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# Manticore 13B Chat Pyg - GPTQ
- Model creator: [Open Access AI Collective](https://huggingface.co/openaccess-ai-collective)
- Original model: [Manticore 13B Chat Pyg](https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg)

<!-- description start -->
## Description

This repo contains GPTQ model files for [Open Access AI Collective's Manticore 13B Chat Pyg](https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg).

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.

<!-- description end -->
<!-- repositories-available start -->
## Repositories available

* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/manticore-13b-chat-pyg-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/manticore-13b-chat-pyg-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/manticore-13b-chat-pyg-GGUF)
* [Open Access AI Collective's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: Vicuna

```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:

```

<!-- prompt-template end -->


<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters

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.

All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.

<details>
  <summary>Explanation of GPTQ parameters</summary>

- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used.  Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.

</details>

| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/manticore-13b-chat-pyg-GPTQ/tree/main) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 7.45 GB | Yes | 4-bit, without Act Order and group size 128g. |

<!-- README_GPTQ.md-provided-files end -->

<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches

- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/manticore-13b-chat-pyg-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/manticore-13b-chat-pyg-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## 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're sure you know how to make a manual install.

1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/manticore-13b-chat-pyg-GPTQ`.
  - To download from a specific branch, enter for example `TheBloke/manticore-13b-chat-pyg-GPTQ:main`
  - 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: `manticore-13b-chat-pyg-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 and should not set manual 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!
<!-- README_GPTQ.md-text-generation-webui end -->

<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code

### Install the necessary packages

Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7
```

If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:

```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```

### For CodeLlama models only: you must use Transformers 4.33.0 or later.

If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```

### You can then use the following code

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/manticore-13b-chat-pyg-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             revision="main")

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

prompt = "Tell me about AI"
prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {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, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->

<!-- README_GPTQ.md-compatibility start -->
## Compatibility

The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).

[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->

<!-- footer start -->
<!-- 200823 -->
## 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!

Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

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**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov


Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

<!-- footer end -->

# Original model card: Open Access AI Collective's Manticore 13B Chat Pyg


# Manticore 13B Chat

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

Manticore 13B Chat builds on Manticore with new datasets, including a de-duped subset of the Pygmalion dataset. It also removes all Alpaca style prompts using `###` in favor of 
chat only style prompts using `USER:`,`ASSISTANT:` as well as [pygmalion/metharme prompting](https://huggingface.co/PygmalionAI/metharme-7b#prompting) using `<|system|>, <|user|> and <|model|>` tokens.

Questions, comments, feedback, looking to donate, or want to help? Reach out on our [Discord](https://discord.gg/PugNNHAF5r) or email [wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org)

# Training Datasets

Manticore 13B Chat is a Llama 13B model fine-tuned on the following datasets along with the datasets from the original Manticore 13B. 

**Manticore 13B Chat was trained on 25% of the datasets below. The datasets were merged, shuffled, and then sharded into 4 parts.**

- de-duped pygmalion dataset, filtered down to RP data
- [riddle_sense](https://huggingface.co/datasets/riddle_sense) - instruct augmented 
- hellaswag, updated for detailed explanations w 30K+ rows
- [gsm8k](https://huggingface.co/datasets/gsm8k) - instruct augmented 
- [ewof/code-alpaca-instruct-unfiltered](https://huggingface.co/datasets/ewof/code-alpaca-instruct-unfiltered)

Manticore 13B
- [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) - based on a cleaned and de-suped subset
- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
- [Wizard-Vicuna](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT)
- [GPT4-LLM-Cleaned](https://huggingface.co/datasets/teknium/GPT4-LLM-Cleaned)
- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct)
- ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the `train` split
- [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses, derived from the `train` split
- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses
- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization

Not added from Manticore 13B:
- mmlu - mmlu datasets were not added to this model as the `test` split is used for benchmarks

# Shoutouts

Special thanks to Nanobit for helping with Axolotl, TheBloke for quantizing these models are more accessible to all, ehartford for cleaned datasets, and 0x000011b for the RP dataset.
# Demo

Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality.
- https://huggingface.co/spaces/openaccess-ai-collective/manticore-13b-chat-pyg

## Release Notes

- https://wandb.ai/wing-lian/manticore-13b-v2/runs/hxr3aiiw

## Build

Manticore was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8xA100 80GB 
 - 3 epochs taking approximately 8 hours. No further epochs will be released. 
 - The configuration to duplicate this build is provided in this repo's [/config folder](https://huggingface.co/openaccess-ai-collective/manticore-13b/tree/main/configs).

## Bias, Risks, and Limitations
Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information.

## Examples

```
USER: make up a sonnet in the style of shakespeare about a skier skiing in the glades on a bluebird day.
ASSISTANT: A skier doth glide 'neath sun’s glow, His edges sharp to cut the snow, Bluebird sky above, While he doth revel on the slopes below, The cold air biting his nose, But not his spirits, For he is filled with a sense of joy, As he carves through the powdery drifts. His body moves in graceful rhythm, And each turn he makes with ease, He feels like he could ski forever, With no care for the world outside these glades.
```

```
USER: tell me a joke about alpacas
ASSISTANT: Did you hear about the famous alpaca ballet dancer? She was a prima llama.
USER: okay, that is actually pretty good
ASSISTANT: What do you call an alpaca in a bowtie? A llama-cholla!
USER: why is that funny?
ASSISTANT: Because it’s a play on words. “Llama-cholla” sounds like “lame chola,” which is Spanish slang for someone who acts cool or pretentious. So, the joke is implying that alpacas are vain and act cool with their bowties.
```

```
USER: What is 44 + 77?
ASSISTANT: 121
USER: What is the sum of 33 and 77?
ASSISTANT: 110
USER: tell me 5 words that start with “ex” and the sum of 33 and 77.
ASSISTANT: exotic, expressive, explicit, experimental, and extravagant. The sum of 33 and 77 is 110.
USER: tell me 3 words that start with “egr” and end in “s”
ASSISTANT: egret, eggplant, and egrets.
```
- oof


```
USER: Sally has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?
ASSISTANT: Sally has a total of 6 siblings, so she has a total of 6 - 2 = 4 sisters.
USER: Sally has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have? Use multi-step reasoning to explain your answer.
ASSISTANT: Sally has a total of three brothers, so she has a total of three * 2 = six siblings. Her family consists of three boys and six girls in total. This means that Sally has three sisters since there are six female relatives and only three male ones.
USER: But when brothers have sisters, aren’t they shared or the same?
ASSISTANT: No, not every sibling shares their mother. For example, I have two brothers who share one mother but my third brother has a different mother from both of them.
USER: Let’s assume they all share the same mother and father. Now explain with multi-step reasoning.
ASSISTANT: If Sally has three brothers then that means she has six siblings altogether. Each brother has two sisters, which means that there are twelve female relatives in her family - one for each of her brothers and herself. Since there are only six females in the entire family, that means that Sally’s mother has no other children than her own three sons - so she can’t be the mother of any of the sisters. So all those sisters have to belong to one of the brothers, and since they are shared by two brothers, then each brother has a single sister. Therefore, there is only one sister for Sally, making the final answer 1.
```
- pretty sure that reasoning isn't correct, and the final answer of having 1 sister is a lucky guess

{"language": ["en"], "license": "other", "library_name": "transformers", "datasets": ["anon8231489123/ShareGPT_Vicuna_unfiltered", "ehartford/wizard_vicuna_70k_unfiltered", "ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered", "QingyiSi/Alpaca-CoT", "teknium/GPT4-LLM-Cleaned", "teknium/GPTeacher-General-Instruct", "metaeval/ScienceQA_text_only", "hellaswag", "openai/summarize_from_feedback", "riddle_sense", "gsm8k", "ewof/code-alpaca-instruct-unfiltered"], "model_name": "Manticore 13B Chat Pyg", "base_model": "openaccess-ai-collective/manticore-13b-chat-pyg", "inference": false, "model_creator": "Open Access AI Collective", "model_type": "llama", "pipeline_tag": "text-generation", "prompt_template": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:\n", "quantized_by": "TheBloke"}
transformers safetensors llama text-generation en dataset:anon8231489123/ShareGPT_Vicuna_unfiltered dataset:ehartford/wizard_vicuna_70k_unfiltered dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered dataset:QingyiSi/Alpaca-CoT dataset:teknium/GPT4-LLM-Cleaned dataset:teknium/GPTeacher-General-Instruct dataset:metaeval/ScienceQA_text_only dataset:hellaswag dataset:openai/summarize_from_feedback dataset:riddle_sense dataset:gsm8k dataset:ewof/code-alpaca-instruct-unfiltered base_model:openaccess-ai-collective/manticore-13b-chat-pyg license:other autotrain_compatible text-generation-inference 4-bit region:us
TAGS
#transformers #safetensors #llama #text-generation #en #dataset-anon8231489123/ShareGPT_Vicuna_unfiltered #dataset-ehartford/wizard_vicuna_70k_unfiltered #dataset-ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered #dataset-QingyiSi/Alpaca-CoT #dataset-teknium/GPT4-LLM-Cleaned #dataset-teknium/GPTeacher-General-Instruct #dataset-metaeval/ScienceQA_text_only #dataset-hellaswag #dataset-openai/summarize_from_feedback #dataset-riddle_sense #dataset-gsm8k #dataset-ewof/code-alpaca-instruct-unfiltered #base_model-openaccess-ai-collective/manticore-13b-chat-pyg #license-other #autotrain_compatible #text-generation-inference #4-bit #region-us 


![](https://i.URL alt=)



[[TheBloke's LLM work is generously supported by a grant from [andreessen horowitz (a16z)](URL)](URL to contribute? TheBloke's Patreon page</a></p>
    </div>
</div>
<div style=)](URL & support: TheBloke's Discord server</a></p>
    </div>
    <div style=)




---


Manticore 13B Chat Pyg - GPTQ
=============================


* Model creator: Open Access AI Collective
* Original model: Manticore 13B Chat Pyg


Description
-----------


This repo contains GPTQ model files for Open Access AI Collective's Manticore 13B Chat Pyg.


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
----------------------


* AWQ model(s) for GPU inference.
* GPTQ models for GPU inference, with multiple quantisation parameter options.
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Open Access AI Collective's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions


Prompt template: Vicuna
-----------------------


Provided files and GPTQ parameters
----------------------------------


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.


All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the 'main' branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.



Explanation of GPTQ parameters
* Bits: The bit size of the quantised model.
* GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
* Act Order: True or False. Also known as 'desc\_act'. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
* Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
* GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
* Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
* ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.




How to download from branches
-----------------------------


* In text-generation-webui, you can add ':branch' to the end of the download name, eg 'TheBloke/manticore-13b-chat-pyg-GPTQ:main'
* With Git, you can clone a branch with:
* In Python Transformers code, the branch is the 'revision' parameter; see below.


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.


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


1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/manticore-13b-chat-pyg-GPTQ'.


* To download from a specific branch, enter for example 'TheBloke/manticore-13b-chat-pyg-GPTQ:main'
* 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: 'manticore-13b-chat-pyg-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 and should not set manual 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
-------------------------------------------


### Install the necessary packages


Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.


If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:


### For CodeLlama models only: you must use Transformers 4.33.0 or later.


If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:


### You can then use the following code


Compatibility
-------------


The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with Occ4m's GPTQ-for-LLaMa fork.


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


Huggingface Text Generation Inference (TGI) is compatible with all GPTQ models.


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!


Thanks to Clay from URL!


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: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie\_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S\_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, URL, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov


Thank you to all my generous patrons and donaters!


And thank you again to a16z for their generous grant.


Original model card: Open Access AI Collective's Manticore 13B Chat Pyg
=======================================================================


Manticore 13B Chat
==================


<img src="URL alt="Built with Axolotl" width="200" height="32"/>


Manticore 13B Chat builds on Manticore with new datasets, including a de-duped subset of the Pygmalion dataset. It also removes all Alpaca style prompts using '###' in favor of
chat only style prompts using 'USER:','ASSISTANT:' as well as pygmalion/metharme prompting using '<|system|>, <|user|> and <|model|>' tokens.


Questions, comments, feedback, looking to donate, or want to help? Reach out on our Discord or email wing@URL


Training Datasets
=================


Manticore 13B Chat is a Llama 13B model fine-tuned on the following datasets along with the datasets from the original Manticore 13B.


Manticore 13B Chat was trained on 25% of the datasets below. The datasets were merged, shuffled, and then sharded into 4 parts.


* de-duped pygmalion dataset, filtered down to RP data
* riddle\_sense - instruct augmented
* hellaswag, updated for detailed explanations w 30K+ rows
* gsm8k - instruct augmented
* ewof/code-alpaca-instruct-unfiltered


Manticore 13B


* ShareGPT - based on a cleaned and de-suped subset
* WizardLM
* Wizard-Vicuna
* subset of QingyiSi/Alpaca-CoT for roleplay and CoT
* GPT4-LLM-Cleaned
* GPTeacher-General-Instruct
* ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the 'train' split
* hellaswag - 5K row subset of instruct augmented for concise responses, derived from the 'train' split
* metaeval/ScienceQA\_text\_only - instruct for concise responses
* openai/summarize\_from\_feedback - instruct augmented tl;dr summarization


Not added from Manticore 13B:


* mmlu - mmlu datasets were not added to this model as the 'test' split is used for benchmarks


Shoutouts
=========


Special thanks to Nanobit for helping with Axolotl, TheBloke for quantizing these models are more accessible to all, ehartford for cleaned datasets, and 0x000011b for the RP dataset.


Demo
====


Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality.


* URL


Release Notes
-------------


* URL


Build
-----


Manticore was built with Axolotl on 8xA100 80GB


* 3 epochs taking approximately 8 hours. No further epochs will be released.
* The configuration to duplicate this build is provided in this repo's /config folder.


Bias, Risks, and Limitations
----------------------------


Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information.


Examples
--------


* oof
* pretty sure that reasoning isn't correct, and the final answer of having 1 sister is a lucky guess



### Install the necessary packages


Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.


If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ### For CodeLlama models only: you must use Transformers 4.33.0 or later.


If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ### You can then use the following code


Compatibility
-------------


The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with Occ4m's GPTQ-for-LLaMa fork.


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


Huggingface Text Generation Inference (TGI) is compatible with all GPTQ models.


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!


Thanks to Clay from URL!


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: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie\_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S\_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, URL, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov


Thank you to all my generous patrons and donaters!


And thank you again to a16z for their generous grant.


Original model card: Open Access AI Collective's Manticore 13B Chat Pyg
=======================================================================


Manticore 13B Chat
==================


<img src="URL alt="Built with Axolotl" width="200" height="32"/>


Manticore 13B Chat builds on Manticore with new datasets, including a de-duped subset of the Pygmalion dataset. It also removes all Alpaca style prompts using '###' in favor of
chat only style prompts using 'USER:','ASSISTANT:' as well as pygmalion/metharme prompting using '<|system|>, <|user|> and <|model|>' tokens.


Questions, comments, feedback, looking to donate, or want to help? Reach out on our Discord or email wing@URL


Training Datasets
=================


Manticore 13B Chat is a Llama 13B model fine-tuned on the following datasets along with the datasets from the original Manticore 13B.


Manticore 13B Chat was trained on 25% of the datasets below. The datasets were merged, shuffled, and then sharded into 4 parts.


* de-duped pygmalion dataset, filtered down to RP data
* riddle\_sense - instruct augmented
* hellaswag, updated for detailed explanations w 30K+ rows
* gsm8k - instruct augmented
* ewof/code-alpaca-instruct-unfiltered


Manticore 13B


* ShareGPT - based on a cleaned and de-suped subset
* WizardLM
* Wizard-Vicuna
* subset of QingyiSi/Alpaca-CoT for roleplay and CoT
* GPT4-LLM-Cleaned
* GPTeacher-General-Instruct
* ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the 'train' split
* hellaswag - 5K row subset of instruct augmented for concise responses, derived from the 'train' split
* metaeval/ScienceQA\_text\_only - instruct for concise responses
* openai/summarize\_from\_feedback - instruct augmented tl;dr summarization


Not added from Manticore 13B:


* mmlu - mmlu datasets were not added to this model as the 'test' split is used for benchmarks


Shoutouts
=========


Special thanks to Nanobit for helping with Axolotl, TheBloke for quantizing these models are more accessible to all, ehartford for cleaned datasets, and 0x000011b for the RP dataset.


Demo
====


Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality.


* URL


Release Notes
-------------


* URL


Build
-----


Manticore was built with Axolotl on 8xA100 80GB


* 3 epochs taking approximately 8 hours. No further epochs will be released.
* The configuration to duplicate this build is provided in this repo's /config folder.


Bias, Risks, and Limitations
----------------------------


Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information.


Examples
--------


* oof
* pretty sure that reasoning isn't correct, and the final answer of having 1 sister is a lucky guess
TAGS
#transformers #safetensors #llama #text-generation #en #dataset-anon8231489123/ShareGPT_Vicuna_unfiltered #dataset-ehartford/wizard_vicuna_70k_unfiltered #dataset-ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered #dataset-QingyiSi/Alpaca-CoT #dataset-teknium/GPT4-LLM-Cleaned #dataset-teknium/GPTeacher-General-Instruct #dataset-metaeval/ScienceQA_text_only #dataset-hellaswag #dataset-openai/summarize_from_feedback #dataset-riddle_sense #dataset-gsm8k #dataset-ewof/code-alpaca-instruct-unfiltered #base_model-openaccess-ai-collective/manticore-13b-chat-pyg #license-other #autotrain_compatible #text-generation-inference #4-bit #region-us 
 ### Install the necessary packages


Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.


If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ### For CodeLlama models only: you must use Transformers 4.33.0 or later.


If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ### You can then use the following code


Compatibility
-------------


The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with Occ4m's GPTQ-for-LLaMa fork.


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


Huggingface Text Generation Inference (TGI) is compatible with all GPTQ models.


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!


Thanks to Clay from URL!


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: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie\_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S\_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, URL, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov


Thank you to all my generous patrons and donaters!


And thank you again to a16z for their generous grant.


Original model card: Open Access AI Collective's Manticore 13B Chat Pyg
=======================================================================


Manticore 13B Chat
==================


<img src="URL alt="Built with Axolotl" width="200" height="32"/>


Manticore 13B Chat builds on Manticore with new datasets, including a de-duped subset of the Pygmalion dataset. It also removes all Alpaca style prompts using '###' in favor of
chat only style prompts using 'USER:','ASSISTANT:' as well as pygmalion/metharme prompting using '<|system|>, <|user|> and <|model|>' tokens.


Questions, comments, feedback, looking to donate, or want to help? Reach out on our Discord or email wing@URL


Training Datasets
=================


Manticore 13B Chat is a Llama 13B model fine-tuned on the following datasets along with the datasets from the original Manticore 13B.


Manticore 13B Chat was trained on 25% of the datasets below. The datasets were merged, shuffled, and then sharded into 4 parts.


* de-duped pygmalion dataset, filtered down to RP data
* riddle\_sense - instruct augmented
* hellaswag, updated for detailed explanations w 30K+ rows
* gsm8k - instruct augmented
* ewof/code-alpaca-instruct-unfiltered


Manticore 13B


* ShareGPT - based on a cleaned and de-suped subset
* WizardLM
* Wizard-Vicuna
* subset of QingyiSi/Alpaca-CoT for roleplay and CoT
* GPT4-LLM-Cleaned
* GPTeacher-General-Instruct
* ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the 'train' split
* hellaswag - 5K row subset of instruct augmented for concise responses, derived from the 'train' split
* metaeval/ScienceQA\_text\_only - instruct for concise responses
* openai/summarize\_from\_feedback - instruct augmented tl;dr summarization


Not added from Manticore 13B:


* mmlu - mmlu datasets were not added to this model as the 'test' split is used for benchmarks


Shoutouts
=========


Special thanks to Nanobit for helping with Axolotl, TheBloke for quantizing these models are more accessible to all, ehartford for cleaned datasets, and 0x000011b for the RP dataset.


Demo
====


Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality.


* URL


Release Notes
-------------


* URL


Build
-----


Manticore was built with Axolotl on 8xA100 80GB


* 3 epochs taking approximately 8 hours. No further epochs will be released.
* The configuration to duplicate this build is provided in this repo's /config folder.


Bias, Risks, and Limitations
----------------------------


Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information.


Examples
--------


* oof
* pretty sure that reasoning isn't correct, and the final answer of having 1 sister is a lucky guess