# Llama2 13B Orca v2 8K - GPTQ
- Model creator: [OpenAssistant](https://huggingface.co/OpenAssistant)
- Original model: [Llama2 13B Orca v2 8K](https://huggingface.co/OpenAssistant/llama2-13b-orca-v2-8k-3166)
## Description
This repo contains GPTQ model files for [OpenAssistant's Llama2 13B Orca v2 8K](https://huggingface.co/OpenAssistant/llama2-13b-orca-v2-8k-3166).
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
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML)
* [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/llama2-13b-orca-v2-8k-3166)
## 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) | GPTQ Dataset | Size | ExLlama Compat? | Made With | Desc |
| ------ | ---- | ---------- | -------------------- | ------------ | ---- | --------------- | --------- | ---- |
| [main](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ/tree/main) | 4 | 128 | No | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 7.26 GB | Yes | AutoGPTQ | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8.00 GB | Yes | 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](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 7.51 GB | Yes | AutoGPTQ | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | Processing, coming soon | Yes | AutoGPTQ | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | Processing, coming soon | No | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | Processing, coming soon | No | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ:gptq-4bit-32g-actorder_True`
- With Git, you can clone a branch with:
```
git clone --branch --single-branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-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-Llama2-13B-Orca-v2-8K-3166-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-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-Llama2-13B-Orca-v2-8K-3166-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-Llama2-13B-Orca-v2-8K-3166-GPTQ"
model_basename = "gptq_model-4bit-128g"
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**: Luke from CarbonQuill, Aemon Algiz.
**Patreon special mentions**: Willem Michiel, Ajan Kanaga, Cory Kujawski, Alps Aficionado, Nikolai Manek, Jonathan Leane, Stanislav Ovsiannikov, Michael Levine, Luke Pendergrass, Sid, K, Gabriel Tamborski, Clay Pascal, Kalila, William Sang, Will Dee, Pieter, Nathan LeClaire, ya boyyy, David Flickinger, vamX, Derek Yates, Fen Risland, Jeffrey Morgan, webtim, Daniel P. Andersen, Chadd, Edmond Seymore, Pyrater, Olusegun Samson, Lone Striker, biorpg, alfie_i, Mano Prime, Chris Smitley, Dave, zynix, Trenton Dambrowitz, Johann-Peter Hartmann, Magnesian, Spencer Kim, John Detwiler, Iucharbius, Gabriel Puliatti, LangChain4j, Luke @flexchar, Vadim, Rishabh Srivastava, Preetika Verma, Ai Maven, Femi Adebogun, WelcomeToTheClub, Leonard Tan, Imad Khwaja, Steven Wood, Stefan Sabev, Sebastain Graf, usrbinkat, Dan Guido, Sam, Eugene Pentland, Mandus, transmissions 11, Slarti, Karl Bernard, Spiking Neurons AB, Artur Olbinski, Joseph William Delisle, ReadyPlayerEmma, Olakabola, Asp the Wyvern, Space Cruiser, Matthew Berman, Randy H, subjectnull, danny, John Villwock, Illia Dulskyi, Rainer Wilmers, theTransient, Pierre Kircher, Alexandros Triantafyllidis, Viktor Bowallius, terasurfer, Deep Realms, SuperWojo, senxiiz, Oscar Rangel, Alex, Stephen Murray, Talal Aujan, Raven Klaugh, Sean Connelly, Raymond Fosdick, Fred von Graf, chris gileta, Junyu Yang, Elle
Thank you to all my generous patrons and donaters!
# Original model card: OpenAssistant's Llama2 13B Orca v2 8K
- wandb: [jlhr5cf2](https://wandb.ai/open-assistant/supervised-finetuning/runs/jlhr5cf2)
- sampling-report: [2023-07-31_OpenAssistant_llama2-13b-orca-v2-8k-3166_sampling_llama2_prompt.json](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-pretrained%2F2023-07-31_OpenAssistant_llama2-13b-orca-v2-8k-3166_sampling_llama2_prompt.json)
## Model Configuration
```
llama2-13b-orca-v2-8k:
rng_seed: 0xe1291f21
show_dataset_stats: true
random_offset_probability: 0.0
use_custom_sampler: true
sort_by_length: false
dtype: fp16
log_dir: /mnt/data/ikka/data_cache/llama2_13b_orcav2_logs
output_dir: /mnt/data/ikka/data_cache/llama2_13b_orcav2
learning_rate: 1e-5
model_name: conceptofmind/LLongMA-2-13b
deepspeed_config: configs/zero_config_pretrain.json
weight_decay: 0.000001
max_length: 8192
warmup_steps: 100
peft_model: false
use_flash_attention: true
gradient_checkpointing: true
gradient_accumulation_steps: 4
per_device_train_batch_size: 2
per_device_eval_batch_size: 1
residual_dropout: 0.0
eval_steps: 200
save_steps: 200
num_train_epochs: 1
save_total_limit: 4
superhot: false
superhot_config:
type: linear
scaling_factor: 2
datasets:
- orca-chat: # shahules786/orca-chat
data_files: orca-chat-gpt4-8k.json
max_val_set: 5000
val_split: 0.1
- evol-codealpaca-v1: # theblackcat102/evol-codealpaca-v1
fill_min_length: 20000
val_split: 0.1
- megacode: # rombodawg/MegaCodeTraining112k
fill_min_length: 24000
val_split: 0.1
max_val_set: 1000
- evol_instruct_code: # nickrosh/Evol-Instruct-Code-80k-v1
fill_min_length: 24000
val_split: 0.1
max_val_set: 1000
```
