Original llama.cpp quant methods: q4_0, q4_1, q5_0, q5_1, q8_0
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit 2d5db48.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit 2d43387.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
Explanation of the new k-quant methods
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
GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
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
stable-vicuna-13B.ggmlv3.q2_K.bin
q2_K
2
5.43 GB
7.93 GB
New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
stable-vicuna-13B.ggmlv3.q3_K_L.bin
q3_K_L
3
6.87 GB
9.37 GB
New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
stable-vicuna-13B.ggmlv3.q3_K_M.bin
q3_K_M
3
6.25 GB
8.75 GB
New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
stable-vicuna-13B.ggmlv3.q3_K_S.bin
q3_K_S
3
5.59 GB
8.09 GB
New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
stable-vicuna-13B.ggmlv3.q4_0.bin
q4_0
4
7.32 GB
9.82 GB
Original llama.cpp quant method, 4-bit.
stable-vicuna-13B.ggmlv3.q4_1.bin
q4_1
4
8.14 GB
10.64 GB
Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
stable-vicuna-13B.ggmlv3.q4_K_M.bin
q4_K_M
4
7.82 GB
10.32 GB
New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
stable-vicuna-13B.ggmlv3.q4_K_S.bin
q4_K_S
4
7.32 GB
9.82 GB
New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
stable-vicuna-13B.ggmlv3.q5_0.bin
q5_0
5
8.95 GB
11.45 GB
Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
stable-vicuna-13B.ggmlv3.q5_1.bin
q5_1
5
9.76 GB
12.26 GB
Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
stable-vicuna-13B.ggmlv3.q5_K_M.bin
q5_K_M
5
9.21 GB
11.71 GB
New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
stable-vicuna-13B.ggmlv3.q5_K_S.bin
q5_K_S
5
8.95 GB
11.45 GB
New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
stable-vicuna-13B.ggmlv3.q6_K.bin
q6_K
6
10.68 GB
13.18 GB
New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors
stable-vicuna-13B.ggmlv3.q8_0.bin
q8_0
8
13.83 GB
16.33 GB
Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
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 run in llama.cpp
I use the following command line; adjust for your tastes and needs:
./main -t 10 -ngl 32 -m stable-vicuna-13B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8.
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
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.
Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
Patreon special mentions: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.
Thank you to all my generous patrons and donaters!
Original model card: CarperAI's Stable Vicuna 13B
StableVicuna-13B
Model Description
StableVicuna-13B is a Vicuna-13B v0 model fine-tuned using reinforcement learning from human feedback (RLHF) via Proximal Policy Optimization (PPO) on various conversational and instructional datasets.
Apply Delta Weights
StableVicuna-13B cannot be used from the CarperAI/stable-vicuna-13b-delta weights alone. To obtain the correct model, one must add back the difference between LLaMA 13B and CarperAI/stable-vicuna-13b-delta weights. We provide the apply_delta.py script to automate the conversion, which you can run as:
Once the delta weights are applied, get started chatting with the model by using the transformers library. Following a suggestion from Vicuna Team with Vicuna v0 you should install transformers with this version:
Contact: For questions and comments about the model, visit the CarperAI and StableFoundation Discord servers.
Hyperparameter
Value
\(n_\text{parameters}\)
13B
\(d_\text{model}\)
5120
\(n_\text{layers}\)
40
\(n_\text{heads}\)
40
Training
Training Dataset
StableVicuna-13B is fine-tuned on a mix of three datasets. OpenAssistant Conversations Dataset (OASST1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages distributed across 66,497 conversation trees, in 35 different languages;
GPT4All Prompt Generations, a dataset of 400k prompts and responses generated by GPT-4; and Alpaca, a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine.
The reward model used during RLHF was also trained on OpenAssistant Conversations Dataset (OASST1) along with two other datasets: Anthropic HH-RLHF, a dataset of preferences about AI assistant helpfulness and harmlessness; and Stanford Human Preferences Dataset a dataset of 385K collective human preferences over responses to questions/instructions in 18 different subject areas, from cooking to legal advice.
Training Procedure
CarperAI/stable-vicuna-13b-delta was trained using PPO as implemented in trlX with the following configuration:
Hyperparameter
Value
num_rollouts
128
chunk_size
16
ppo_epochs
4
init_kl_coef
0.1
target
6
horizon
10000
gamma
1
lam
0.95
cliprange
0.2
cliprange_value
0.2
vf_coef
1.0
scale_reward
None
cliprange_reward
10
generation_kwargs
max_length
512
min_length
48
top_k
0.0
top_p
1.0
do_sample
True
temperature
1.0
Use and Limitations
Intended Use
This model is intended to be used for text generation with a focus on conversational tasks. Users may further fine-tune the model on their own data to improve the model's performance on their specific tasks in accordance with the non-commercial license.
Limitations and bias
The base LLaMA model is trained on various data, some of which may contain offensive, harmful, and biased content that can lead to toxic behavior. See Section 5.1 of the LLaMA paper. We have not performed any studies to determine how fine-tuning on the aforementioned datasets affect the model's behavior and toxicity. Do not treat chat responses from this model as a substitute for human judgment or as a source of truth. Please use responsibly.
Acknowledgements
This work would not have been possible without the support of Stability AI.
Citations
bibtex
1@article{touvron2023llama,
2 title={LLaMA: Open and Efficient Foundation Language Models},
3 author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
4 journal={arXiv preprint arXiv:2302.13971},
5 year={2023}
6}
bibtex
1@misc{vicuna2023,
2 title = {Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality},
3 url = {https://vicuna.lmsys.org},
4 author = {Chiang, Wei-Lin and Li, Zhuohan and Lin, Zi and Sheng, Ying and Wu, Zhanghao and Zhang, Hao and Zheng, Lianmin and Zhuang, Siyuan and Zhuang, Yonghao and Gonzalez, Joseph E. and Stoica, Ion and Xing, Eric P.},
5 month = {March},
6 year = {2023}
7}
bibtex
1@misc{gpt4all,
2 author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar},
3 title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo},
4 year = {2023},
5 publisher = {GitHub},
6 journal = {GitHub repository},
7 howpublished = {\url{https://github.com/nomic-ai/gpt4all}},
8}
bibtex
1@misc{alpaca,
2 author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
3 title = {Stanford Alpaca: An Instruction-following LLaMA model},
4 year = {2023},
5 publisher = {GitHub},
6 journal = {GitHub repository},
7 howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
8}
bibtex
1@software{leandro_von_werra_2023_7790115,
2 author = {Leandro von Werra and
3 Alex Havrilla and
4 Max reciprocated and
5 Jonathan Tow and
6 Aman cat-state and
7 Duy V. Phung and
8 Louis Castricato and
9 Shahbuland Matiana and
10 Alan and
11 Ayush Thakur and
12 Alexey Bukhtiyarov and
13 aaronrmm and
14 Fabrizio Milo and
15 Daniel and
16 Daniel King and
17 Dong Shin and
18 Ethan Kim and
19 Justin Wei and
20 Manuel Romero and
21 Nicky Pochinkov and
22 Omar Sanseviero and
23 Reshinth Adithyan and
24 Sherman Siu and
25 Thomas Simonini and
26 Vladimir Blagojevic and
27 Xu Song and
28 Zack Witten and
29 alexandremuzio and
30 crumb},
31 title = {{CarperAI/trlx: v0.6.0: LLaMa (Alpaca), Benchmark
32 Util, T5 ILQL, Tests}},
33 month = mar,
34 year = 2023,
35 publisher = {Zenodo},
36 version = {v0.6.0},
37 doi = {10.5281/zenodo.7790115},
38 url = {https://doi.org/10.5281/zenodo.7790115}
39}
