bit-128g-actorder_True | 4 | 128 | True | 7.26 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. | 
| gptq-8bit-128g-actorder_True | 8 | 128 | True | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. | 
| gptq-8bit-64g-actorder_True | 8 | 64 | True | 13.95 GB | False | AutoGPTQ | 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. | 
| gptq-8bit-128g-actorder_False | 8 | 128 | False | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. | 
| gptq-8bit--1g-actorder_True | 8 | None | True | 13.36 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |

## How to download from branches

- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Nous-Hermes-Llama2-GPTQ:gptq-4bit-32g-actorder_True`
- With Git, you can clone a branch with:
```
git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Nous-Hermes-Llama2-GPTQ`
```
- In Python Transformers code, the branch is the `revision` parameter; see below.

## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).

Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).

It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Nous-Hermes-Llama2-GPTQ`.
  - To download from a specific branch, enter for example `TheBloke/Nous-Hermes-Llama2-GPTQ:gptq-4bit-32g-actorder_True`
  - see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done"
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Nous-Hermes-Llama2-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
  * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!

## How to use this GPTQ model from Python code

First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:

`GITHUB_ACTIONS=true pip install auto-gptq`

Then try the following example code:

```python
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

model_name_or_path = "TheBloke/Nous-Hermes-Llama2-GPTQ"
model_basename = "gptq_model-4bit-128g"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

"""
To download from a specific branch, use the revision parameter, as in this example:

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        revision="gptq-4bit-32g-actorder_True",
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=False,
        device="cuda:0",
        quantize_config=None)
"""

prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {prompt}

### Response:
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])
```

## Compatibility

The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.

ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

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# Original model card: Nous Research's Nous Hermes Llama 2 13B


# Model Card: Nous-Hermes-Llama2-13b

Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI.

## Model Description

Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors.

This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable.

This model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine.

## Example Outputs:
![Example4](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example5.png "Example 4")
![Example1](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/Example1.png "Example 1")
![Example2](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example2.png "Example 2")
![Example3](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example3.png "Example 3")

## Model Training

The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style.

This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below

## Collaborators
The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI. 
  
Special mention goes to @winglian for assisting in some of the training issues.

Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. 

Among the contributors of datasets:
- GPTeacher was made available by Teknium
- Wizard LM by nlpxucan
- Nous Research Instruct Dataset was provided by Karan4D and HueminArt.  
- GPT4-LLM and Unnatural Instructions were provided by Microsoft
- Airoboros dataset by jondurbin
- Camel-AI's domain expert datasets are from Camel-AI
- CodeAlpaca dataset by Sahil 2801.

If anyone was left out, please open a thread in the community tab.

## Prompt Format

The model follows the Alpaca prompt format:
```
### Instruction:
<prompt>

### Response:
<leave a newline blank for model to respond>

```

or 

```
### Instruction:
<prompt>

### Input:
<additional context>

### Response:
<leave a newline blank for model to respond>

```  

## Benchmark Results
AGI-Eval
```
|             Task             |Version| Metric |Value |   |Stderr|
|agieval_aqua_rat              |      0|acc     |0.2362|±  |0.0267|
|                              |       |acc_norm|0.2480|±  |0.0272|
|agieval_logiqa_en             |      0|acc     |0.3425|±  |0.0186|
|                              |       |acc_norm|0.3472|±  |0.0187|
|agieval_lsat_ar               |      0|acc     |0.2522|±  |0.0287|
|                              |       |acc_norm|0.2087|±  |0.0269|
|agieval_lsat_lr               |      0|acc     |0.3510|±  |0.0212|
|                              |       |acc_norm|0.3627|±  |0.0213|
|agieval_lsat_rc               |      0|acc     |0.4647|±  |0.0305|
|                              |       |acc_norm|0.4424|±  |0.0303|
|agieval_sat_en                |      0|acc     |0.6602|±  |0.0331|
|                              |       |acc_norm|0.6165|±  |0.0340|
|agieval_sat_en_without_passage|      0|acc     |0.4320|±  |0.0346|
|                              |       |acc_norm|0.4272|±  |0.0345|
|agieval_sat_math              |      0|acc     |0.2909|±  |0.0307|
|                              |       |acc_norm|0.2727|±  |0.0301|
```
GPT-4All Benchmark Set
```
|    Task     |Version| Metric |Value |   |Stderr|
|arc_challenge|      0|acc     |0.5102|±  |0.0146|
|             |       |acc_norm|0.5213|±  |0.0146|
|arc_easy     |      0|acc     |0.7959|±  |0.0083|
|             |       |acc_norm|0.7567|±  |0.0088|
|boolq        |      1|acc     |0.8394|±  |0.0064|
|hellaswag    |      0|acc     |0.6164|±  |0.0049|
|             |       |acc_norm|0.8009|±  |0.0040|
|openbookqa   |      0|acc     |0.3580|±  |0.0215|
|             |       |acc_norm|0.4620|±  |0.0223|
|piqa         |      0|acc     |0.7992|±  |0.0093|
|             |       |acc_norm|0.8069|±  |0.0092|
|winogrande   |      0|acc     |0.7127|±  |0.0127|
```
BigBench Reasoning Test
```
|                      Task                      |Version|       Metric        |Value |   |Stderr|

|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5526|±  |0.0362|
|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7344|±  |0.0230|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.2636|±  |0.0275|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.0195|±  |0.0073|
|                                                |       |exact_str_match      |0.0000|±  |0.0000|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2760|±  |0.0200|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2100|±  |0.0154|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4400|±  |0.0287|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.2440|±  |0.0192|
|bigbench_navigate                               |      0|multiple_choice_grade|0.4950|±  |0.0158|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.5570|±  |0.0111|
|bigbench_ruin_names                             |      0|multiple_choice_grade|0.3728|±  |0.0229|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.1854|±  |0.0123|
|bigbench_snarks                                 |      0|multiple_choice_grade|0.6298|±  |0.0360|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.6156|±  |0.0155|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.3140|±  |0.0147|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2032|±  |0.0114|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1406|±  |0.0083|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4400|±  |0.0287|
```

These are the highest benchmarks Hermes has seen on every metric, achieving the following average scores:
- GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1
- 0.3657 on BigBench, up from 0.328 on hermes-llama1
- 0.372 on AGIEval, up from 0.354 on Hermes-llama1

These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position. 

## Resources for Applied Use Cases:
For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord  
For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot  

## Future Plans
We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward. 

## Model Usage
The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.
  
