TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
Tess XS Creative V1.0 - GPTQ
- Model creator: Migel Tissera
- Original model: Tess XS Creative V1.0
Description
This repo contains GPTQ model files for Migel Tissera's Tess XS Creative V1.0.
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.
These files were quantised using hardware kindly provided by Massed Compute.
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
- Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Orca-Vicuna
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
This may not be a complete list; if you know of others, please let me know!
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.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
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 calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration 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 and Mistral models in 4-bit.
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
|---|---|---|---|---|---|---|---|---|---|
| main | 4 | 128 | Yes | 0.1 | open-instruct | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| gptq-4bit-32g-actorder_True | 4 | 32 | Yes | 0.1 | open-instruct | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| gptq-8bit--1g-actorder_True | 8 | None | Yes | 0.1 | open-instruct | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| gptq-8bit-128g-actorder_True | 8 | 128 | Yes | 0.1 | open-instruct | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| gptq-8bit-32g-actorder_True | 8 | 32 | Yes | 0.1 | open-instruct | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| gptq-4bit-64g-actorder_True | 4 | 64 | Yes | 0.1 | open-instruct | 4096 | 4.29 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
How to download, including from branches
In text-generation-webui
To download from the main branch, enter TheBloke/Tess-XS-Creative-v1.0-GPTQ in the "Download model" box.
To download from another branch, add :branchname to the end of the download name, eg TheBloke/Tess-XS-Creative-v1.0-GPTQ:gptq-4bit-32g-actorder_True
From the command line
I recommend using the huggingface-hub Python library:
pip3 install huggingface-hub
To download the main branch to a folder called Tess-XS-Creative-v1.0-GPTQ:
mkdir Tess-XS-Creative-v1.0-GPTQ
huggingface-cli download TheBloke/Tess-XS-Creative-v1.0-GPTQ --local-dir Tess-XS-Creative-v1.0-GPTQ --local-dir-use-symlinks False
To download from a different branch, add the --revision parameter:
mkdir Tess-XS-Creative-v1.0-GPTQ
huggingface-cli download TheBloke/Tess-XS-Creative-v1.0-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Tess-XS-Creative-v1.0-GPTQ --local-dir-use-symlinks False
More advanced huggingface-cli download usage
If you remove the --local-dir-use-symlinks False parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: ~/.cache/huggingface), and symlinks will be added to the specified --local-dir, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the HF_HOME environment variable, and/or the --cache-dir parameter to huggingface-cli.
For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:
pip3 install hf_transfer
And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:
mkdir Tess-XS-Creative-v1.0-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Tess-XS-Creative-v1.0-GPTQ --local-dir Tess-XS-Creative-v1.0-GPTQ --local-dir-use-symlinks False
Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.
With git (not recommended)
To clone a specific branch with git, use a command like this:
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Tess-XS-Creative-v1.0-GPTQ
Note that using Git with HF repos is strongly discouraged. It will be much slower than using huggingface-hub, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the .git folder as a blob.)
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.
Click the Model tab.
Under Download custom model or LoRA, enter
TheBloke/Tess-XS-Creative-v1.0-GPTQ.- To download from a specific branch, enter for example
TheBloke/Tess-XS-Creative-v1.0-GPTQ:gptq-4bit-32g-actorder_True - see Provided Files above for the list of branches for each option.
- To download from a specific branch, enter for example
Click Download.
The model will start downloading. Once it's finished it will say "Done".
In the top left, click the refresh icon next to Model.
In the Model dropdown, choose the model you just downloaded:
Tess-XS-Creative-v1.0-GPTQThe model will automatically load, and is now ready for use!
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.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file
Once you're ready, click the Text Generation tab and enter a prompt to get started!
Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0
Example Docker parameters:
--model-id TheBloke/Tess-XS-Creative-v1.0-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
pip3 install huggingface-hub
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
Python code example: inference from this GPTQ model
Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
Example Python code
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Tess-XS-Creative-v1.0-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
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'''SYSTEM: {system_message}
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'])
Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
ExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
Discord
For further support, and discussions on these models and AI in general, join us at:
Thanks, and how to contribute
Thanks to the chirper.ai team!
Thanks to Clay from gpus.llm-utils.org!
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: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Migel Tissera's Tess XS Creative V1.0
Depracated By: https://huggingface.co/migtissera/Tess-XS-v1.1
Tess
Tess, short for Tessoro/Tessoso, is a general purpose Large Language Model series. Tess-XS-v1.0 was trained on the Mistral-7B base.
Prompt Format:
SYSTEM: <ANY SYSTEM CONTEXT>
USER: What is the relationship between Earth's atmosphere, magnetic field and gravity?
ASSISTANT:
Sample Conversation
You: What's the relationship between an electric field and a magnetic field, given the same time and location?
To fully understand the relationship between electric fields and magnetic fields, we need to consider the phenomenon of electromagnetism, which is the interaction between electric charges and currents that gives rise to both electric and magnetic fields.
Electric fields are created by stationary electric charges or by changing magnetic fields. A stationary electric charge produces an electric field that extends outward in all directions from the charge. The strength of the electric field at any point depends on the charge and the distance from the charge. The direction of the electric field lines is determined by the convention that positive charges attract negative charges and repel other positive charges.
Magnetic fields, on the other hand, are created by moving electric charges and by changes in electric fields. A moving electric charge creates a magnetic field that surrounds the charge, and it flows in a circular motion. The magnetic field lines are usually depicted as spiraling outward from the moving charge. The strength of the magnetic field at any point depends on the current and the distance from the current-carrying wire.
The relationship between electric and magnetic fields is described by Maxwell's equations, which are a set of differential equations governing the behavior of electromagnetic fields. One of Maxwell's equations, the Faraday's law of induction, states that a changing magnetic field induces an electric field. This means that changes in the magnetic field can cause electric fields to be created, and vice versa.
In summary, electric and magnetic fields are intimately related, and one cannot exist without the other. A changing electric field creates a magnetic field, and a changing magnetic field creates an electric field. The relationship between them is described by Maxwell's equations, which form the foundation of classical electromagnetism.
You: The known physics states that the electric field and the magnetic field for a given object are proportional to each other. i.e. an increase in the magnetic field will result in an increase in the corresponding electric field. But in some UAP events, we have observed that a decrease in electric field actually causing an increase in the corresponding magnetic field. This is not a fault in our measurements. Can we theorize what could be the case here?
The statement that an increase in the magnetic field will result in an increase in the corresponding electric field is not correct. It's important to understand that the electric field and the magnetic field are not proportional to each other, and they don't always follow a simple relationship. The relationship between the electric and magnetic fields is more complex and is described by Maxwell's equations, which include the Faraday's law of induction, which states that a changing magnetic field induces an electric field, and vice versa.
However, it's not clear whether you are referring to a specific event or a general observation about UAPs. If you are referring to a particular event in which a decreased electric field is observed, and then an increased magnetic field is observed, this could be due to a number of factors. It's possible that the observation is an artifact of the measurement process, or it could be caused by a phenomenon that is not fully understood yet.
It's important to note that our understanding of electromagnetism is based on our current scientific knowledge, and there may be limitations to our understanding of specific phenomena. Additionally, the field of unidentified aerial phenomena (UAP) is still in its infancy, and there is much we do not yet know about these types of events.
If you are referring to a specific event that you believe demonstrates this relationship, it would be beneficial to provide more details about the observation and the measurement process, as well as any additional context that could help explain the apparent phenomenon.
You: Hang on, so the Maxwell's equations don't actually say that the electric and magnetic fields are proportional to each other?
No, Maxwell's equations do not state that the electric and magnetic fields are proportional to each other. They describe the relationship between electric and magnetic fields, but the relationship is more complex than a simple proportionality. Maxwell's equations include the Faraday's law of induction, which states that a changing magnetic field induces an electric field, and this electric field results from the magnetic field changing with time.
To simplify the relationship, it might be helpful to consider that a magnetic field can be created by an electric current or by a changing electric field. Similarly, an electric field can be created by a magnetic field that is changing with time. However, the relationship between the electric and magnetic fields is not linear, and it is governed by the laws of electromagnetism.
It's important to note that the statement about the proportion of electric and magnetic fields is an oversimplification. The relationship between them is more accurately described by the complex equations of electromagnetism, which include Maxwell's equations.
You: Thank you for clarifying, that's really helpful. Can you explain to me the complex equations of electromagnetism now?
The complex equations of electromagnetism are Maxwell's equations, which include four partial differential equations that describe the behavior of electric and magnetic fields. These equations are:
- Gauss's law for electricity:
∇⋅E = 4πκρ where ρ is the electric charge density.
- Gauss's law for magnetism:
∇⋅B = 0 (because there are no magnetic monopoles).
- Faraday's law of induction:
∇ × E = -∂B/∂t which gives us the electric field of a changing magnetic field.
- Ampère-Maxwell law:
∇ × B = μ0(j + ε0∂E/∂t) which gives us the magnetic field of a changing electric field.
These equations, along with specific boundary conditions, completely describe the interaction of electric and magnetic fields in the classical theory of electromagnetism. They are complex because they involve the derivatives of the fields with respect to time and space, and they must be solved to understand the behavior of electromagnetic phenomena.
You: I notice that you put a value for the permittivity of free space in Gauss's law for electricity. Can you write the equation in general terms?
∇⋅E = 4πκρ
This is the general form of Gauss's law for electricity. κ is the electric constant, which has the value 8.854187817... × 10^-12 F/m (or C²/Nm²) in SI units. This value is often called the permittivity of free space, but it is a dimensionless constant that relates the electric field to the charge density.
In this equation, the divergence of the electric field (∇⋅E) is equal to the charge density (ρ) multiplied by 4πκ. The divergence is a measure of how much the electric field points towards charges. If there is a positive charge, the electric field will point inward. If there is a negative charge, the electric field will point outward.
This equation is the same as Coulomb's law but in a more compact and general form. It can be used to predict the behavior of electric fields in any situation involving charges, whether they are static or moving.
You: Thank you! You're great. Now can we move on to Quantum domain. What is the leading theory of electromagnetism in quantum physics?
Quantum mechanics is the leading theory of electromagnetism in the quantum domain. It provides a framework for understanding the behavior of subatomic particles and fields, including electromagnetism.
In quantum mechanics, electromagnetic fields are described by quantum fields, which are mathematical constructs that represent the energy and momentum of the field. These fields are quantized, meaning that they can only exist in discrete amounts as particles called photons.
The most fundamental principle in quantum mechanics is the uncertainty principle, which states that it is impossible to measure both the position and the momentum of a particle with arbitrary precision. This principle has implications for how we understand the behavior of electromagnetic fields at the quantum level.
Quantum mechanics also predicts the existence of virtual particles, which are temporary quantum fluctuations that disappear almost immediately after they appear. These virtual particles can interact with each other and with real particles, leading to a complex interplay that affects the behavior of electromagnetic fields.
In summary, quantum mechanics provides a framework for understanding electromagnetism in the quantum domain. It replaces classical concepts of fields with a quantum mechanical description that includes uncertainty, virtual particles, and a quantized interpretation of the field.
You: What is QED?
QED stands for Quantum Electrodynamics. It is a theory in quantum field theory that describes the interactions between electrons and photons. It is based on quantum mechanics and special relativity, and it is one of the most successful theories in physics, having made many precise predictions that have been confirmed by experiment.
In QED, electrons and photons are treated as quantum particles, which means they have wave-particle duality and can exhibit both wave-like and particle-like properties. The theory also accounts for the interaction between these particles.
QED is characterized by the fine structure constant, which is a dimensionless constant that describes the strength of the electromagnetic force. It is very small, which indicates that the electromagnetic force is much weaker than the other fundamental forces.
The theory has been tested extensively and has stood up to every experimental challenge. It has been crucial in the development of modern technology, such as lasers and semiconductors, and it continues to be an important tool for understanding high-energy processes in particle physics.
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