TheBloke/Writing_Partner_Mistral_7B-GGUF

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

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Writing Partner Mistral 7B - GGUF

• Model creator: FPHam
• Original model: Writing Partner Mistral 7B

Description

This repo contains GGUF format model files for FPHam's Writing Partner Mistral 7B.

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

Prompt template: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

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
writing_partner_mistral_7b.Q2_K.gguf	Q2_K	2	3.08 GB	5.58 GB	smallest, significant quality loss - not recommended for most purposes
writing_partner_mistral_7b.Q3_K_S.gguf	Q3_K_S	3	3.16 GB	5.66 GB	very small, high quality loss
writing_partner_mistral_7b.Q3_K_M.gguf	Q3_K_M	3	3.52 GB	6.02 GB	very small, high quality loss
writing_partner_mistral_7b.Q3_K_L.gguf	Q3_K_L	3	3.82 GB	6.32 GB	small, substantial quality loss
writing_partner_mistral_7b.Q4_0.gguf	Q4_0	4	4.11 GB	6.61 GB	legacy; small, very high quality loss - prefer using Q3_K_M
writing_partner_mistral_7b.Q4_K_S.gguf	Q4_K_S	4	4.14 GB	6.64 GB	small, greater quality loss
writing_partner_mistral_7b.Q4_K_M.gguf	Q4_K_M	4	4.37 GB	6.87 GB	medium, balanced quality - recommended
writing_partner_mistral_7b.Q5_0.gguf	Q5_0	5	5.00 GB	7.50 GB	legacy; medium, balanced quality - prefer using Q4_K_M
writing_partner_mistral_7b.Q5_K_S.gguf	Q5_K_S	5	5.00 GB	7.50 GB	large, low quality loss - recommended
writing_partner_mistral_7b.Q5_K_M.gguf	Q5_K_M	5	5.13 GB	7.63 GB	large, very low quality loss - recommended
writing_partner_mistral_7b.Q6_K.gguf	Q6_K	6	5.94 GB	8.44 GB	very large, extremely low quality loss
writing_partner_mistral_7b.Q8_0.gguf	Q8_0	8	7.70 GB	10.20 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/Writing_Partner_Mistral_7B-GGUF and below it, a specific filename to download, such as: writing_partner_mistral_7b.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/Writing_Partner_Mistral_7B-GGUF writing_partner_mistral_7b.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/Writing_Partner_Mistral_7B-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/Writing_Partner_Mistral_7B-GGUF writing_partner_mistral_7b.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 writing_partner_mistral_7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"

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

Change -c 2048 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 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.

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/Writing_Partner_Mistral_7B-GGUF", model_file="writing_partner_mistral_7b.Q4_K_M.gguf", model_type="mistral", 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: 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: FPHam's Writing Partner Mistral 7B

Support me at Ko-fi

Hi there! I'm your writing assistant.

Think of me as your partner in crime---or rather, your partner in writing. When you hit a wall (and trust me, you will), I'll be there to knock it down for you.

If you find yourself stumped, I can help you think outside the box, whether you need to figure out what happens next, develop interesting sidekicks, or create a believable love interest. And if you get stuck on names for people or places, well, that's my specialty.

Just give me a shout if you have questions, need advice, or want to bounce an idea off someone. Together, we'll turn your novel into a best seller!

Intended Use

The purpose of this model isn't to write content for you (although it can), but rather to engage in a conversation about your writing process.

Model uses ChatML

<|im_start|>system
<|im_end|>
<|im_start|>user
How to plot my story?<|im_end|>
<|im_start|>assistant

Example 1: (I had ChatGPT generate a paragraph for a sci-fi story.)

Here is an opening of my story:

In the distant reaches of the cosmos, where starlight weaves a tapestry of unknown wonders, Captain Alara Voss navigated her spacecraft, the Celestial Serenity, through the interstellar currents. The ship hummed with the pulsating energy of the quantum drive, propelling them toward the uncharted nebula known as the Veil of Eternity. Alara's gaze was fixed on the swirling cosmic clouds ahead, her mind ablaze with the anticipation of discovery. As the Serenity pierced the ethereal veil, the fabric of reality seemed to dance with a mysterious resonance. Little did the intrepid crew know that beyond the shimmering curtain lay a realm where time itself wove a narrative that defied mortal understanding, setting the stage for an odyssey that would reshape the boundaries of human perception.

I like this opening! It sets up some interesting questions and intrigue right off the bat. But I do think you might want to reconsider your point of view in the first paragraph. Right now it sounds more like a summary than an actual scene because there isn't any dialogue or action. Consider starting with something more immediate, perhaps from Alara's perspective. You could even begin with her looking out at the stars, thinking about what she sees. This will give us a chance to get to know her better before you launch into the big picture stuff. Here's an example:

Alara Voss leaned against the cold metal railing of the observation deck, staring out into the infinite expanse of blackness that surrounded her ship. Stars dotted the night sky like scattered diamonds, each one a potential destination, each one a mystery waiting to be solved. She took a deep breath and let it out slowly, savoring the feeling of freedom that washed over her. It had been months since they'd left Earth, and already their journey had taken them light-years away from everything they knew. Her thoughts were interrupted by the low hum of the quantum drive kicking in, signaling that they were ready to make another jump. She watched as the stars began to blur together, forming a shimmering pathway that led the way to their next adventure.

"Captain," came the voice of her second-in-command over the intercom. "We're entering the Veil of Eternity."

"Thank you, Ensign," Alara replied, not taking her eyes off the swirling vortex ahead. "Let's see what lies beyond."

Example 2:

How to write proper dialogue?

Dialogue is a very important part of writing fiction. It's the way characters speak, and it helps define who they are. It also moves the story forward and adds tension, humor, or emotion. Dialogue must be realistic and natural-sounding, but it doesn't have to be boring. You want your readers to pay attention to what the characters are saying, so make sure that each line of dialogue counts.

Here are some tips on how to write good dialogue:

• Keep it short. Long speeches get tedious quickly. If one character talks for more than two sentences, break up his speech with an action or interjection from another character.

• Use contractions (I'm, he'll). They sound more like real conversation than formal language does.

• Vary sentence structure. This makes dialogue easier to read and understand.

• Don't use slang unless it fits the setting or character perfectly. Slang dates easily and may not mean anything to future generations.

• Avoid using too many adverbs in dialogue tags. Instead of "he said loudly," try something like "he bellowed." Or "she whispered."

• Be careful not to give away too much information in dialogue tags. For example, if you say "Sarah laughed nervously," you're telling the reader she was nervous rather than showing her anxiety through her words or actions.

• Make sure your characters don't all talk alike. Each person should have a unique voice, based on his background, education, age, gender, etc.

• Don't forget about body language. The way someone holds himself, sits, stands, fidgets, or looks at others can add depth to a scene.

• Use dialogue to reveal character traits and motivations. Listen carefully to people around you, and then translate those conversations onto paper.

• Finally, remember that dialogue needs to serve a purpose in your story. Whether it advances the plot, sets the mood, or creates conflict, every word spoken by your characters should move the narrative along.