---
inference: false
license: other
model_type: llama
---
# OpenAssistant LLaMA 30B SFT 7 GPTQ
These files are GPTQ model files for [OpenAssistant LLaMA 30B SFT 7](https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor).
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 models were quantised using hardware kindly provided by [Latitude.sh](https://www.latitude.sh/accelerate).
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-HF)
## Prompt template: OpenAssistant
```
<|prompter|>{prompt}<|endoftext|><|assistant|>
```
## Provided files
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.
| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
| main | 4 | None | True | 16.94 GB | True | GPTQ-for-LLaMa | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
| gptq-4bit-32g-actorder_True | 4 | 32 | True | 19.44 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
| gptq-4bit-64g-actorder_True | 4 | 64 | True | 18.18 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
| gptq-4bit-128g-actorder_True | 4 | 128 | True | 17.55 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
| gptq-8bit--1g-actorder_True | 8 | None | True | 32.99 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
| gptq-8bit-128g-actorder_False | 8 | 128 | False | 33.73 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
| gptq-3bit--1g-actorder_True | 3 | None | True | 12.92 GB | False | AutoGPTQ | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| gptq-3bit-128g-actorder_False | 3 | 128 | False | 13.51 GB | False | AutoGPTQ | 3-bit, with group size 128g but no act-order. Slightly higher VRAM requirements than 3-bit None. |
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ:gptq-4bit-32g-actorder_True`
- With Git, you can clone a branch with:
```
git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/OpenAssistant-SFT-7-Llama-30B-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 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 know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ:gptq-4bit-32g-actorder_True`
- 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: `OpenAssistant-SFT-7-Llama-30B-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 set 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
First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) installed:
`GITHUB_ACTIONS=true pip install auto-gptq`
Then try the following example code:
```python
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
model_name_or_path = "TheBloke/OpenAssistant-SFT-7-Llama-30B-GPTQ"
model_basename = "OpenAssistant-SFT-7-Llama-30B-GPTQ-4bit--1g.act.order"
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
use_safetensors=True,
trust_remote_code=False,
device="cuda:0",
use_triton=use_triton,
quantize_config=None)
"""
To download from a specific branch, use the revision parameter, as in this example:
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
revision="gptq-4bit-32g-actorder_True",
model_basename=model_basename,
use_safetensors=True,
trust_remote_code=False,
device="cuda:0",
quantize_config=None)
"""
prompt = "Tell me about AI"
prompt_template=f'''<|prompter|>{prompt}<|endoftext|><|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, 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'])
```
## Compatibility
The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.
ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
## 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**: Aemon Algiz.
**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
# Original model card: OpenAssistant LLaMA 30B SFT 7
# OpenAssistant LLaMA 30B SFT 7
Due to the license attached to LLaMA models by Meta AI it is not possible to directly distribute LLaMA-based models. Instead we provide XOR weights for the OA models.
Thanks to Mick for writing the `xor_codec.py` script which enables this process
## The Process
Note: This process applies to `oasst-sft-7-llama-30b` model. The same process can be applied to other models in future, but the checksums will be different..
**This process is tested only on Linux (specifically Ubuntu). Some users have reported that the process does not work on Windows. We recommend using WSL if you only have a Windows machine.**
To use OpenAssistant LLaMA-Based Models, you should have a copy of the original LLaMA model weights and add them to a `llama` subdirectory here. If you cannot obtain the original LLaMA, see the note in italic below for a possible alternative.
Ensure your LLaMA 30B checkpoint matches the correct md5sums:
```
f856e9d99c30855d6ead4d00cc3a5573 consolidated.00.pth
d9dbfbea61309dc1e087f5081e98331a consolidated.01.pth
2b2bed47912ceb828c0a37aac4b99073 consolidated.02.pth
ea0405cdb5bc638fee12de614f729ebc consolidated.03.pth
4babdbd05b8923226a9e9622492054b6 params.json
```
*If you do not have a copy of the original LLaMA weights and cannot obtain one, you may still be able to complete this process. Some users have reported that [this model](https://huggingface.co/elinas/llama-30b-hf-transformers-4.29) can be used as a base for the XOR conversion. This will also allow you to skip to Step 7. However, we only support conversion starting from LLaMA original checkpoint and cannot provide support if you experience issues with this alternative approach.*
**Important: Follow these exact steps to convert your original LLaMA checkpoint to a HuggingFace Transformers-compatible format. If you use the wrong versions of any dependency, you risk ending up with weights which are not compatible with the XOR files.**
1. Create a clean Python **3.10** virtual environment & activate it:
```
python3.10 -m venv xor_venv
source xor_venv/bin/activate
```
2. Clone transformers repo and switch to tested version:
```
git clone https://github.com/huggingface/transformers.git
cd transformers
git checkout d04ec99bec8a0b432fc03ed60cea9a1a20ebaf3c
pip install .
```
3. Install **exactly** these dependency versions:
```
pip install torch==1.13.1 accelerate==0.18.0 sentencepiece==0.1.98 protobuf==3.20.1
```
4. Check `pip freeze` output:
```
accelerate==0.18.0
certifi==2022.12.7
charset-normalizer==3.1.0
filelock==3.12.0
huggingface-hub==0.13.4
idna==3.4
numpy==1.24.2
nvidia-cublas-cu11==11.10.3.66
nvidia-cuda-nvrtc-cu11==11.7.99
nvidia-cuda-runtime-cu11==11.7.99
nvidia-cudnn-cu11==8.5.0.96
packaging==23.1
protobuf==3.20.1
psutil==5.9.5
PyYAML==6.0
regex==2023.3.23
requests==2.28.2
sentencepiece==0.1.98
tokenizers==0.13.3
torch==1.13.1
tqdm==4.65.0
transformers @ file:///mnt/data/koepf/transformers
typing_extensions==4.5.0
urllib3==1.26.15
```
5. While in `transformers` repo root, run HF LLaMA conversion script:
```
python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir --output_dir --model_size 30B
```
6. Run `find . -type f -exec md5sum "{}" +` in the conversion target directory (`output_dir`). This should produce exactly the following checksums if your files are correct:
```
462a2d07f65776f27c0facfa2affb9f9 ./pytorch_model-00007-of-00007.bin
e1dc8c48a65279fb1fbccff14562e6a3 ./pytorch_model-00003-of-00007.bin
9cffb1aeba11b16da84b56abb773d099 ./pytorch_model-00001-of-00007.bin
aee09e21813368c49baaece120125ae3 ./generation_config.json
92754d6c6f291819ffc3dfcaf470f541 ./pytorch_model-00005-of-00007.bin
3eddc6fc02c0172d38727e5826181adb ./pytorch_model-00004-of-00007.bin
eeec4125e9c7560836b4873b6f8e3025 ./tokenizer.model
99762d59efa6b96599e863893cf2da02 ./pytorch_model-00006-of-00007.bin
598538f18fed1877b41f77de034c0c8a ./config.json
fdb311c39b8659a5d5c1991339bafc09 ./tokenizer.json
fecfda4fba7bfd911e187a85db5fa2ef ./pytorch_model.bin.index.json
edd1a5897748864768b1fab645b31491 ./tokenizer_config.json
6b2e0a735969660e720c27061ef3f3d3 ./special_tokens_map.json
5cfcb78b908ffa02e681cce69dbe4303 ./pytorch_model-00002-of-00007.bin
```
**Important: You should now have the correct LLaMA weights and be ready to apply the XORs. If the checksums above do not match yours, there is a problem.**
7. Once you have LLaMA weights in the correct format, you can apply the XOR decoding:
```
python xor_codec.py oasst-sft-7-llama-30b/ oasst-sft-7-llama-30b-xor/ llama30b_hf/
```
You should **expect to see one warning message** during execution:
`Exception when processing 'added_tokens.json'`
This is normal. **If similar messages appear for other files, something has gone wrong**.
8. Now run `find . -type f -exec md5sum "{}" +` in the output directory (here `oasst-sft-6-llama-30b`). You should get a file with exactly these checksums:
```
8ae4537c64a1ef202d1d82eb0d356703 ./pytorch_model-00007-of-00007.bin
d84f99d23369e159e50cb0597b6c9673 ./pytorch_model-00003-of-00007.bin
f7de50a725d678eb65cc3dced727842f ./pytorch_model-00001-of-00007.bin
27b0dc092f99aa2efaf467b2d8026c3f ./added_tokens.json
aee09e21813368c49baaece120125ae3 ./generation_config.json
31a2b04b139f4af043ad04478f1497f5 ./pytorch_model-00005-of-00007.bin
a16a2dfacbde77a1659a7c9df7966d0a ./pytorch_model-00004-of-00007.bin
eeec4125e9c7560836b4873b6f8e3025 ./tokenizer.model
baa778a8679d47b085446faf97b72758 ./pytorch_model-00006-of-00007.bin
b2d64f2198ab7b53e3b8d12fbcadeb3c ./config.json
deb33dd4ffc3d2baddcce275a00b7c1b ./tokenizer.json
76d47e4f51a8df1d703c6f594981fcab ./pytorch_model.bin.index.json
ed59bfee4e87b9193fea5897d610ab24 ./tokenizer_config.json
704373f0c0d62be75e5f7d41d39a7e57 ./special_tokens_map.json
e836168cdbbb74db51d04f25ed6408ce ./pytorch_model-00002-of-00007.bin
```
If so you have successfully decoded the weights and should be able to use the model with HuggingFace Transformers. **If your checksums do not match those above, there is a problem.**
### Configuration
```
llama-30b-sft-7:
dtype: fp16
log_dir: "llama_log_30b"
learning_rate: 1e-5
model_name: /home/ubuntu/Open-Assistant/model/model_training/.saved/llama-30b-super-pretrain/checkpoint-3500
#model_name: OpenAssistant/llama-30b-super-pretrain
output_dir: llama_model_30b
deepspeed_config: configs/zero3_config_sft.json
weight_decay: 0.0
residual_dropout: 0.0
max_length: 2048
use_flash_attention: true
warmup_steps: 20
gradient_checkpointing: true
gradient_accumulation_steps: 12
per_device_train_batch_size: 2
per_device_eval_batch_size: 3
eval_steps: 101
save_steps: 485
num_train_epochs: 4
save_total_limit: 3
use_custom_sampler: true
sort_by_length: false
#save_strategy: steps
save_strategy: epoch
datasets:
- oasst_export:
lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz
val_split: 0.05
- vicuna:
val_split: 0.05
max_val_set: 800
fraction: 1.0
- dolly15k:
val_split: 0.05
max_val_set: 300
- grade_school_math_instructions:
val_split: 0.05
- code_alpaca:
val_split: 0.05
max_val_set: 250
```
- **OASST dataset paper:** https://arxiv.org/abs/2304.07327