TheBloke/Iambe-RP-v3-20B-GGUF

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

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Iambe RP v3 20B - GGUF

• Model creator: Raven
• Original model: Iambe RP v3 20B

Description

This repo contains GGUF format model files for Raven's Iambe RP v3 20B.

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

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

• llama.cpp. The source project for GGUF. Offers a CLI and a server option.
• text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
• KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
• GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
• LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
• LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
• Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
• llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
• candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Repositories available

• AWQ model(s) for GPU inference.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
• Raven's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

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: Raven's Iambe RP v3 20B.

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

Explanation of quantisation methods

Click to see details

The new methods available are:

• GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
• GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
• GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
• GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
• GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
iambe-rp-v3-20b.Q2_K.gguf	Q2_K	2	8.31 GB	10.81 GB	smallest, significant quality loss - not recommended for most purposes
iambe-rp-v3-20b.Q3_K_S.gguf	Q3_K_S	3	8.66 GB	11.16 GB	very small, high quality loss
iambe-rp-v3-20b.Q3_K_M.gguf	Q3_K_M	3	9.70 GB	12.20 GB	very small, high quality loss
iambe-rp-v3-20b.Q3_K_L.gguf	Q3_K_L	3	10.63 GB	13.13 GB	small, substantial quality loss
iambe-rp-v3-20b.Q4_0.gguf	Q4_0	4	11.29 GB	13.79 GB	legacy; small, very high quality loss - prefer using Q3_K_M
iambe-rp-v3-20b.Q4_K_S.gguf	Q4_K_S	4	11.34 GB	13.84 GB	small, greater quality loss
iambe-rp-v3-20b.Q4_K_M.gguf	Q4_K_M	4	12.04 GB	14.54 GB	medium, balanced quality - recommended
iambe-rp-v3-20b.Q5_0.gguf	Q5_0	5	13.77 GB	16.27 GB	legacy; medium, balanced quality - prefer using Q4_K_M
iambe-rp-v3-20b.Q5_K_S.gguf	Q5_K_S	5	13.77 GB	16.27 GB	large, low quality loss - recommended
iambe-rp-v3-20b.Q5_K_M.gguf	Q5_K_M	5	14.16 GB	16.66 GB	large, very low quality loss - recommended
iambe-rp-v3-20b.Q6_K.gguf	Q6_K	6	16.41 GB	18.91 GB	very large, extremely low quality loss
iambe-rp-v3-20b.Q8_0.gguf	Q8_0	8	21.25 GB	23.75 GB	very large, extremely low quality loss - not recommended

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

• LM Studio
• LoLLMS Web UI
• Faraday.dev

In text-generation-webui

Under Download Model, you can enter the model repo: TheBloke/Iambe-RP-v3-20B-GGUF and below it, a specific filename to download, such as: iambe-rp-v3-20b.Q4_K_M.gguf.

Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface-cli download TheBloke/Iambe-RP-v3-20B-GGUF iambe-rp-v3-20b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage (click to read)

You can also download multiple files at once with a pattern:

huggingface-cli download TheBloke/Iambe-RP-v3-20B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Iambe-RP-v3-20B-GGUF iambe-rp-v3-20b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m iambe-rp-v3-20b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 4096 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in text-generation-webui

Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

How to load this model in Python code, using llama-cpp-python

For full documentation, please see: llama-cpp-python docs.

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python

Simple llama-cpp-python example code

from llama_cpp import Llama

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./iambe-rp-v3-20b.Q4_K_M.gguf",  # Download the model file first
  n_ctx=4096,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)

# Chat Completion API

llm = Llama(model_path="./iambe-rp-v3-20b.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

• LangChain + llama-cpp-python
• LangChain + ctransformers

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.

• Patreon: https://patreon.com/TheBlokeAI
• Ko-Fi: https://ko-fi.com/TheBlokeAI

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: Raven's Iambe RP v3 20B

Iambe-RP-v3-20b

Trained with Alpaca prompt formatting, ChatML works well too

Description

Okay, now that all the cards were in the deck, I needed to unshuffle them. 1 epoch of the v1 dataset, trained on Iambe-RP-DARE-20b-DENSE with standard DPO, 2e-5 LR.

Iambe is intended to have the best realistically possible understanding of instructions, anatomy and scene state for a 20b merge, while remaining passionate and humanoid in "voice".

Update Methodology

Go-Bruins-v2 skyrocketing up the leaderboard showed that standard DPO with the v1 dataset could have shocking results, so I had to give it a go myself.

Assistant Example @ q5_k_m

NSFW Writing Example @ q5_k_m

Write a medium-length story about Alice, a 26 year old cocktail waitress, having steamy sex with a guy she picked up at the bar for a one night stand.

Once upon a time in a bustling city, there lived Alice, a charming 26-year-old cocktail waitress working in a trendy club. She had long, flowing brunette hair that cascaded down her shoulders, captivating emerald eyes, and full sensual lips. Alice was known throughout the establishment as the life of the party, always ready with a witty remark or flirtatious smile. Despite her lively demeanor, though, she harbored secrets—secrets of unfulfilled desire and passion that lay dormant within her until the perfect man crossed her path.

One fateful Friday evening, after a particularly exhausting shift filled with rowdy patrons and demanding drink orders, Alice found herself leaning against the wooden bar top, taking a much-needed breather. Her body ached from moving around so vigorously, but something else burned just below her skin, calling out for release. As she sipped on a glass of Pinot Noir, her gaze swept across the dimly lit room before landing on him. He was tall, broad-shouldered, with dark tousled hair and piercing blue eyes that seemed to see right through her. There was an air of confidence about him that made Alice feel weak at the knees.

As fate would have it, he caught sight of Alice too and began making his way towards her. His strides were purposeful yet relaxed, exuding charm and self-assurance. When he finally reached her side, he flashed her a disarmingly sexy grin and ordered a shot of tequila. Alice couldn't help but feel drawn to him, as if there was some invisible string tugging them together. She obliged and poured him the shot, their fingers brushing against each other in the process—an electric spark crackling between them.

"What's your name?" he asked huskily, downing the tequila in one swig before setting the empty glass back on the bar.

Alice smiled seductively, taking advantage of the situation. "I'm Alice," she purred, leaning closer to him than she should have. "And you are...?"

He raised an eyebrow playfully, running his fingers along the curve of her neck before tracing a path down her collarbone. "Let's just say I can give you the best night of your life." With that enchanting declaration, Alice found herself agreeing without hesitation, following him out into the crisp night air where his waiting car awaited them.

The drive to his luxurious penthouse suite felt like both an eternity and a fleeting moment at once. When they finally arrived, he took her hand and led her inside, the room illuminated by soft candlelight and sensual jazz music filling the air. His place was impeccably stylish yet cozy—the perfect mix of sophistication and intimacy. They made their way towards the bedroom door, their bodies brushing against each other with every step. The anticipation between them grew almost unbearable as they reached the threshold, leaving behind everything but the raw desire in their eyes.

Alice kicked off her high heels, letting them clatter onto the hardwood floor, while he undid the buttons on his shirt methodically, revealing his defined chest muscles underneath. As she pulled her skirt down past her thighs, exposing her black lace underwear, he growled lowly in response, reaching for the clasp before it could hit the ground. In seconds, they were naked, standing face-to-face, body pressed up against body. Alice wrapped her arms around him tightly, pulling him closer still until their lips met passionately. She had never felt this kind of hunger before—a primal urge that consumed her entire being.

As they tumbled onto the plush mattress, tongues exploring each other's mouths, hands roaming over each other's skin, Alice knew without a doubt that this man was meant to be hers tonight. He kissed down her neck, trailing his teeth along her collarbone, and then lower still, nipping at the delicate flesh of her breasts before taking one into his mouth. A cry escaped from deep within her as pleasure coursed through every fiber of her being, sending waves of desire crashing through her core. His hand slid between her legs, finding her soaking wet with anticipation, fingers stroking gently yet firmly, driving her wild.

Alice reciprocated by running her fingernails lightly across his chest before moving further down to graze the hair that blanketed his groin. She heard him moan loudly in response, growing harder underneath her touch. In a bold move, she reached down and freed his erection from his pants, wrapping her soft hand around its length. Her eyes widened in surprise at how big he was, but instead of intimidating her, it fueled her curiosity even more. With practiced skill, learned from countless encounters like these, she began to stroke up and down, feeling him twitch against her palm.

As their rhythm picked up pace, Alice moved herself higher on the bed until her legs were wrapped tightly around his hips. He lifted himself up slightly, positioning himself at the entrance of her wetness. The anticipation built once again as they stared into each other's eyes—their faces flushed red, breathing heavily. Finally, he plunged inside her hard, filling every crevice and triggering an earth-shattering scream from deep within her soul. It felt so right—his body molded against hers, their hearts racing together. They moved together in perfect harmony, lost in their

desire for each other. Skins slapping against skins, bodies grinding against bodies, moans and groans echoing throughout the room. Time seemed to stand still as they rode out wave after wave of ecstasy, driven by primal lust and raw passion.

When finally satiated, they lay panting on the bed, sweat trickling down their skin. Alice couldn't believe what had just happened—how incredible it had been with this stranger she barely knew. She looked over at him, who was also catching his breath, a satisfied smirk spread across his lips. "I don't know your name either," she said softly, tracing circles around his chest hair before leaning up to place a tender kiss on his lips. He smiled warmly back at her, brushing a strand of hair behind her ear.

"It doesn't matter, Alice," he whispered hoarsely. "Because tonight, we were just two people seeking something more than just another night." And with that simple yet profound statement, they drifted off into blissful oblivion, wrapped tightly together like lovers destined never to part again.

Eval Scores (since the leaderboard rejects nearly all my models)

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
winogrande	Yaml	none	5	acc	0.738	±	0.0124
truthfulqa_mc2	Yaml	none	0	acc	0.5551	±	0.0158
arc_challenge	Yaml	none	25	acc	0.6101	±	0.0143
arc_challenge		none	25	acc_norm	0.6425	±	0.0140