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# Vicuna 13B CoT - GPTQ
- Model creator: [Shuaijie She](https://huggingface.co/kevinpro)
- Original model: [Vicuna 13B CoT](https://huggingface.co/kevinpro/Vicuna-13B-CoT)

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

This repo contains GPTQ model files for [Shuaijie She's Vicuna 13B CoT](https://huggingface.co/kevinpro/Vicuna-13B-CoT).

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/Vicuna-13B-CoT-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Vicuna-13B-CoT-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Vicuna-13B-CoT-GGUF)
* [Shuaijie She's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Vicuna-13B-CoT-fp16)
<!-- 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/Vicuna-13B-CoT-GPTQ/tree/main) | 4 | 128 | No | 0.01 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 8.11 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/Vicuna-13B-CoT-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/Vicuna-13B-CoT-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/Vicuna-13B-CoT-GPTQ`.
  - To download from a specific branch, enter for example `TheBloke/Vicuna-13B-CoT-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: `Vicuna-13B-CoT-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/Vicuna-13B-CoT-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: Shuaijie She's Vicuna 13B CoT


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# Kevin Pro's Vicuna 13B CoT fp16

These files are pytorch format fp16 model files for [Kevin Pro's Vicuna 13B CoT](https://huggingface.co/kevinpro/Vicuna-13B-CoT).

It is the result of merging and/or converting the source repository to float16.

## Repositories available

* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Vicuna-13B-CoT-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Vicuna-13B-CoT-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Vicuna-13B-CoT-fp16)

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

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

[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)

## 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**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

**Patreon special mentions**: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.

Thank you to all my generous patrons and donaters!

<!-- footer end -->

# Original model card: Kevin Pro's Vicuna 13B CoT

# Model Card for Model ID
SFT to enhance the CoT capabiliy of Vicuna

If you find the model helpful, please click "like" to support us. 
We also welcome feedback on your usage experience and any issues you encounter in the issues section.

Another 7B version: https://huggingface.co/kevinpro/Vicuna-7B-CoT


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

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## Model Card Contact

[More Information Needed]



{"license": "other", "model_name": "Vicuna 13B CoT", "base_model": "kevinpro/Vicuna-13B-CoT", "inference": false, "model_creator": "Shuaijie She", "model_type": "llama", "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 arxiv:1910.09700 base_model:kevinpro/Vicuna-13B-CoT license:other autotrain_compatible text-generation-inference 4-bit region:us
TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #base_model-kevinpro/Vicuna-13B-CoT #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=)




---


Vicuna 13B CoT - GPTQ
=====================


* Model creator: Shuaijie She
* Original model: Vicuna 13B CoT


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


This repo contains GPTQ model files for Shuaijie She's Vicuna 13B CoT.


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
* Shuaijie She'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/Vicuna-13B-CoT-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/Vicuna-13B-CoT-GPTQ'.


* To download from a specific branch, enter for example 'TheBloke/Vicuna-13B-CoT-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: 'Vicuna-13B-CoT-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: Shuaijie She's Vicuna 13B CoT
==================================================



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



[<a href="URL to contribute? TheBloke's Patreon page</a>](URL & support: my new Discord server</a></p>
    </div>
    <div style=)



Kevin Pro's Vicuna 13B CoT fp16
===============================


These files are pytorch format fp16 model files for Kevin Pro's Vicuna 13B CoT.


It is the result of merging and/or converting the source repository to float16.


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


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: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.


Patreon special mentions: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.


Thank you to all my generous patrons and donaters!


Original model card: Kevin Pro's Vicuna 13B CoT
===============================================


Model Card for Model ID
=======================


SFT to enhance the CoT capabiliy of Vicuna


If you find the model helpful, please click "like" to support us.
We also welcome feedback on your usage experience and any issues you encounter in the issues section.


Another 7B version: URL


Model Details
-------------


### Model Description


* Developed by:
* Shared by [optional]:
* Model type:
* Language(s) (NLP):
* License:
* Finetuned from model [optional]:


### Model Sources [optional]


* Repository:
* Paper [optional]:
* Demo [optional]:


Uses
----


### Direct Use


### Downstream Use [optional]


### Out-of-Scope Use


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


### Recommendations


Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
----------------


### Training Data


### Training Procedure


#### Preprocessing [optional]


#### Training Hyperparameters


* Training regime:


#### Speeds, Sizes, Times [optional]


Evaluation
----------


### Testing Data, Factors & Metrics


#### Testing Data


#### Factors


#### Metrics


### Results


#### Summary


Model Examination [optional]
----------------------------


Environmental Impact
--------------------


Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).


* Hardware Type:
* Hours used:
* Cloud Provider:
* Compute Region:
* Carbon Emitted:


Technical Specifications [optional]
-----------------------------------


### Model Architecture and Objective


### Compute Infrastructure


#### Hardware


#### Software


[optional]


BibTeX:


APA:


Glossary [optional]
-------------------


More Information [optional]
---------------------------


Model Card Authors [optional]
-----------------------------


Model Card Contact
------------------



### 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: Shuaijie She's Vicuna 13B CoT
==================================================



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



[<a href="URL to contribute? TheBloke's Patreon page</a>](URL & support: my new Discord server</a></p>
    </div>
    <div style=)



Kevin Pro's Vicuna 13B CoT fp16
===============================


These files are pytorch format fp16 model files for Kevin Pro's Vicuna 13B CoT.


It is the result of merging and/or converting the source repository to float16.


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


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: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.


Patreon special mentions: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.


Thank you to all my generous patrons and donaters!


Original model card: Kevin Pro's Vicuna 13B CoT
===============================================


Model Card for Model ID
=======================


SFT to enhance the CoT capabiliy of Vicuna


If you find the model helpful, please click "like" to support us.
We also welcome feedback on your usage experience and any issues you encounter in the issues section.


Another 7B version: URL


Model Details
------------- ### Model Description


* Developed by:
* Shared by [optional]:
* Model type:
* Language(s) (NLP):
* License:
* Finetuned from model [optional]: ### Model Sources [optional]


* Repository:
* Paper [optional]:
* Demo [optional]:


Uses
---- ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use


Bias, Risks, and Limitations
---------------------------- ### Recommendations


Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
---------------- ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters


* Training regime: #### Speeds, Sizes, Times [optional]


Evaluation
---------- ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary


Model Examination [optional]
----------------------------


Environmental Impact
--------------------


Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).


* Hardware Type:
* Hours used:
* Cloud Provider:
* Compute Region:
* Carbon Emitted:


Technical Specifications [optional]
----------------------------------- ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software


[optional]


BibTeX:


APA:


Glossary [optional]
-------------------


More Information [optional]
---------------------------


Model Card Authors [optional]
-----------------------------


Model Card Contact
------------------
TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #base_model-kevinpro/Vicuna-13B-CoT #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: Shuaijie She's Vicuna 13B CoT
==================================================



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



[<a href="URL to contribute? TheBloke's Patreon page</a>](URL & support: my new Discord server</a></p>
    </div>
    <div style=)



Kevin Pro's Vicuna 13B CoT fp16
===============================


These files are pytorch format fp16 model files for Kevin Pro's Vicuna 13B CoT.


It is the result of merging and/or converting the source repository to float16.


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


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: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.


Patreon special mentions: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.


Thank you to all my generous patrons and donaters!


Original model card: Kevin Pro's Vicuna 13B CoT
===============================================


Model Card for Model ID
=======================


SFT to enhance the CoT capabiliy of Vicuna


If you find the model helpful, please click "like" to support us.
We also welcome feedback on your usage experience and any issues you encounter in the issues section.


Another 7B version: URL


Model Details
------------- ### Model Description


* Developed by:
* Shared by [optional]:
* Model type:
* Language(s) (NLP):
* License:
* Finetuned from model [optional]: ### Model Sources [optional]


* Repository:
* Paper [optional]:
* Demo [optional]:


Uses
---- ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use


Bias, Risks, and Limitations
---------------------------- ### Recommendations


Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
---------------- ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters


* Training regime: #### Speeds, Sizes, Times [optional]


Evaluation
---------- ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary


Model Examination [optional]
----------------------------


Environmental Impact
--------------------


Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).


* Hardware Type:
* Hours used:
* Cloud Provider:
* Compute Region:
* Carbon Emitted:


Technical Specifications [optional]
----------------------------------- ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software


[optional]


BibTeX:


APA:


Glossary [optional]
-------------------


More Information [optional]
---------------------------


Model Card Authors [optional]
-----------------------------


Model Card Contact
------------------