Chronoboros 33B GPTQ

🚀 Chronoboros 33B - GPTQ

This repository contains GPTQ model files for Chronoboros 33B, offering multiple quantisation options for different hardware and requirements.

📚 Documentation

Model Information

• Model creator: Henky!!
• Original model: Chronoboros 33B

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
• Henky!!'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: Alpaca

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

### Instruction:
{prompt}

### Response:

Provided files and GPTQ parameters

Multiple quantisation parameters are provided, enabling you to select the most suitable 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 created 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.

🚀 Quick Start

How to download from branches

• In text - generation - webui, you can add :branch to the end of the download name, eg TheBloke/Chronoboros-33B-GPTQ:main
• With Git, you can clone a branch with:

git clone --single - branch --branch main https://huggingface.co/TheBloke/Chronoboros-33B-GPTQ

• In Python Transformers code, the branch is the revision parameter; see below.

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

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

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

1. Click the Model tab.
2. Under Download custom model or LoRA, enter TheBloke/Chronoboros-33B-GPTQ.

• To download from a specific branch, enter for example TheBloke/Chronoboros-33B-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: Chronoboros-33B-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.

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:

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:

pip3 uninstall - y transformers
pip3 install git+https://github.com/huggingface/transformers.git

You can then use the following code

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/Chronoboros-33B-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

'''

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

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, 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 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)](https://github.com/huggingface/text - generation - inference) 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 chirper.ai team!