---
base_model: DiscoResearch/DiscoLM-120b
datasets:
- Open-Orca/SlimOrca-Dedup
- teknium/openhermes
- meta-math/MetaMathQA
- migtissera/Synthia-v1.3
- THUDM/AgentInstruct
- LeoLM/German_Songs
- LeoLM/German_Poems
- LeoLM/OpenSchnabeltier
- bjoernp/ultrachat_de
inference: false
language:
- en
library_name: transformers
license: llama2
model_creator: Disco Research
model_name: DiscoLM 120B
model_type: llama
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- goliath
- deutsch
- llama2
- discoresearch
---
# DiscoLM 120B - AWQ
- Model creator: [Disco Research](https://huggingface.co/DiscoResearch)
- Original model: [DiscoLM 120B](https://huggingface.co/DiscoResearch/DiscoLM-120b)
## Description
This repo contains AWQ model files for [Disco Research's DiscoLM 120B](https://huggingface.co/DiscoResearch/DiscoLM-120b).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/DiscoLM-120b-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/DiscoLM-120b-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/DiscoLM-120b-GGUF)
* [Disco Research's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/DiscoResearch/DiscoLM-120b)
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/DiscoLM-120b-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 61.96 GB
## 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're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/DiscoLM-120b-AWQ`.
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: `DiscoLM-120b-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/DiscoLM-120b-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/DiscoLM-120b-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
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:
```shell
--model-id TheBloke/DiscoLM-120b-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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)
```
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/DiscoLM-120b-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
## 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!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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: Disco Research's DiscoLM 120B
# DiscoLM 120b (Alpha)
**DiscoLM 120b (Alpha)** is an experimental 120b model based on [Alpindale´s Goliath 120b](https://huggingface.co/alpindale/goliath-120b), a merge of different Llama2-70b models, and further finetuned on a dataset of some the most popular open-source instruction sets.
Disco 120b is a [DiscoResearch](https://huggingface.co/DiscoResearch) project and was trained by [Björn Plüster](https://huggingface.co/bjoernp).
The model was trained with compute provided by [HessianAI](https://hessian.ai/) - we are very grateful for their support; please check out their wesbite and projects!
## Table of Contents
1. [Download](#download)
2. [Benchmarks](#benchmarks)
3. [Prompt Format](#prompt-format)
4. [Dataset](#dataset)
5. [Acknowledgements](#acknowledgements)
6. [Contact](#contact)
7. [About DiscoResearch](#about-discoresearch)
8. [Disclaimer](#disclaimer)
## Download
| Huggingface | GPTQ | GGUF | AWQ | *Base Model* |
|-------|-------|-------|-------|-------|
| [Link](https://huggingface.co/DiscoResearch/DiscoLM-120b) | soon | soon | soon | [Goliath 120b](https://huggingface.co/alpindale/goliath-120b) |
## Benchmarks
### Hugginface Leaderboard
This models is still an early Alpha and we can't guarantee that there isn't any contamination.
However, the average of **72.15** would earn the #2 spot on the HF leaderboard at the time of writing and the highest score for a >70b model yet.
| Metric | Value |
|-----------------------|-------|
| ARC (25-shot) | 69.54 |
| HellaSwag (10-shot) | 86.49 |
| MMLU (5-shot) | 70.32 |
| TruthfulQA (0-shot) | 61.42 |
| Winogrande (5-shot) | 83.03 |
| GSM8k (5-shot) | 68.39 |
| **Avg.** | **72.15** |
We use [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard.
### FastEval
| Metric | Value |
|-----------------------|-------|
| GSM8K | 81.2 |
| Math | 22.3 |
| BBH | 72.9 |
| MMLU | 67.9 |
| **Avg.** | **53.3** |
### MTBench
```json
{
"first_turn": 8.45,
"second_turn": 7.45,
"categories": {
"writing": 9.4,
"roleplay": 8.65,
"reasoning": 6.85,
"math": 5.55,
"coding": 4.95,
"extraction": 9.15,
"stem": 9.225,
"humanities": 9.825
},
"average": 7.95
}
```
## Prompt Format
This model follows the ChatML format:
```
<|im_start|>system
You are DiscoLM, a helpful assistant.
<|im_end|>
<|im_start|>user
Please tell me possible reasons to call a research collective "Disco Research"<|im_end|>
<|im_start|>assistant
```
This formatting is also available via a pre-defined Transformers chat template, which means that lists of messages can be formatted for you with the apply_chat_template() method:
```python
chat = [
{"role": "system", "content": "You are DiscoLM, a helpful assistant."},
{"role": "user", "content": "Please tell me possible reasons to call a research collective Disco Research"}
]
tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```
If you use `tokenize=True` and `return_tensors="pt"` instead, then you will get a tokenized and formatted conversation ready to pass to `model.generate()`.
## Dataset
The dataset curation for DiscoLM 120b followed a "brute force"/"PoC" approach, as one goal was to see whether a 120b model can "absorb" more instruction data than a 70b model.
The following datasets were used for training DiscoLM 120b:
* [SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
* [OpenPlatypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
* [OpenHermes](https://huggingface.co/datasets/teknium/openhermes)
* [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
* [UltraChat](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
* [Synthia v.1.3](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
* [AgentInstruct](https://huggingface.co/datasets/THUDM/AgentInstruct)
Many thanks for all dataset providers/curators!
## Contact
Best way to reach us is on our [Discord](https://discord.gg/4pAqJP7W).
## About DiscoResearch
DiscoResearch is an aspiring open research community. Disco should be a place where researchers from many communities can come together to combine their expertise and create innovative and groundbreaking LLMs. Come join our Discord, share your opinions and ideas, and advance open LLM research with us!
## Acknowledgements
Disco 120b is a [DiscoResearch](https://huggingface.co/DiscoResearch) project and was trained by [Björn Plüster](https://huggingface.co/bjoernp). [Jan Harries](https://huggingface.co/jphme) helped with technical adivce, logistics and the Model Card and [AutoMeta](https://huggingface.co/Alignment-Lab-AI) also provided helpful technical adivce.
The model was trained with compute provided by [HessianAI](https://hessian.ai/) - many thanks in particular to [Patrick Schramowski](https://huggingface.co/PSaiml) for his support.
We are standing on the shoulders of giants; many thanks in no particular order to [alpindale](https://huggingface.co/alpindale) for Goliath 120b (with important contributions by [Charles Goddard](https://huggingface.co/chargoddard) and [Undi95](https://huggingface.co/Undi95)), [TheBloke](https://huggingface.co/TheBloke) for providing quantized versions, [winglian](https://huggingface.co/winglian) for Axolotl which was used to train the model and the SlimOrca dataset, [garage-bAInd](https://huggingface.co/garage-bAInd), [Teknium](https://huggingface.co/teknium), [Migel Tissera](https://huggingface.co/migtissera), [MetaMath](https://huggingface.co/meta-math) for their great datasets (please contact us if we forgot to mention you here!).
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model.
This model should only be used for research purposes. The original Llama2 license and all restrictions of datasets used to train this model apply.
