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TheBloke
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Iambe-20B-DARE-AWQ

Text Generation
Transformers
Safetensors
llama
text-generation-inference
4-bit precision
awq
Model card Files Community
TheBlokeAI

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

Iambe 20B Dare - AWQ

Description

This repo contains AWQ model files for LadyBabyloin's Iambe 20B Dare.

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.

It is supported by:

Repositories available

Prompt template: Alpaca 

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Licensing

The creator of the source model has listed its license as cc-by-nc-4.0, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: LadyBabyloin's Iambe 20B Dare.

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 10.87 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/Iambe-20B-DARE-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: Iambe-20B-DARE-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/Iambe-20B-DARE-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
'''

prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]

sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="TheBloke/Iambe-20B-DARE-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/Iambe-20B-DARE-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
'''

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/Iambe-20B-DARE-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
'''

# 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: 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: LadyBabyloin's Iambe 20B Dare

Iambe-20b-DARE

Description and Role

Named after a charming daughter of Echo and Pan in Greek myth, Iambe-20b-DARE is a DARE merge building on my recent experiments.

Iambe is intended to have the best realistically possible understanding of anatomy and of a scene's state for a 20b merge, while remaining personable and authentic in "voice".

Prompting and Context

Iambe-20b-DARE uses Alpaca formatting, and has an effective context size of 4096 tokens. This model is uncensored, and the output/deployment of this model is the responsibility of the user.

Method and Hypothesis

Based on my extended vanilla model BigLlama, this adds elements of:

Examples

(q5_k_m GGUF quant, Ooba textgen, Midnight Enigma preset for KISS reasons)

Reasoning and Recall - STEM

Reasoning and Recall - Social Science

Ethics and Prompt Interpretation

Role-Playing, Complex Card

SillyTavern, Roleplay instruct preset, just Min_P 0.1 and Temp 1.2

!!NSFW!! - Erotica Writing Example - !!NSFW!!

Testing and Conclusions

VERY Impressed so far, concrete data coming eventually.

Does still have some confusion (at q5_k_m), but has instantly become my daily driver.

Recipe

merge_method: dare_ties

  • base_model: athirdpath/BigLlama-20b

  • model: NeverSleep/Noromaid-20b-v0.1.1

    weight: 0.41 / density: 0.50

  • model: athirdpath/Eileithyia-20b

    weight: 0.18 / density: 0.30

  • model: athirdpath/CleverGirl-20b-Blended

    weight: 0.41 / density: 0.50

int8_mask: true

dtype: bfloat16

Gratitude

Thanks to brucethemoose for the recipe. Thanks to Undi95 and IkariDev at NeverSleep for Noromaid, as well as lots of inspiration. Thanks to Sao10K for half of CleverGirl.

Geneology Credits - Models and LoRAs, 3 levels deep

  • athirdpath/BigLlama-20b

    • TheBloke/Llama-2-13B-fp16

    • iamshnoo/alpaca-2-13b-english

  • NeverSleep/Noromaid-20b-v0.1.1

    • Aesir Private RP dataset

    • HuggingFaceH4/no_robots

  • athirdpath/CleverGirl-20b-Blended

    • athirdpath/Orca-2-13b-Alpaca-Uncensored

      • microsoft/Orca-2-13b

      • athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA

    • Sao10K/Mythical-Destroyer-V2-L2-13B

      • TheBloke/Llama-2-13B-fp16

      • Gryphe/MythoMax-L2-13b

      • totally-not-an-llm/PuddleJumper-13b

      • jondurbin/airoboros-l2-13b-2.1

      • rombodawg/LosslessMegaCoder-llama2-13b-mini

      • The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16

  • athirdpath/Eileithyia-20b

    • athirdpath/Harmonia-20B

      • Undi95/Emerhyst-20B

        • Undi95/Emerald-13B

        • Undi95/Amethyst-13B

      • Undi95/MXLewd-L2-20B

        • Undi95/MLewd-L2-13B-v2-3

        • Undi95/ReMM-v2.1-L2-13B

        • Xwin-LM/Xwin-LM-13B-V0.1

      • Undi95/Lewd-Sydney-20B

        • Free_Sydney_V2_13b_HF

        • Undi95/Xwin-MLewd-13B-V0.2

        • lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT

      • athirdpath/Nethena-20b-Glued

        • NeverSleep/Nethena-20B

        • athirdpath/Nethena-20b-Glue-LORA

      • tavtav/Rose-20B

        • CalderaAI/13B-Thorns-l2

        • NeverSleep/Noromaid-13b-v0.1.0

      • Undi95/PsyMedRP-v1-20B

        • jondurbin/airoboros-l2-13b-3.0

        • ehartford/Samantha-1.11-13b

        • Xwin-LM/Xwin-LM-13B-V0.1

        • chaoyi-wu/MedLLaMA_13B

        • migtissera/Synthia-13B-v1.2

      • NeverSleep/Noromaid-20b-v0.1.1

        • Aesir Private RP dataset

        • HuggingFaceH4/no_robots

      • Undi95/U-Amethyst-20B

        • Xwin-LM/Xwin-LM-13B-V0.1

        • The-Face-Of-Goonery/Huginn-13b-FP16

        • zattio770/120-Days-of-LORA-v2-13B

        • lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT

        • Undi95/Unholy-v1-12L-13B

    • athirdpath/Eileithyia-20b-LORA

Thanks again to everyone involved.

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