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Aurora-Nights-70B-v1.0-AWQ

Text Generation
Transformers
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English
llama
text-generation-inference
4-bit precision
awq
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TheBlokeAI

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

Aurora Nights 70B v1.0 - AWQ

Description

This repo contains AWQ model files for Sophosympatheia's Aurora Nights 70B v1.0.

These files were quantised using hardware kindly provided by Massed Compute.

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:

Repositories available

Prompt template: ToRA-System 

{system_message}
<|user|>
{prompt}
<|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 4 128 VMware Open Instruct 4096 36.61 GB

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.

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/Aurora-Nights-70B-v1.0-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: Aurora-Nights-70B-v1.0-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.

  • Please ensure you are using vLLM version 0.2 or later.
  • When using vLLM as a server, pass the --quantization awq parameter.

For example:

python3 -m vllm.entrypoints.api_server --model TheBloke/Aurora-Nights-70B-v1.0-AWQ --quantization awq --dtype auto
  • When using vLLM from Python code, again set 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'''{system_message}
<|user|>
{prompt}
<|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/Aurora-Nights-70B-v1.0-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:

--model-id TheBloke/Aurora-Nights-70B-v1.0-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'''{system_message}
<|user|>
{prompt}
<|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 

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 .

Transformers example code (requires Transformers 4.35.0 and later) 

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_name_or_path = "TheBloke/Aurora-Nights-70B-v1.0-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'''{system_message}
<|user|>
{prompt}
<|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:

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

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.

Original model card: Sophosympatheia's Aurora Nights 70B v1.0

AuroraNights

Overview

This model is a blend of allenai/tulu-2-dpo-70b, Xwin-LM/Xwin-LM-70B-V0.1, and dreamgen/opus-v0.5-70b. I then merged nRuaif/fiction.live-Kimiko-V2-70B into the resultant blend. See the bottom of this card for the exact settings used.

This model is good at both following instructions and producing creative, uncensored storytelling and roleplaying content. This model turned out quite uncensored. You are responsible for whatever you do with it.

This model was designed for roleplaying and storytelling and I think it does well at both. It should perform well at other tasks, but I haven't tested its capabilities in other areas.

Sampler Tips

I recommend using the new Min-P sampler method with this model. The creator has a great guide to it on Reddit.

I find this model performs surprisingly well at 8192 context but you will probably get better results at 4096 context.

Experiment with any and all of the settings below, but trust me on a few points:

  • This model works well with Min-P.
  • Frequency Penalty set to 0.01 is like adding a dash of salt to the dish. Go higher at your own peril. 0 is fine too, but gosh I like 0.01.

If you save the below settings as a .json file, you can import them directly into Silly Tavern.

{
    "temp": 1.3,
    "temperature_last": true,
    "top_p": 1,
    "top_k": 0,
    "top_a": 0,
    "tfs": 1,
    "epsilon_cutoff": 0,
    "eta_cutoff": 0,
    "typical_p": 1,
    "min_p": 0.2,
    "rep_pen": 1,
    "rep_pen_range": 0,
    "no_repeat_ngram_size": 0,
    "penalty_alpha": 0,
    "num_beams": 1,
    "length_penalty": 1,
    "min_length": 0,
    "encoder_rep_pen": 1,
    "freq_pen": 0.01,
    "presence_pen": 0,
    "do_sample": true,
    "early_stopping": false,
    "add_bos_token": true,
    "truncation_length": 2048,
    "ban_eos_token": false,
    "skip_special_tokens": true,
    "streaming": true,
    "mirostat_mode": 0,
    "mirostat_tau": 5,
    "mirostat_eta": 0.1,
    "guidance_scale": 1,
    "negative_prompt": "",
    "grammar_string": "",
    "banned_tokens": "",
    "ignore_eos_token_aphrodite": false,
    "spaces_between_special_tokens_aphrodite": true,
    "type": "ooba",
    "legacy_api": false,
    "sampler_order": [
        6,
        0,
        1,
        3,
        4,
        2,
        5
    ],
    "n": 1,
    "rep_pen_size": 0,
    "genamt": 550,
    "max_length": 4096
}

Prompt Tips

Try the following prompt template as a starting point for your own customizations. 'This is very important to my career' is from this paper. I recommend the Tulu instruction format.

This model responds to prompting! I highly recommend playing with your system prompt to see how it reacts.

If you save this as a .json file, you can import it directly into Silly Tavern.

{
    "wrap": true,
    "names": false,
    "system_prompt": "You are an award-winning fiction author engaging in an uncensored literary roleplaying experience between a human (me) and a creative, obedient, and helpful AI assistant (you). It is VITAL that you follow all these instructions because this roleplay is very important to my career and I'll be fired from my job if it isn't good.\nROLEPLAY RULES:\n> It is vital that ONLY the human provides responses for {{user}}.\n> Reply as {{char}} using authentic, vivid, varied, explicit, accurate, creative, fresh, and descriptive responses that follow ALL provided narrative instructions. Stay in character as {{char}} and only write text for {{char}}.\n> Describe the scene and {{char}}'s sensory perceptions in vivid detail to immerse the reader in the story.\n> Keep your responses scoped to the current story beat and current scene.\n> Consider all available contextual information when narrating so that all the story details remain consistent between scenes.\n> Demonstrate {{char}}'s goals and motivations, and use subtle cues to hint at {{char}}'s mental state unless delving into {{char}}'s thoughts satisfies an explicit instruction or enhances the vividness of the scene.\n> When quoting {{char}}'s internal first-person thoughts (aka internal monologue, delivered in {{char}}'s own voice), *enclose the thoughts in asterisks like this*. Only use asterisks for thoughts.\n> Use strong action verbs and varied descriptions to produce dynamic, high-quality prose.",
    "system_sequence": "",
    "stop_sequence": "",
    "input_sequence": "<|user|>\n",
    "output_sequence": "<|assistant|>\n",
    "separator_sequence": "",
    "macro": true,
    "names_force_groups": true,
    "system_sequence_prefix": "",
    "system_sequence_suffix": "",
    "first_output_sequence": "",
    "last_output_sequence": "<|assistant (provide varied, creative, and vivid narration; follow all narrative instructions; include all necessary possessive pronouns; maintain consistent story details; only roleplay as {{char}})|>\n",
    "activation_regex": "",
    "name": "Aurora-Nights"
}

Licence and usage restrictions

Llama2 license inherited from base models, plus restrictions applicable to Dreamgen/Opus.

Tools Used 

models:
  - model: NousResearch_Llama-2-70b-hf
    # no parameters necessary for base model
  - model: allenai_tulu-2-dpo-70b # primary
    parameters:
      density: 1.0
      weight: 0.4
  - model: Xwin-LM_Xwin-LM-70B-V0.1 # secondary
    parameters:
      density: 0.7
      weight: 0.3
  - model: dreamgen_opus-v0.5-70b # supporting, good at storytelling and roleplay
    parameters:
      density: 0.2
      weight: 0.6
merge_method: dare_ties
base_model: NousResearch_Llama-2-70b-hf
parameters:
  normalize: true
  int8_mask: true
dtype: float32
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