language:
- en
license: llama2
library_name: transformers
datasets:
- togethercomputer/llama-instruct
model_name: Llama2 7B 32K Instruct
base_model: togethercomputer/Llama-2-7B-32K-Instruct
inference: false
model_creator: Together
model_type: llama
prompt_template: |
[INST]
{prompt}
[\INST]
quantized_by: TheBloke
![TheBlokeAI](https://i.imgur.com/EBdldam.jpg)
TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
Llama2 7B 32K Instruct - GGUF
- Model creator: Together
- Original model: Llama2 7B 32K Instruct
Description
This repo contains GGUF format model files for Together's Llama2 7B 32K Instruct.
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
- Together's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Llama2-Instruct-Only
[INST]
{prompt}
[\INST]
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 |
---|---|---|---|---|---|
llama-2-7b-32k-instruct.Q2_K.gguf | Q2_K | 2 | 2.83 GB | 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
llama-2-7b-32k-instruct.Q3_K_S.gguf | Q3_K_S | 3 | 2.95 GB | 5.45 GB | very small, high quality loss |
llama-2-7b-32k-instruct.Q3_K_M.gguf | Q3_K_M | 3 | 3.30 GB | 5.80 GB | very small, high quality loss |
llama-2-7b-32k-instruct.Q3_K_L.gguf | Q3_K_L | 3 | 3.60 GB | 6.10 GB | small, substantial quality loss |
llama-2-7b-32k-instruct.Q4_0.gguf | Q4_0 | 4 | 3.83 GB | 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
llama-2-7b-32k-instruct.Q4_K_S.gguf | Q4_K_S | 4 | 3.86 GB | 6.36 GB | small, greater quality loss |
llama-2-7b-32k-instruct.Q4_K_M.gguf | Q4_K_M | 4 | 4.08 GB | 6.58 GB | medium, balanced quality - recommended |
llama-2-7b-32k-instruct.Q5_0.gguf | Q5_0 | 5 | 4.65 GB | 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
llama-2-7b-32k-instruct.Q5_K_S.gguf | Q5_K_S | 5 | 4.65 GB | 7.15 GB | large, low quality loss - recommended |
llama-2-7b-32k-instruct.Q5_K_M.gguf | Q5_K_M | 5 | 4.78 GB | 7.28 GB | large, very low quality loss - recommended |
llama-2-7b-32k-instruct.Q6_K.gguf | Q6_K | 6 | 5.53 GB | 8.03 GB | very large, extremely low quality loss |
llama-2-7b-32k-instruct.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/Llama-2-7B-32K-Instruct-GGUF and below it, a specific filename to download, such as: llama-2-7b-32k-instruct.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/Llama-2-7B-32K-Instruct-GGUF llama-2-7b-32k-instruct.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/Llama-2-7B-32K-Instruct-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/Llama-2-7B-32K-Instruct-GGUF llama-2-7b-32k-instruct.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 llama-2-7b-32k-instruct.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST]\n{prompt}\n[\INST]"
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/Llama-2-7B-32K-Instruct-GGUF", model_file="llama-2-7b-32k-instruct.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:
Discord
For further support, and discussions on these models and AI in general, join us at:
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: Together's Llama2 7B 32K Instruct
Llama-2-7B-32K-Instruct
Model Description
Llama-2-7B-32K-Instruct is an open-source, long-context chat model finetuned from Llama-2-7B-32K, over high-quality instruction and chat data. We built Llama-2-7B-32K-Instruct with less than 200 lines of Python script using Together API, and we also make the recipe fully available. We hope that this can enable everyone to finetune their own version of Llama-2-7B-32K — play with Together API and give us feedback!
Data Collection Details
Llama-2-7B-32K-Instruct is fine-tuned over a combination of two parts:
19K single- and multi-round conversations generated by human instructions and Llama-2-70B-Chat outputs. We collected the dataset following the distillation paradigm that is used by Alpaca, Vicuna, WizardLM, Orca — producing instructions by querying a powerful LLM (in this case, Llama-2-70B-Chat). The complete dataset is also released here. We also share the complete recipe for the data collection process here.
Long-context Summarization and Long-context QA. We follow the recipe of Llama-2-7B-32K, and train our model with the BookSum dataset and Multi-document Question Answering.
The final data mixture used for model finetuning is: 19K instruction (50%) + BookSum (25%) + MQA (25%).
Model Usage
We encourage you to try out this model using the Together API. The updated inference stack allows for efficient inference.
To run the model locally, we strongly recommend to install Flash Attention V2, which is necessary to obtain the best performance:
# Please update the path of `CUDA_HOME`
export CUDA_HOME=/usr/local/cuda-11.8
pip install transformers==4.31.0
pip install sentencepiece
pip install ninja
pip install flash-attn --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
You can load the model directly from the Hugging Face model hub using
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Llama-2-7B-32K-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/Llama-2-7B-32K-Instruct",
trust_remote_code=True, torch_dtype=torch.float16)
input_ids = tokenizer.encode("[INST]\nWrite a poem about cats\n[/INST]\n\n", return_tensors="pt")
output = model.generate(input_ids, max_length=128,
temperature=0.7, repetition_penalty=1.1, top_p=0.7, top_k=50)
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
The model is also hosted on Together Playground. You can simply play with the model by using prompt formatted by:
[INST]\n<your instruction here>\n[\INST]\n\n
For example, if we query the model with
[INST]\nWrite a poem about cats\n[\INST]\n\n
the model will return
[INST]
Write a poem about cats
[/INST]
Cats are mysterious creatures, with their aloof and independent nature. They're also incredibly beautiful, with their sleek fur and piercing eyes. Here's a poem that captures the essence of cats:
Cats, oh cats, how can I describe you?
Your beauty is beyond compare, it seems.
You're graceful and elegant, like a ballerina's dance,
But don't let your charm fool you, for you're not easily tamed.
With your soft purring and playful meows,
You draw us in with your enchanting powers.
We love to watch you play, your tail twirling 'round,
As if you're dancing on air, with no sound.
But don't be fooled by your sweetness, my friend,
For beneath that gentle exterior, lies a fierce defender.
When danger lurks, you'll spring into action,
Protecting those you hold dear, without question.
Solet us admire you, from afar,
For in your own way, you're truly unique, a star.
And though we may never fully understand,
The depths of your soul, we'll always stand, hand in paw, as one.
This poem captures the essence of cats, highlighting their beauty, independence,and protective nature. It also celebrates the special bond between humans and cats, recognizing their unique qualities and the joy they bring to our lives.
Model Evaluation
We evaluate the model from three aspects: 1) Alpaca Eval; 2) Rouge score over BookSum; and 3) Accuracy over Multi-document Question Answering (MQA). We compare with models including GPT-3.5-Turbo-16K, https://huggingface.co/meta-llama/Llama-2-7b-chat-hf, Longchat-7b-16k and Longchat-7b-v1.5-32k. We summarize the results below:
Alpaca Eval
Model win_rate standard_error n_total avg_length Llama-2-7B-Chat-hf 71.37 1.59 805 1479 Llama-2-7B-32K-Instruct 70.36 1.61 803 1885 oasst-rlhf-llama-33b 66.52 1.66 805 1079 text_davinci_003 50.00 0.00 805 307 falcon-40b-instruct 45.71 1.75 805 662 alpaca-farm-ppo-human 41.24 1.73 805 803 alpaca-7b 26.46 1.54 805 396 text_davinci_001 15.17 1.24 804 296 Rouge Score over BookSum
Model R1 R2 RL Llama-2-7B-Chat-hf 0.055 0.008 0.046 Longchat-7b-16k 0.303 0.055 0.160 Longchat-7b-v1.5-32k 0.308 0.057 0.163 GPT-3.5-Turbo-16K 0.324 0.066 0.178 Llama-2-7B-32K-Instruct (ours) 0.336 0.076 0.184 Accuracy over MQA
Model 20 docs (Avg 2.9K tokens) 30 docs (Avg 4.4K tokens) 50 docs (Avg 7.4K tokens) Llama-2-7B-Chat-hf 0.448 0.421 0.354 Longchat-7b-16k 0.510 0.473 0.428 Longchat-7b-v1.5-32k 0.534 0.516 0.479 GPT-3.5-Turbo-16K 0.622 0.609 0.577 Llama-2-7B-32K-Instruct (ours) 0.622 0.604 0.589
Limitations and Bias
As with all language models, Llama-2-7B-32K-Instruct may generate incorrect or biased content. It's important to keep this in mind when using the model.
Community
Join us on Together Discord