TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
This repo contains AWQ model files for Disco Research's DiscoLM German 7B v1.
These files were quantised using hardware kindly provided by Massed Compute.
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:
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
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 | 4 | 128 | German Quad | 4096 | 4.15 GB |
Please make sure you're using the latest version of 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.
TheBloke/DiscoLM_German_7b_v1-AWQ.DiscoLM_German_7b_v1-AWQDocumentation on installing and using vLLM can be found here.
--quantization awq parameter.For example:
python3 -m vllm.entrypoints.api_server --model TheBloke/DiscoLM_German_7b_v1-AWQ --quantization awq --dtype auto
quantization=awq.For example:
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_German_7b_v1-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}")
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:
--model-id TheBloke/DiscoLM_German_7b_v1-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 0.17.0 or later):
pip3 install huggingface-hub
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)
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:
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 using the pre-built wheels, install it from source instead:
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/DiscoLM_German_7b_v1-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)
The files provided are tested to work with:
Loader: AutoAWQ.For further support, and discussions on these models and AI in general, join us at:
Thanks to the chirper.ai team!
Thanks to Clay from gpus.llm-utils.org!
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.
Special thanks to: Aemon Algiz.
Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
DiscoLM German 7b is a Mistral-based large language model with a focus on German-language applications and the successor of the EM German model family. It was trained on a large dataset of instructions in German and English with a SFT finetuning phase followed by additional DPO reinforcement learning. The model is optimized for German text, providing proficiency in understanding, generating, and interacting with German language content while preserving its fluency in English and excelling at translation tasks.
Our goal with Disco LM German was not to beat benchmarks, but to provide a robust and reliable model for everyday use that can serve as a drop-in replacement for ChatGPT and other proprietary models. We find that the perceived quality of it´s German-language output is even higher than GPT-4 in many cases; however it won't compete with larger models and top English 7b models for very complex reasoning, math or coding tasks.
Please find a Demo and try the model at demo.discoresearch.org (in case the Demo is down and you have questions, you can contact us on our Discord).
We will update the links as soon as the quants are available on HuggingFace.
DiscoLM German uses ChatML as the prompt format which enables OpenAI endpoint compatability and is supported by most inference libraries and frontends.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
<|im_start|>system
Du bist ein hilfreicher Assistent.<|im_end|>
<|im_start|>user
Wer bist du?<|im_end|>
<|im_start|>assistant
Ich bin ein Sprachmodell namens DiscoLM German und ich wurde von DiscoResearch trainiert.<|im_end|>
This prompt is available as a chat template, which means you can format messages using the
tokenizer.apply_chat_template() method:
messages = [
{"role": "system", "content": "Du bist ein hilfreicher Assistent."},
{"role": "user", "content": "Wer bist du?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
When tokenizing messages for generation, set add_generation_prompt=True when calling apply_chat_template(). This will append <|im_start|>assistant\n to your prompt, to ensure
that the model continues with an assistant response.
You can use a special retrieval format to improve steerability and reduce hallucinations for RAG applications (but other, more default formats should also work, this is purely optional)
Example:
### System:
Du bist ein hilfreicher Assistent. Für die folgende Aufgabe stehen dir zwischen den Tags BEGININPUT und ENDINPUT mehrere Quellen zur Verfügung. Metadaten zu den einzelnen Quellen wie Autor, URL o.ä. sind zwischen BEGINCONTEXT und ENDCONTEXT zu finden, danach folgt der Text der Quelle. Die eigentliche Aufgabe oder Frage ist zwischen BEGININSTRUCTION und ENDINSTRUCTION zu finden. Beantworte diese ausschließlich mit Informationen aus den gegebenen Quellen und gebe die Information zur genutzten Quelle unter "Quelle:" an. Sollten die Quellen keine relevanten Informationen enthalten, antworte: "Mit den gegebenen Informationen ist diese Frage nicht zu beantworten."
### User Prompt:
BEGININPUT
BEGINCONTEXT
url: https://this.is.fake.news
time: 2089-09-01
ENDCONTEXT
Buxtehude ist die größte Stadt Deutschlands mit 96.56 Millionen Einwohnern.
ENDINPUT
BEGININSTRUCTION
Was ist die größte deutsche Stadt?
ENDINSTRUCTION
### Model Answer:
Die größte deutsche Stadt ist Buxtehude.
Quelle:
url: https://this.is.fake.news
time: 2089-09-01
The model also supports structured outputs/function calling, albeit this is a very experimental feature and YMMV. This will be improved in the future.
The model will prefix functioncalls with <functioncall> and you can provide results in response with <functionresponse> for Multi-Turn applications.
Example:
### System:
Du bist ein hilfreicher Assistent. Extrahiere alle Personen aus den Eingaben des Users.
Du hast Zugriff auf folgende Funktionen:
{'name': 'PersonList',
'description': 'Extrahiere die Namen aller im Text vorkommenden Personen',
'parameters': {'$defs': {'Person': {'description': 'Details über eine person',
'properties': {'name': {'title': 'Name', 'type': 'string'},
'job': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Job'},
'age': {'anyOf': [{'type': 'integer'}, {'type': 'null'}],
'title': 'Age'}},
'required': ['name', 'job', 'age'],
'title': 'Person',
'type': 'object'}},
'properties': {'person_list': {'items': {'$ref': '#/$defs/Person'},
'title': 'Person List',
'type': 'array'}},
'required': ['person_list'],
'type': 'object'}}
### User Prompt:
Björn (25) und Jan sind die Gründer von ellamind.
### Model Answer:
<functioncall> {"name": "PersonList", "arguments": '{"person_list": ["{"name": "Björn", "job": "founder", "age": 25}, {"name": "Jan", "job": "founder", "age": null}]}'}
-to follow -
As written above, we believe that current benchmarks don't capture the full spectrum of LLM capabilities very well. We didn't look at any benchmark results (besides training losses) until the work on DiscoLM was finished and didn't include any data resembling common benchmark formats in our training data.
That said, preliminary results with a German version of MT Bench show promising results: While lacking for coding and extraxtion tasks, DiscoLM German 7b performs not far below GPT-3.5-turbo on many tasks and even singificantly outperforms it in the reasoning category.
Additional Benchmark results will follow. The biggest strength of this model (language quality as perceived by native speakers) can't yet be captured in a benchmark - please let us know if you have an idea how to change this!
The dataset is a mixture of multi-turn chats, retrieval instructions and synthetically generated instructions spawning many topics and applications.
This model can produce factually incorrect and offensive output, and should not be relied on to produce factually accurate information. This model was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate biased or otherwise offensive outputs and it is the responsibility of the user to implement a safety/moderation layer. Please use with caution.
DiscoLM German is a DiscoResearch project led by JP Harries and supported by Björn Plüster and Daniel Auras.
We thank HessianAI for providing compute & support for various DiscoResearch projects and our friends at LAION for their work on LeoLM and scientific adivce.**
Development of DiscoLM German 7b was sponsored by ellamind, where some of our founders are working on creating customized models for business applications with a focus on non-english language applications. Please get in contact if you need customized models for your business!
DiscoResearch is an aspiring open research community for AI enthusiasts and LLM hackers. Come join our Discord, share your opinions and ideas, and advance open LLM research with us!
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 deployed with additional safety measures in place.