# BigScience's BLOOMZ 176B GPTQ
These files are GPTQ 4bit model files for [BigScience's BLOOMZ](https://huggingface.co/bigscience/bloomz).
It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
**This is a BIG model! 2 x 80GB or 3 x 48GB GPUs are required**
## Important note: files must be joined before use
It is not currently possible to shard GPTQ files, therefore the model file is one single 94 GB `safetensors` file.
Huggingface Hub has a 50GB per-file limit. I have therefore been forced to split the file in to three parts for upload.
I did this using the simple *nix command `split`.
To join the files on any *nix system, run:
```
cat gptq_model-4bit--1g.JOINBEFOREUSE.split-*.safetensors > gptq_model-4bit--1g.safetensors
```
To join the files on Windows, open a Command Prompt and run:
```
COPY /B gptq_model-4bit--1g.JOINBEFOREUSE.split-a.safetensors + gptq_model-4bit--1g.JOINBEFOREUSE.split-b.safetensors + gptq_model-4bit--1g.JOINBEFOREUSE.split-c.safetensors gptq_model-4bit--1g.safetensors
```
Or for Python code for joining the files, see the Python section below.
The SHA256SUM of the joined file will be:
```
50baeab9859362d22df6f822f158b9ba75b44ffc6605b715992fe6245aa6e93a gptq_model-4bit--1g.safetensors
```
Once you have the joined file, you can safely delete `gptq_model-4bit--1g.JOINBEFOREUSE.split-*.safetensors`.
## Repositories available
* [4-bit GPTQ model for GPU inference](https://huggingface.co/TheBlokeAI/bloomz-175B-GPTQ)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/bigscience/bloomz)
## Two files provided - separate branches
- Main branch: `gptq_model-4bit--1g.safetensors`
- Group Size = None
- Desc Act (act-order) = True
- This version will use the least possible VRAM, and should have higher inference performance in CUDA mode
- Branch `group_size_128g`: `gptq_model-4bit-128g.safetensors`
- Group Size = 128g
- Desc Act (act-oder) = True
- This version will use more VRAM, which shouldn't be a problem as it shouldn't exceed 2 x 80GB or 3 x 48GB cards.
- However CUDA inference performance is likely to be a lot slower, possibly necessitating the use of Triton mode.
By default you will download the first file, unless you choose to download from branch `group_size_128g`.
## Prompt template: none
```
Translate to English: Je t’aime.
Translation:
```
## 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.
Note 1: this is a non-Llama model which cannot be used with ExLlama. Use Loader: AutoGPTQ.
Note 2: As described above, you must join the files after downloading and before loading in text-generation-webui.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/bloomz-176B-GPTQ`.
- If you would rather download the group_size 128g version, enter `TheBloke/bloomz-176B-GPTQ:group_size_128g`
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done". This is a huge model so it may take a while!
5. Now follow the steps described above to join the model to get a single `.safetensors` file.
6. Untick **Autoload model**
7. In the top left, click the refresh icon next to **Model**.
8. In the **Model** dropdown, choose the model you just downloaded: `bloomz-176B-GPTQ`
9. Make sure Loader is set to AutGPTQ.
10. This model cannot load on one GPU, so you should set **GPU Memory** accordingly.
- If using two 80GB GPUs, try: GPU0 = 60GB, GPU1 = 79GB
- If using three 48GB GPUs, try: GPU0 = 30GB, GPU1 = 47GB, GPU2 = 47GB
11. Click **Save settings** to save your settings, and then **Reload** to load the model.
12. The model will load, and is now ready for use!
13. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
## How to use this GPTQ model from Python code
First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
`GITHUB_ACTIONS=true pip install auto-gptq`
Because this model has to be joined locally, you must first download it. Example download code:
```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="TheBloke/bloomz-176B-GPTQ",
local_dir="/workspace/models/bloomz-176GB-GPTQ",
local_dir_use_symlinks=False)
```
If you want to download the group_size 128g file instead, add `revision="group_size_128g"` to the above command.
Now join the three `split` files, which can be done with the following Python code:
```python
import glob
# Get the list of all files matching the pattern
files = glob.glob('gptq_model-4bit--1g.JOINBEFOREUSE.split-*.safetensors')
# Open the output file in binary write mode
with open('gptq_model-4bit--1g.safetensors', 'wb') as outfile:
for filename in files:
with open(filename, 'rb') as infile:
outfile.write(infile.read())
```
Then try the following example code:
```python
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse
# Use the local path you downloaded the model to and joined the split files in
model_name_or_path = "/workspace/models/bloomz-176GB-GPTQ"
model_basename = "gptq_model-4bit--1g"
use_triton = False
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
model_basename=model_basename,
max_memory={0: '60GiB', 1: '79GiB'} # max_memory is for 2 x 80GB GPUs; adjust if your config is different!
use_safetensors=True,
trust_remote_code=False,
use_triton=use_triton,
quantize_config=None)
prompt = "Translate this to French: AI is the future of computing"
prompt_template=f'''{prompt}
Translation:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.15
)
print(pipe(prompt_template)[0]['generated_text'])
```
## Provided files
## Main branch:
**gptq_model-4bit--1g.safetensors**
This will work with AutoGPTQ. It is untested with GPTQ-for-LLaMa. It will *not* work with ExLlama.
It was created with group_size none (-1) to reduce VRAM usage, and with --act-order (desc_act) to improve accuracy of responses.
* `gptq_model-4bit-128g.safetensors`
* Works with AutoGPTQ in CUDA or Triton modes.
* Does NOT work with [ExLlama](https://github.com/turboderp/exllama) as it's not a Llama model.
* Works with GPTQ-for-LLaMa in CUDA mode. May have issues with GPTQ-for-LLaMa Triton mode.
* Works with text-generation-webui, including one-click-installers.
* Parameters: Groupsize = -1. Act Order / desc_act = True.
## Branch `group_size_128g`
**gptq_model-4bit-128g.safetensors**
This will work with AutoGPTQ. It is untested with GPTQ-for-LLaMa. It will *not* work with ExLlama.
It was created with both group_size 128g and --act-order (desc_act) for even higher inference accuracy, at the cost of increased VRAM usage. Because we already need 2 x 80GB or 3 x 48GB GPUs, I don't expect the increased VRAM usage to change the GPU requirements.
**Note** Using group_size + desc_act together can significantly lower performance in AutoGPTQ CUDA. You might want to try AutoGPTQ Triton mode instead (Linux only.)
* `gptq_model-4bit-128g.safetensors`
* Works with AutoGPTQ in CUDA or Triton modes.
* Does NOT work with [ExLlama](https://github.com/turboderp/exllama) as it's not a Llama model.
* Untested with GPTQ-for-LLaMa.
* Works with text-generation-webui, including one-click-installers.
* Parameters: Groupsize = 128. Act Order / desc_act = True.
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: zynix , ya boyyy, Trenton Dambrowitz, Imad Khwaja, Alps Aficionado, chris gileta, John Detwiler, Willem Michiel, RoA, Mano Prime, Rainer Wilmers, Fred von Graf, Matthew Berman, Ghost , Nathan LeClaire, Iucharbius , Ai Maven, Illia Dulskyi, Joseph William Delisle, Space Cruiser, Lone Striker, Karl Bernard, Eugene Pentland, Greatston Gnanesh, Jonathan Leane, Randy H, Pierre Kircher, Willian Hasse, Stephen Murray, Alex , terasurfer , Edmond Seymore, Oscar Rangel, Luke Pendergrass, Asp the Wyvern, Junyu Yang, David Flickinger, Luke, Spiking Neurons AB, subjectnull, Pyrater, Nikolai Manek, senxiiz, Ajan Kanaga, Johann-Peter Hartmann, Artur Olbinski, Kevin Schuppel, Derek Yates, Kalila, K, Talal Aujan, Khalefa Al-Ahmad, Gabriel Puliatti, John Villwock, WelcomeToTheClub, Daniel P. Andersen, Preetika Verma, Deep Realms, Fen Risland, trip7s trip, webtim, Sean Connelly, Michael Levine, Chris McCloskey, biorpg, vamX, Viktor Bowallius, Cory Kujawski.
Thank you to all my generous patrons and donaters!
# Original model card: BigScience's BLOOMZ 176B
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.
- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co)
- **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
- **BLOOMZ & mT0 Model Family:**
