TheBloke/Inkbot-13B-8k-0.2-GGUF

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

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Inkbot 13B 8K 0.2 - GGUF

• Model creator: Adam Brusselback
• Original model: Inkbot 13B 8K 0.2

Description

This repo contains GGUF format model files for Adam Brusselback's Inkbot 13B 8K 0.2.

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 incomplate 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.
• LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
• 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.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
• 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.

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
• Adam Brusselback's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: Inkbot

<#meta#>
- Date: [DATE]
- Task: [TASK TYPE]
<#system#>
{system_message}
<#chat#>
<#user#>
{prompt}
<#bot#>

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
inkbot-13b-8k-0.2.Q2_K.gguf	Q2_K	2	5.43 GB	7.93 GB	smallest, significant quality loss - not recommended for most purposes
inkbot-13b-8k-0.2.Q3_K_S.gguf	Q3_K_S	3	5.66 GB	8.16 GB	very small, high quality loss
inkbot-13b-8k-0.2.Q3_K_M.gguf	Q3_K_M	3	6.34 GB	8.84 GB	very small, high quality loss
inkbot-13b-8k-0.2.Q3_K_L.gguf	Q3_K_L	3	6.93 GB	9.43 GB	small, substantial quality loss
inkbot-13b-8k-0.2.Q4_0.gguf	Q4_0	4	7.37 GB	9.87 GB	legacy; small, very high quality loss - prefer using Q3_K_M
inkbot-13b-8k-0.2.Q4_K_S.gguf	Q4_K_S	4	7.41 GB	9.91 GB	small, greater quality loss
inkbot-13b-8k-0.2.Q4_K_M.gguf	Q4_K_M	4	7.87 GB	10.37 GB	medium, balanced quality - recommended
inkbot-13b-8k-0.2.Q5_0.gguf	Q5_0	5	8.97 GB	11.47 GB	legacy; medium, balanced quality - prefer using Q4_K_M
inkbot-13b-8k-0.2.Q5_K_S.gguf	Q5_K_S	5	8.97 GB	11.47 GB	large, low quality loss - recommended
inkbot-13b-8k-0.2.Q5_K_M.gguf	Q5_K_M	5	9.23 GB	11.73 GB	large, very low quality loss - recommended
inkbot-13b-8k-0.2.Q6_K.gguf	Q6_K	6	10.68 GB	13.18 GB	very large, extremely low quality loss
inkbot-13b-8k-0.2.Q8_0.gguf	Q8_0	8	13.83 GB	16.33 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/Inkbot-13B-8k-0.2-GGUF and below it, a specific filename to download, such as: inkbot-13b-8k-0.2.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/Inkbot-13B-8k-0.2-GGUF inkbot-13b-8k-0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage

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

huggingface-cli download TheBloke/Inkbot-13B-8k-0.2-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/Inkbot-13B-8k-0.2-GGUF inkbot-13b-8k-0.2.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 32 -m inkbot-13b-8k-0.2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<#meta#>\n- Date: [DATE]\n- Task: [TASK TYPE]\n<#system#>\n{system_message}\n<#chat#>\n<#user#>\n{prompt}\n<#bot#>"

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.

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 here: text-generation-webui/docs/llama.cpp.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.

How to load this model in Python code, using ctransformers

First install the package

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

# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers

Simple ctransformers example code

from ctransformers import AutoModelForCausalLM

# 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 = AutoModelForCausalLM.from_pretrained("TheBloke/Inkbot-13B-8k-0.2-GGUF", model_file="inkbot-13b-8k-0.2.Q4_K_M.gguf", model_type="llama", gpu_layers=50)

print(llm("AI is going to"))

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: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Adam Brusselback's Inkbot 13B 8K 0.2

Model Card for Inkbot

Model Details

Inkbot is a conversational AI model designed to interpret and respond to structured prompts with or without contextual information. Its unique feature lies in the structured prompt system, allowing users to engage in dynamic dialogues that can evolve based on context, metadata, and user input.

Performance

• The model excels in RAG type queries, answering from context, and overriding memory when necessary.
• It can handle very large contexts, but may sometimes enter a repeating text loop, especially during complex tasks.
• The model is intended to be more functional and less chatty, avoiding the waste of tokens on superfluous language.

How to Use

Inkbot uses a structured prompt template system.

Prompt Template Structure

With Context:

<#meta#>
- Date: {current_date}
- Task: {task_name}
<#system#>
{system_prompt}
<#chat#>
<#user#>
{user}
<#user_context#>
{user_context}
<#bot#>
{bot}

Without Context:

<#meta#>
- Date: {current_date}
- Task: {task_name}
<#system#>
{system_prompt}
<#chat#>
<#user#>
{user}
<#bot#>
{bot}

Examples

With Context:

<#meta#>
- Date: 2023-07-20
- Task: general
<#system#>
A dialogue focused on determining dates based on context clues.
<#chat#>
<#user#>
What is today's date?
<#bot#>
The date is: 2023-07-20
<#user#>
When did the event in the provided context take place?
<#user_context#>
It's the day after new years eve, and I'm starting 2018 with a headache.
<#bot#>
The event in the context took place on: 2018-01-01

Without Context:

<#meta#>
- Date: 2023-07-20
- Task: general
<#system#>
A simple dialogue about daily queries.
<#chat#>
<#user#>
Will it rain today?
<#bot#>
I am not sure, without knowing your location I am unable to determine the weather.
<#user#>
Can you compose a haiku for me?
<#bot#>
Whispers of the breeze,
Nature's melodies unfold,
Dawn dispels the night.

Task Options

The model has been trained on a variety of tasks. Below is a breakdown of these tasks, along with example prompts to help guide your usage.

2. Content Generation

Tasks in this category involve creating or expanding content.

kg_writer (800 examples)

Example Prompts:

• "Using the provided knowledge graph, write an article about the topics and entities in the graph, incorporating the linked ideas. Use idea tags while writing to help focus."
• "Construct a story based on the information in the knowledge graph."

summary (1,600 examples)

Example Prompts:

• "Generate an extensive summary of the given document."
• "Please read the provided document to understand the context and content. Use this understanding to generate a summary. Separate the article into chunks, and sequentially create a summary for each chunk. Give me a final summary in the end."

paraphrase (1,100 examples)

Example Prompts:

• "Rephrase the following sentence while retaining its original meaning."
• "Can you provide an alternative wording for the paragraph below?"

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3. Content Analysis

Tasks in this category evaluate, grade, or filter content.

grading (400 examples)

Example Prompts:

• "Based on the provided document, please rate the usefulness as training data on a scale from 0-5."

sponsorblock (5,200 examples)

Example Prompts:

• "Read the document and extract any sentences or phrases that contain explicit mentions of sponsorship, promotional partnerships, or any form of paid content."

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4. Information Structuring

Tasks in this category involve the structured representation or extraction of information.

kg (3,600 examples)

Example Prompts:

• "Create a Knowledge Graph from the document provided."
• "Extract key concepts and relationships from the conversation to form a knowledge graph."

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5. General Interaction

Tasks in this category are designed for general questions and interactions.

general (1,600 examples)

Example Prompts:

• "What is the capital of France?"
• "Explain particle physics to a 5 years old."

Limitations

• Adhere to the prompt structure for best results.
• When providing contextual details, clarity is essential for Inkbot to derive accurate and meaningful responses.
• The overriding memory from user_context property generally only works for the next prompt or two, after which the model reverts to its original behavior.
• On complex tasks, like creating a coherent story based on a set of facts from context, there's a potential for a repeating text loop as context fills.
• Sometimes the model doesn't know when to end a knowledge graph, which can result in adding nodes and edges until it runs out of context.

Additional Notes

• Use rope-freq-scale=0.5 or compress_pos_emb=2 for 8k ctx
• The 'date', 'task', and 'system' are crucial metadata components that need to be provided outside the core dialogue.
• Use the 'user_context' when you want to offer supplementary context that guides Inkbot's response. You can interleave it in the chat log as necessary. It comes after the users instruction.
• The specific tag format, such as <#word#>, is used to because there are filters in a lot of APIs for <|word|> and this makes interactions easier.

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license: llama2