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Llama 30B Instruct 2048 - GGUF

Model creator: upstage
Original model: Llama 30B Instruct 2048

Description

This repo contains GGUF format model files for Upstage's Llama 30B Instruct 2048.

Many thanks to William Beauchamp from Chai for providing the hardware used to make and upload these files!

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

Prompt template: Orca-Hashes

### System:
{system_message}

### User:
{prompt}

### 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
upstage-llama-30b-instruct-2048.Q2_K.gguf	Q2_K	2	13.50 GB	16.00 GB	smallest, significant quality loss - not recommended for most purposes
upstage-llama-30b-instruct-2048.Q3_K_S.gguf	Q3_K_S	3	14.06 GB	16.56 GB	very small, high quality loss
upstage-llama-30b-instruct-2048.Q3_K_M.gguf	Q3_K_M	3	15.76 GB	18.26 GB	very small, high quality loss
upstage-llama-30b-instruct-2048.Q3_K_L.gguf	Q3_K_L	3	17.28 GB	19.78 GB	small, substantial quality loss
upstage-llama-30b-instruct-2048.Q4_0.gguf	Q4_0	4	18.36 GB	20.86 GB	legacy; small, very high quality loss - prefer using Q3_K_M
upstage-llama-30b-instruct-2048.Q4_K_S.gguf	Q4_K_S	4	18.44 GB	20.94 GB	small, greater quality loss
upstage-llama-30b-instruct-2048.Q4_K_M.gguf	Q4_K_M	4	19.62 GB	22.12 GB	medium, balanced quality - recommended
upstage-llama-30b-instruct-2048.Q5_0.gguf	Q5_0	5	22.40 GB	24.90 GB	legacy; medium, balanced quality - prefer using Q4_K_M
upstage-llama-30b-instruct-2048.Q5_K_S.gguf	Q5_K_S	5	22.40 GB	24.90 GB	large, low quality loss - recommended
upstage-llama-30b-instruct-2048.Q5_K_M.gguf	Q5_K_M	5	23.05 GB	25.55 GB	large, very low quality loss - recommended
upstage-llama-30b-instruct-2048.Q6_K.gguf	Q6_K	6	26.69 GB	29.19 GB	very large, extremely low quality loss
upstage-llama-30b-instruct-2048.Q8_0.gguf	Q8_0	8	34.57 GB	37.07 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/upstage-llama-30b-instruct-2048-GGUF and below it, a specific filename to download, such as: upstage-llama-30b-instruct-2048.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/upstage-llama-30b-instruct-2048-GGUF upstage-llama-30b-instruct-2048.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/upstage-llama-30b-instruct-2048-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/upstage-llama-30b-instruct-2048-GGUF upstage-llama-30b-instruct-2048.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 upstage-llama-30b-instruct-2048.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System:\n{system_message}\n\n### User:\n{prompt}\n\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 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 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/upstage-llama-30b-instruct-2048-GGUF", model_file="upstage-llama-30b-instruct-2048.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:

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: Upstage's Llama 30B Instruct 2048

LLaMa-30b-instruct-2048 model card

Model Details

Developed by: Upstage
Backbone Model: LLaMA
Variations: It has different model parameter sizes and sequence lengths: 30B/1024, 30B/2048, 65B/1024
Language(s): English
Library: HuggingFace Transformers
License: This model is under a Non-commercial Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out this form, but have either lost your copy of the weights or encountered issues converting them to the Transformers format
Where to send comments: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the Hugging Face community's model repository
Contact: For questions and comments about the model, please email [email protected]

Dataset Details

Used Datasets

openbookqa
sciq
Open-Orca/OpenOrca
metaeval/ScienceQA_text_only
GAIR/lima
No other data was used except for the dataset mentioned above

Prompt Template

### System:
{System}

### User:
{User}

### Assistant:
{Assistant}

Usage

Tested on A100 80GB
Our model can handle up to 10k+ input tokens, thanks to the rope_scaling option

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

tokenizer = AutoTokenizer.from_pretrained("upstage/llama-30b-instruct-2048")
model = AutoModelForCausalLM.from_pretrained(
    "upstage/llama-30b-instruct-2048",
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_8bit=True,
    rope_scaling={"type": "dynamic", "factor": 2} # allows handling of longer inputs
)

prompt = "### User:\nThomas is healthy, but he has to go to the hospital. What could be the reasons?\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
del inputs["token_type_ids"]
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

Hardware and Software

Hardware: We utilized an A100x8 * 1 for training our model
Training Factors: We fine-tuned this model using a combination of the DeepSpeed library and the HuggingFace Trainer / HuggingFace Accelerate

Evaluation Results

Overview

We conducted a performance evaluation based on the tasks being evaluated on the Open LLM Leaderboard. We evaluated our model on four benchmark datasets, which include ARC-Challenge, HellaSwag, MMLU, and TruthfulQA We used the lm-evaluation-harness repository, specifically commit b281b0921b636bc36ad05c0b0b0763bd6dd43463
We used MT-bench, a set of challenging multi-turn open-ended questions, to evaluate the models

Main Results

Model	H4(Avg)	ARC	HellaSwag	MMLU	TruthfulQA	MT_Bench
Llama-2-70b-instruct-v2(Ours, Open LLM Leaderboard)	73	71.1	87.9	70.6	62.2	7.44063
Llama-2-70b-instruct (Ours, Open LLM Leaderboard)	72.3	70.9	87.5	69.8	61	7.24375
llama-65b-instruct (Ours, Open LLM Leaderboard)	69.4	67.6	86.5	64.9	58.8
Llama-2-70b-hf	67.3	67.3	87.3	69.8	44.9
llama-30b-instruct-2048 (Ours, Open LLM Leaderboard)	67.0	64.9	84.9	61.9	56.3
llama-30b-instruct (Ours, Open LLM Leaderboard)	65.2	62.5	86.2	59.4	52.8
llama-65b	64.2	63.5	86.1	63.9	43.4
falcon-40b-instruct	63.4	61.6	84.3	55.4	52.5

Scripts for H4 Score Reproduction

Prepare evaluation environments:

# clone the repository
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
# check out the specific commit
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
# change to the repository directory
cd lm-evaluation-harness

Ethical Issues

Ethical Considerations

There were no ethical issues involved, as we did not include the benchmark test set or the training set in the model's training process

Contact Us

Why Upstage LLM?

Upstage's LLM research has yielded remarkable results. As of August 1st, our 70B model has reached the top spot in openLLM rankings, marking itself as the current leading performer globally. Recognizing the immense potential in implementing private LLM to actual businesses, we invite you to easily apply private LLM and fine-tune it with your own data. For a seamless and tailored solution, please do not hesitate to reach out to us. ► click here to contact

Downloads last month: 293

GGUF

Model size

32.5B params

Architecture

llama

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Examples

Text Generation

Inference API (serverless) has been turned off for this model.

Model tree for TheBloke/upstage-llama-30b-instruct-2048-GGUF

Base model

upstage/llama-30b-instruct-2048

Quantized

(3)

this model

TheBloke
/

upstage-llama-30b-instruct-2048-GGUF

Llama 30B Instruct 2048 - GGUF

Description

About GGUF

Repositories available

Prompt template: Orca-Hashes

Compatibility

Explanation of quantisation methods

Provided files

How to download GGUF files

In `text-generation-webui`

On the command line, including multiple files at once

Example `llama.cpp` command

How to run in `text-generation-webui`

How to run from Python code

How to load this model in Python code, using ctransformers

First install the package

Simple ctransformers example code

How to use with LangChain

Discord

Thanks, and how to contribute

Original model card: Upstage's Llama 30B Instruct 2048

LLaMa-30b-instruct-2048 model card

Model Details

Dataset Details

Used Datasets

Prompt Template

Usage

Hardware and Software

Evaluation Results

Overview

Main Results

Scripts for H4 Score Reproduction

Ethical Issues

Ethical Considerations

Contact Us

Why Upstage LLM?

Model tree for TheBloke/upstage-llama-30b-instruct-2048-GGUF

Datasets used to train TheBloke/upstage-llama-30b-instruct-2048-GGUF

Llama 30B Instruct 2048 - GGUF

Description

About GGUF

Repositories available

Prompt template: Orca-Hashes

Compatibility

Explanation of quantisation methods

Provided files

How to download GGUF files

In text-generation-webui

On the command line, including multiple files at once

Example llama.cpp command

How to run in text-generation-webui

How to run from Python code

How to load this model in Python code, using ctransformers

First install the package

Simple ctransformers example code

How to use with LangChain

Discord

Thanks, and how to contribute

Original model card: Upstage's Llama 30B Instruct 2048

LLaMa-30b-instruct-2048 model card

Model Details

Dataset Details

Used Datasets

Prompt Template

Usage

Hardware and Software

Evaluation Results

Overview

Main Results

Scripts for H4 Score Reproduction

Ethical Issues

Ethical Considerations

Contact Us

Why Upstage LLM?

Model tree for TheBloke/upstage-llama-30b-instruct-2048-GGUF

Datasets used to train TheBloke/upstage-llama-30b-instruct-2048-GGUF

In `text-generation-webui`

Example `llama.cpp` command

How to run in `text-generation-webui`