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# Jon Durbin's Airoboros 33B GPT4 GPTQ

These files are GPTQ 4bit model files for [Jon Durbin's Airoboros 33B GPT4](https://huggingface.co/jondurbin/airoboros-33b-gpt4).

It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).

## Repositories available

* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/airoboros-33b-gpt4-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/airoboros-33b-gpt4-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jondurbin/airoboros-33b-gpt4)

## Prompt template

```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.
USER: prompt
ASSISTANT:
```

## How to easily download and use this model in text-generation-webui

Please make sure you're using the latest version of text-generation-webui

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/airoboros-33b-gpt4-GPTQ`.
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: `airoboros-33b-gpt4-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:

`pip install auto-gptq`

Then try the following example code:

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

model_name_or_path = "TheBloke/airoboros-33b-gpt4-GPTQ"
model_basename = "model"

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)

prompt = "Tell me about AI"
prompt_template=f'''### Human: {prompt}
### Assistant:'''

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'])
```

## Provided files

**gptq_model-4bit--1g.safetensors**

This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.

It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.

* `gptq_model-4bit--1g.safetensors`
  * Works with AutoGPTQ in CUDA or Triton modes.
  * Works with GPTQ-for-LLaMa in CUDA mode.  May have issues with GPTQ-for-LLaMa Triton mode.
  * Works with text-generation-webui, including one-click-installers.
  * Parameters: Groupsize = -1. Act Order / desc_act = True.

<!-- footer start -->
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## Discord

For further support, and discussions on these models and AI in general, join us at:

[TheBloke AI's Discord server](https://discord.gg/theblokeai)

## Thanks, and how to contribute.

Thanks to the [chirper.ai](https://chirper.ai) team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI

**Special thanks to**: Aemon Algiz.

**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter


Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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# Original model card: Jon Durbin's Airoboros 33B GPT4


## Overview

This is a qlora fine-tuned 33b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros

The dataset used to fine-tune this model is available [here](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.1), with a specific focus on:
- trivia
- math/reasoning (although it still sucks)
- coding
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general

This model was fine-tuned with a fork of FastChat, and therefore uses the standard vicuna template:
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. USER: [prompt] ASSISTANT:
```

So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).

The most important bit, to me, is the context obedient question answering support, without extensive prompt engineering.

### Usage

The easiest way to get started is to use my fork of FastChat, which is mostly the same but allows for the increased context length and adds support for multi-line inputs:
```
pip install git+https://github.com/jondurbin/FastChat
```

Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
  --model-path airoboros-33b-gpt4 \
  --temperature 0.5 \
  --max-new-tokens 2048 \
  --no-history
```

### Context obedient question answering

By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question.  The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.

The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
url: https://some.web.site/123
date: 2023-06-01
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s).  The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```

It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.

*The __only__ prompts that need this closed context formating are closed-context instructions.  Normal questions/instructions do not!*

I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set

It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.

Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries?  Source?
ENDINSTRUCTION
```

And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```

The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:

```
USER: BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
Bluberries are green.
ENDINPUT
BEGININSTRUCTION
What color are bluberries?  Source?
ENDINSTRUCTION
 ASSISTANT:
```

<details>
  <summary>A more elaborate example, with a rewrite of the Michigan Wikipedia article to be fake data.</summary>

  Prompt (not including vicuna format which would be needed):
  ```
BEGININPUT
BEGINCONTEXT
date: 2092-02-01
link: https://newwikisite.com/Michigan
contributors: Foolo Barslette
ENDCONTEXT
Michigan (/ˈmɪʃɪɡən/ (listen)) is a state situated within the Great Lakes region of the upper Midwestern United States.
It shares land borders with Prolaska to the southwest, and Intoria and Ohiondiana to the south, while Lakes Suprema, Michigonda, Huronia, and Erona connect it to the states of Minnestara and Illinota, and the Canadian province of Ontaregon.
With a population of nearly 15.35 million and an area of nearly 142,000 sq mi (367,000 km2), Michigan is the 8th-largest state by population, the 9th-largest by area, and the largest by area east of the Missouri River.
Its capital is Chaslany, and its most populous city is Trentroit.
Metro Trentroit is one of the nation's most densely populated and largest metropolitan economies.
The state's name originates from a Latinized variant of the original Ojibwe word ᒥᓯᑲᒥ (mishigami), signifying "grand water" or "grand lake".

Michigan is divided into two peninsulas. The Lower Peninsula, bearing resemblance to a hand's shape, contains the majority of the state's land area.
The Upper Peninsula (often referred to as "the U.P.") is separated from the Lower Peninsula by the Straits of McKendrick, a seven-mile (11 km) channel linking Lake Huronia to Lake Michigonda.
The McKendrick Bridge unites the peninsulas.
Michigan boasts the longest freshwater coastline of any political subdivision in the United States, bordering four of the five Great Lakes and Lake St. Cassius.

It also possesses 84,350 inland lakes and ponds.
Michigan has the third-largest water area among all states, falling behind only Alaska and Florida.

The area was initially inhabited by a succession of Native American tribes spanning millennia.
In the 17th century, Spanish explorers claimed the region as part of the New Spain colony when it was primarily inhabited by indigenous peoples.
Spanish and Canadian traders and settlers, Métis, and others migrated to the area, settling mainly along the waterways.
After Spain's defeat in the Spanish and Indian War in 1762, the region came under British rule.

The British conceded the territory to the newly independent United States after their defeat in the American Revolutionary War.

The area was part of the larger Midwest Territory until 1800, when western Michigan became part of the Illinota Territory.
Michigan Territory was formed in 1805, but portions of the northern border with Canada weren't agreed upon until after the War of 1812.

Michigan joined the Union in 1837 as the 26th state, a free one. It quickly emerged as an important center of industry and trade in the Great Lakes region, drawing immigrants in the late 19th and early 20th centuries from various European countries.

Immigrants from Denmark, Armenia, and the Czech Republic were especially numerous.
Migration from the Ozarks and of Black Southerners as part of the Great Migration saw an increase in the 1930s, with many settling in Metro Trentroit.

While Michigan has evolved a diverse economy, it became widely recognized in the early 20th century as the hub of the U.S. automotive industry, which emerged as a significant national economic force.
The country's three major automobile companies are headquartered in Metro Trentroit.
Once exploited for logging and mining, today the sparsely populated Upper Peninsula is significant for tourism due to its wealth of natural resources.
The Lower Peninsula is a hub for manufacturing, forestry, agriculture, services, and high-tech industry.
ENDINPUT
BEGININSTRUCTION
What is Michigan's capital?
Give me a JSON object with 3 facts from Foolo's article.
What are the states that border Michigan?
Reference?
```

  Response:
  ```
Michigan's capital is Chaslany.

Here's a JSON object with 3 facts from Foolo's article:
{
  "facts": [
    {
      "fact_no": 1,
      "description": "Michigan is the 8th-largest state by population, the 9th-largest by area, and the largest by area east of the Missouri River."
    },
    {
      "fact_no": 2,
      "description": "Michigan is divided into two peninsulas. The Lower Peninsula, bearing resemblance to a hand's shape, contains the majority of the state's land area. The Upper Peninsula (often referred to as \"the U.P.\" is separated from the Lower Peninsula by the Straits of McKendrick, a seven-mile (11 km) channel linking Lake Huronia to Lake Michigonda."
    },
    {
      "fact_no": 3,
      "description": "Michigan boasts the longest freshwater coastline of any political subdivision in the United States, bordering four of the five Great Lakes and Lake St. Cassius. It also possesses 84,350 inland lakes and ponds. Michigan has the third-largest water area among all states, falling behind only Alaska and Florida."
    }
  ]
}

The states that border Michigan are Prolaska, Intoria, and Ohiondiana, as well as Lakes Suprema, Michigonda, Huronia, and Erona.

Reference: https://newwikisite.com/Michigan
```

</details>

<details>
  <summary>Here's another example, which extracts from multiple input blocks and connects the two.</summary>

  Prompt:
  ```
BEGININPUT
BEGINCONTEXT
name: John Doe
date: June 3, 2023
ticket number: JIRA-12345
ENDCONTEXT
Summary:Search results missing random items

Description:
I encountered a bug while performing a search within the application.
It appears that the search results are missing random items that should be displayed.
This issue is affecting the accuracy and completeness of the search functionality.

Steps to Reproduce:
1. Log in to the application.
2. Navigate to the search feature.
3. Enter a search query that should return multiple results.
4. Observe the displayed search results.

Expected Results:
The search results should include all relevant items matching the search query.

Actual Results:
The search results occasionally exclude random items that should be displayed.
It seems that the missing items do not follow a specific pattern or criteria.
Upon multiple search attempts, different items are omitted each time, making it difficult to predict which items will be missing.
ENDINPUT

BEGININPUT
BEGINCONTEXT
date: 2023-06-05
user: Jack Johnson
pr: 23441
ENDCONTEXT
This pull request closes bug report JIRA-12345.

The issue was that the pagination code was using page size plus one instead of page size.
ENDINPUT

BEGININSTRUCTION
Do we have any bug reports related to search results?  If so, were they fixed?  Source?
ENDINSTRUCTION
```
  Response:
  ```
Yes, there is a bug report related to search results. The issue was fixed in pull request 23441 dated 2023-06-05. The user associated with the pull request is Jack Johnson.

Reference(s):
date: 2023-06-05
user: Jack Johnson
pr: 23441
```

</details>

NOTE: Thanks /u/tareq_al_muntasir for testing and finding an issue with many questions and answer pairs in the context.  If you ask a question of a document with question answer pairs, it may continue generating beyond your actual question.  You can "fix" it by replacing question marks with periods in the input texts.  Or, you might be able to add a preamble to the prompt, like "Be sure to only respond to the instructions in the BEGININSTRUCTION block.

### Other stuff

The model is quite decent compared to other local models at generating code, writing, trivia, etc.  Give it a shot at anything, and let me know where it falls apart.

{"license": "other", "datasets": ["jondurbin/airoboros-gpt4-1.1"], "inference": false}
transformers safetensors llama text-generation dataset:jondurbin/airoboros-gpt4-1.1 license:other autotrain_compatible text-generation-inference 4-bit region:us
TAGS
#transformers #safetensors #llama #text-generation #dataset-jondurbin/airoboros-gpt4-1.1 #license-other #autotrain_compatible #text-generation-inference #4-bit #region-us 




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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="URL">andreessen horowitz (a16z)</a></p></div>
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# Jon Durbin's Airoboros 33B GPT4 GPTQ

These files are GPTQ 4bit model files for Jon Durbin's Airoboros 33B GPT4.

It is the result of quantising to 4bit using AutoGPTQ.

## Repositories available

* 4-bit GPTQ models for GPU inference
* 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
* Unquantised fp16 model in pytorch format, for GPU inference and for further conversions

## Prompt template



## How to easily download and use this model in text-generation-webui

Please make sure you're using the latest version of text-generation-webui

1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/airoboros-33b-gpt4-GPTQ'.
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: 'airoboros-33b-gpt4-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 installed:

'pip install auto-gptq'

Then try the following example code:



## Provided files

gptq_model-4bit--1g.safetensors

This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.

It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.

* 'gptq_model-4bit--1g.safetensors'
  * Works with AutoGPTQ in CUDA or Triton modes.
  * Works with GPTQ-for-LLaMa in CUDA mode.  May have issues with GPTQ-for-LLaMa Triton mode.
  * Works with text-generation-webui, including one-click-installers.
  * Parameters: Groupsize = -1. Act Order / desc_act = True.



## Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

## Thanks, and how to contribute.

Thanks to the URL team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

* Patreon: URL
* Ko-Fi: URL

Special thanks to: Aemon Algiz.

Patreon special mentions: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, URL, Vadim, danny, Deo Leter


Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.



# Original model card: Jon Durbin's Airoboros 33B GPT4


## Overview

This is a qlora fine-tuned 33b parameter LlaMa model, using completely synthetic training data created gpt4 via URL

The dataset used to fine-tune this model is available here, with a specific focus on:
- trivia
- math/reasoning (although it still sucks)
- coding
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general

This model was fine-tuned with a fork of FastChat, and therefore uses the standard vicuna template:


So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).

The most important bit, to me, is the context obedient question answering support, without extensive prompt engineering.

### Usage

The easiest way to get started is to use my fork of FastChat, which is mostly the same but allows for the increased context length and adds support for multi-line inputs:


Then, you can invoke it like so (after downloading the model):


### Context obedient question answering

By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question.  The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.

The format for a closed-context prompt is as follows:


It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.

*The __only__ prompts that need this closed context formating are closed-context instructions.  Normal questions/instructions do not!*

I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- 'BEGININPUT' - denotes a new input block
- 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block
- 'ENDCONTEXT' - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- 'ENDINPUT' - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- 'ENDINSTRUCTION' - denotes the end of instruction set

It sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.

Here's a trivial, but important example to prove the point:


And the response:


The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:



<details>
  <summary>A more elaborate example, with a rewrite of the Michigan Wikipedia article to be fake data.</summary>

  Prompt (not including vicuna format which would be needed):
  

  Response:
  

</details>

<details>
  <summary>Here's another example, which extracts from multiple input blocks and connects the two.</summary>

  Prompt:
  
  Response:
  

</details>

NOTE: Thanks /u/tareq_al_muntasir for testing and finding an issue with many questions and answer pairs in the context.  If you ask a question of a document with question answer pairs, it may continue generating beyond your actual question.  You can "fix" it by replacing question marks with periods in the input texts.  Or, you might be able to add a preamble to the prompt, like "Be sure to only respond to the instructions in the BEGININSTRUCTION block.

### Other stuff

The model is quite decent compared to other local models at generating code, writing, trivia, etc.  Give it a shot at anything, and let me know where it falls apart.

# Jon Durbin's Airoboros 33B GPT4 GPTQ

These files are GPTQ 4bit model files for Jon Durbin's Airoboros 33B GPT4.

It is the result of quantising to 4bit using AutoGPTQ. ## Repositories available

* 4-bit GPTQ models for GPU inference
* 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
* Unquantised fp16 model in pytorch format, for GPU inference and for further conversions ## Prompt template ## How to easily download and use this model in text-generation-webui

Please make sure you're using the latest version of text-generation-webui

1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/airoboros-33b-gpt4-GPTQ'.
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: 'airoboros-33b-gpt4-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 installed:

'pip install auto-gptq'

Then try the following example code: ## Provided files

gptq_model-4bit--1g.safetensors

This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.

It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.

* 'gptq_model-4bit--1g.safetensors'
  * Works with AutoGPTQ in CUDA or Triton modes.
  * Works with GPTQ-for-LLaMa in CUDA mode.  May have issues with GPTQ-for-LLaMa Triton mode.
  * Works with text-generation-webui, including one-click-installers.
  * Parameters: Groupsize = -1. Act Order / desc_act = True. ## Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server ## Thanks, and how to contribute.

Thanks to the URL team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

* Patreon: URL
* Ko-Fi: URL

Special thanks to: Aemon Algiz.

Patreon special mentions: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, URL, Vadim, danny, Deo Leter


Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant. # Original model card: Jon Durbin's Airoboros 33B GPT4 ## Overview

This is a qlora fine-tuned 33b parameter LlaMa model, using completely synthetic training data created gpt4 via URL

The dataset used to fine-tune this model is available here, with a specific focus on:
- trivia
- math/reasoning (although it still sucks)
- coding
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general

This model was fine-tuned with a fork of FastChat, and therefore uses the standard vicuna template:


So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).

The most important bit, to me, is the context obedient question answering support, without extensive prompt engineering. ### Usage

The easiest way to get started is to use my fork of FastChat, which is mostly the same but allows for the increased context length and adds support for multi-line inputs:


Then, you can invoke it like so (after downloading the model): ### Context obedient question answering

By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question.  The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.

The format for a closed-context prompt is as follows:


It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.

*The __only__ prompts that need this closed context formating are closed-context instructions.  Normal questions/instructions do not!*

I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- 'BEGININPUT' - denotes a new input block
- 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block
- 'ENDCONTEXT' - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- 'ENDINPUT' - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- 'ENDINSTRUCTION' - denotes the end of instruction set

It sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.

Here's a trivial, but important example to prove the point:


And the response:


The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:



<details>
  <summary>A more elaborate example, with a rewrite of the Michigan Wikipedia article to be fake data.</summary>

  Prompt (not including vicuna format which would be needed):
  

  Response:
  

</details>

<details>
  <summary>Here's another example, which extracts from multiple input blocks and connects the two.</summary>

  Prompt:
  
  Response:
  

</details>

NOTE: Thanks /u/tareq_al_muntasir for testing and finding an issue with many questions and answer pairs in the context.  If you ask a question of a document with question answer pairs, it may continue generating beyond your actual question.  You can "fix" it by replacing question marks with periods in the input texts.  Or, you might be able to add a preamble to the prompt, like "Be sure to only respond to the instructions in the BEGININSTRUCTION block. ### Other stuff

The model is quite decent compared to other local models at generating code, writing, trivia, etc.  Give it a shot at anything, and let me know where it falls apart.
TAGS
#transformers #safetensors #llama #text-generation #dataset-jondurbin/airoboros-gpt4-1.1 #license-other #autotrain_compatible #text-generation-inference #4-bit #region-us 
 # Jon Durbin's Airoboros 33B GPT4 GPTQ

These files are GPTQ 4bit model files for Jon Durbin's Airoboros 33B GPT4.

It is the result of quantising to 4bit using AutoGPTQ. ## Repositories available

* 4-bit GPTQ models for GPU inference
* 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
* Unquantised fp16 model in pytorch format, for GPU inference and for further conversions ## Prompt template ## How to easily download and use this model in text-generation-webui

Please make sure you're using the latest version of text-generation-webui

1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/airoboros-33b-gpt4-GPTQ'.
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: 'airoboros-33b-gpt4-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 installed:

'pip install auto-gptq'

Then try the following example code: ## Provided files

gptq_model-4bit--1g.safetensors

This will work with AutoGPTQ and CUDA versions of GPTQ-for-LLaMa. There are reports of issues with Triton mode of recent GPTQ-for-LLaMa. If you have issues, please use AutoGPTQ instead.

It was created without group_size to lower VRAM requirements, and with --act-order (desc_act) to boost inference accuracy as much as possible.

* 'gptq_model-4bit--1g.safetensors'
  * Works with AutoGPTQ in CUDA or Triton modes.
  * Works with GPTQ-for-LLaMa in CUDA mode.  May have issues with GPTQ-for-LLaMa Triton mode.
  * Works with text-generation-webui, including one-click-installers.
  * Parameters: Groupsize = -1. Act Order / desc_act = True. ## Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server ## Thanks, and how to contribute.

Thanks to the URL team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

* Patreon: URL
* Ko-Fi: URL

Special thanks to: Aemon Algiz.

Patreon special mentions: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, URL, Vadim, danny, Deo Leter


Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant. # Original model card: Jon Durbin's Airoboros 33B GPT4 ## Overview

This is a qlora fine-tuned 33b parameter LlaMa model, using completely synthetic training data created gpt4 via URL

The dataset used to fine-tune this model is available here, with a specific focus on:
- trivia
- math/reasoning (although it still sucks)
- coding
- multiple choice and fill-in-the-blank
- context-obedient question answering
- theory of mind
- misc/general

This model was fine-tuned with a fork of FastChat, and therefore uses the standard vicuna template:


So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).

The most important bit, to me, is the context obedient question answering support, without extensive prompt engineering. ### Usage

The easiest way to get started is to use my fork of FastChat, which is mostly the same but allows for the increased context length and adds support for multi-line inputs:


Then, you can invoke it like so (after downloading the model): ### Context obedient question answering

By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question.  The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.

The format for a closed-context prompt is as follows:


It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.

*The __only__ prompts that need this closed context formating are closed-context instructions.  Normal questions/instructions do not!*

I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- 'BEGININPUT' - denotes a new input block
- 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block
- 'ENDCONTEXT' - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- 'ENDINPUT' - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- 'ENDINSTRUCTION' - denotes the end of instruction set

It sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.

Here's a trivial, but important example to prove the point:


And the response:


The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:



<details>
  <summary>A more elaborate example, with a rewrite of the Michigan Wikipedia article to be fake data.</summary>

  Prompt (not including vicuna format which would be needed):
  

  Response:
  

</details>

<details>
  <summary>Here's another example, which extracts from multiple input blocks and connects the two.</summary>

  Prompt:
  
  Response:
  

</details>

NOTE: Thanks /u/tareq_al_muntasir for testing and finding an issue with many questions and answer pairs in the context.  If you ask a question of a document with question answer pairs, it may continue generating beyond your actual question.  You can "fix" it by replacing question marks with periods in the input texts.  Or, you might be able to add a preamble to the prompt, like "Be sure to only respond to the instructions in the BEGININSTRUCTION block. ### Other stuff

The model is quite decent compared to other local models at generating code, writing, trivia, etc.  Give it a shot at anything, and let me know where it falls apart.