TheBloke/Tulpar-7B-v0-GGUF

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

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Tulpar 7B v0 - GGUF

• Model creator: HyperbeeAI
• Original model: Tulpar 7B v0

Description

This repo contains GGUF format model files for HyperbeeAI's Tulpar 7B v0.

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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

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

Prompt template: User-Assistant-Hashes

### User: {prompt}
### Assistant:

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d36d5be95a0d9088b674dbb27354107221

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
tulpar-7b-v0.Q2_K.gguf	Q2_K	2	2.83 GB	5.33 GB	smallest, significant quality loss - not recommended for most purposes
tulpar-7b-v0.Q3_K_S.gguf	Q3_K_S	3	2.95 GB	5.45 GB	very small, high quality loss
tulpar-7b-v0.Q3_K_M.gguf	Q3_K_M	3	3.30 GB	5.80 GB	very small, high quality loss
tulpar-7b-v0.Q3_K_L.gguf	Q3_K_L	3	3.60 GB	6.10 GB	small, substantial quality loss
tulpar-7b-v0.Q4_0.gguf	Q4_0	4	3.83 GB	6.33 GB	legacy; small, very high quality loss - prefer using Q3_K_M
tulpar-7b-v0.Q4_K_S.gguf	Q4_K_S	4	3.86 GB	6.36 GB	small, greater quality loss
tulpar-7b-v0.Q4_K_M.gguf	Q4_K_M	4	4.08 GB	6.58 GB	medium, balanced quality - recommended
tulpar-7b-v0.Q5_0.gguf	Q5_0	5	4.65 GB	7.15 GB	legacy; medium, balanced quality - prefer using Q4_K_M
tulpar-7b-v0.Q5_K_S.gguf	Q5_K_S	5	4.65 GB	7.15 GB	large, low quality loss - recommended
tulpar-7b-v0.Q5_K_M.gguf	Q5_K_M	5	4.78 GB	7.28 GB	large, very low quality loss - recommended
tulpar-7b-v0.Q6_K.gguf	Q6_K	6	5.53 GB	8.03 GB	very large, extremely low quality loss
tulpar-7b-v0.Q8_0.gguf	Q8_0	8	7.16 GB	9.66 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/Tulpar-7B-v0-GGUF and below it, a specific filename to download, such as: tulpar-7b-v0.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>=0.17.1

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

huggingface-cli download TheBloke/Tulpar-7B-v0-GGUF tulpar-7b-v0.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/Tulpar-7B-v0-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:

HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Tulpar-7B-v0-GGUF tulpar-7b-v0.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

Windows CLI users: Use set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 before running the download command.

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d36d5be95a0d9088b674dbb27354107221 or later.

./main -ngl 32 -m tulpar-7b-v0.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### User: {prompt}\n### Assistant:"

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 from Python using ctransformers

First install the package

# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers

Simple example code to load one of these GGUF models

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/Tulpar-7B-v0-GGUF", model_file="tulpar-7b-v0.q4_K_M.gguf", model_type="llama", gpu_layers=50)

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

How to use with LangChain

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

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: HyperbeeAI's Tulpar 7B v0

Model Description

Tulpar-7b is a LLama2-7b-based model trained by HyperbeeAI. Training is done on a filtered and preprocessed instruction finetuning dataset that includes GPT-4 generated and generally curated datasets like Airoboros and Platypus.

Example Usage

Loading the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HyperbeeAI/Tulpar-7b-v0")
model = AutoModelForCausalLM.from_pretrained("HyperbeeAI/Tulpar-7b-v0", device_map="auto")

You can run inference with both of the following prompts:

input_text="What is deep learning?"
prompt = f"### User: {input_text}\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=512)
print(tokenizer.decode(output[0]))

input_text="What is deep learning?"
prompt = f"Question: {input_text}\n\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=512)
print(tokenizer.decode(output[0]))

Evaluation

Our offline HF Leaderboard evaluation results:

Task	Metric	Value
arc_challenge	acc_norm	0.5614
hellaswag	acc_norm	0.7901
mmlu	acc_norm	0.5242
truthfulqa_mc	mc2	0.5160
Average	-	0.5979

Other GPT4All evaluation results:

Task	Metric	Value
boolq	acc	0.8306
piqa	acc	0.7905
	acc_norm	0.7884
winogrande	acc	0.7159
openbookqa	acc	0.356
	acc_norm	0.448
Average (including HF leaderboard datasets)		0.6468

BigBenchHard results:

Task	Metric	Value
bigbench_causal_judgement	multiple_choice_grade	0.6105
bigbench_date_understanding	multiple_choice_grade	0.6423
bigbench_disambiguation_qa	multiple_choice_grade	0.3643
bigbench_dyck_languages	multiple_choice_grade	0.2000
bigbench_formal_fallacies_syllogisms_negation	multiple_choice_grade	0.5002
bigbench_geometric_shapes	multiple_choice_grade	0.0000
	exact_str_match	0.0000
bigbench_hyperbaton	multiple_choice_grade	0.6754
bigbench_logical_deduction_five_objects	multiple_choice_grade	0.2700
bigbench_logical_deduction_seven_objects	multiple_choice_grade	0.1929
bigbench_logical_deduction_three_objects	multiple_choice_grade	0.4133
bigbench_movie_recommendation	multiple_choice_grade	0.3000
bigbench_navigate	multiple_choice_grade	0.5000
bigbench_reasoning_about_colored_objects	multiple_choice_grade	0.5750
bigbench_ruin_names	multiple_choice_grade	0.3281
bigbench_salient_translation_error_detection	multiple_choice_grade	0.2976
bigbench_snarks	multiple_choice_grade	0.6022
bigbench_sports_understanding	multiple_choice_grade	0.5122
bigbench_temporal_sequences	multiple_choice_grade	0.1450
bigbench_tracking_shuffled_objects_five_objects	multiple_choice_grade	0.1976
bigbench_tracking_shuffled_objects_seven_objects	multiple_choice_grade	0.1440
bigbench_tracking_shuffled_objects_three_objects	multiple_choice_grade	0.4133
Average		0.3754

Ethical Considerations and Limitations

Tulpar is a technology with potential risks and limitations. This model is finetuned only in English and all language-related scenarios are not covered. As HyperbeeAI, we neither guarantee ethical, accurate, unbiased, objective responses nor endorse its outputs. Before deploying this model, you are advised to make safety tests for your use case.