datasets:
- shahules786/orca-chat
- rombodawg/MegaCodeTraining112k
- theblackcat102/evol-codealpaca-v1
- nickrosh/Evol-Instruct-Code-80k-v1
inference: false
license: other
model_creator: OpenAssistant
model_link: https://huggingface.co/OpenAssistant/llama2-13b-orca-v2-8k-3166
model_name: Llama2 13B Orca v2 8K
model_type: llama
quantized_by: TheBloke
Llama2 13B Orca v2 8K - GGML
- Model creator: OpenAssistant
- Original model: Llama2 13B Orca v2 8K
Description
This repo contains GGML format model files for OpenAssistant's Llama2 13B Orca v2 8K.
GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:
- text-generation-webui, the most popular web UI. Supports NVidia CUDA GPU acceleration.
- KoboldCpp, a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
- LM Studio, a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
- LoLLMS Web UI, a great web UI with CUDA GPU acceleration via the c_transformers backend.
- 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.
Repositories available
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
- OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: OpenAssistant
<|prompter|>{prompt}<|endoftext|><|assistant|>
Compatibility
Original llama.cpp quant methods: q4_0, q4_1, q5_0, q5_1, q8_0
These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.
New k-quant methods: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K
These new quantisation methods are compatible with llama.cpp as of June 6th, commit 2d43387.
They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.
Explanation of the new k-quant 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
- 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 |
|---|---|---|---|---|---|
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q2_K.bin | q2_K | 2 | 5.74 GB | 8.24 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. |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 7.14 GB | 9.64 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 |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.53 GB | 9.03 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 |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.87 GB | 8.37 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original quant method, 4-bit. |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 8.06 GB | 10.56 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 |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.56 GB | 10.06 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.40 GB | 11.90 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 |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 9.15 GB | 11.65 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q6_K.bin | q6_K | 6 | 10.83 GB | 13.33 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 GB | Original 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 openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_K_M.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
How to run in text-generation-webui
Further instructions here: text-generation-webui/docs/llama.cpp-models.md.
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!
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: Luke from CarbonQuill, Aemon Algiz.
Patreon special mentions: Willem Michiel, Ajan Kanaga, Cory Kujawski, Alps Aficionado, Nikolai Manek, Jonathan Leane, Stanislav Ovsiannikov, Michael Levine, Luke Pendergrass, Sid, K, Gabriel Tamborski, Clay Pascal, Kalila, William Sang, Will Dee, Pieter, Nathan LeClaire, ya boyyy, David Flickinger, vamX, Derek Yates, Fen Risland, Jeffrey Morgan, webtim, Daniel P. Andersen, Chadd, Edmond Seymore, Pyrater, Olusegun Samson, Lone Striker, biorpg, alfie_i, Mano Prime, Chris Smitley, Dave, zynix, Trenton Dambrowitz, Johann-Peter Hartmann, Magnesian, Spencer Kim, John Detwiler, Iucharbius, Gabriel Puliatti, LangChain4j, Luke @flexchar, Vadim, Rishabh Srivastava, Preetika Verma, Ai Maven, Femi Adebogun, WelcomeToTheClub, Leonard Tan, Imad Khwaja, Steven Wood, Stefan Sabev, Sebastain Graf, usrbinkat, Dan Guido, Sam, Eugene Pentland, Mandus, transmissions 11, Slarti, Karl Bernard, Spiking Neurons AB, Artur Olbinski, Joseph William Delisle, ReadyPlayerEmma, Olakabola, Asp the Wyvern, Space Cruiser, Matthew Berman, Randy H, subjectnull, danny, John Villwock, Illia Dulskyi, Rainer Wilmers, theTransient, Pierre Kircher, Alexandros Triantafyllidis, Viktor Bowallius, terasurfer, Deep Realms, SuperWojo, senxiiz, Oscar Rangel, Alex, Stephen Murray, Talal Aujan, Raven Klaugh, Sean Connelly, Raymond Fosdick, Fred von Graf, chris gileta, Junyu Yang, Elle
Thank you to all my generous patrons and donaters!
Original model card: OpenAssistant's Llama2 13B Orca v2 8K
- wandb: jlhr5cf2
- sampling-report: 2023-07-31_OpenAssistant_llama2-13b-orca-v2-8k-3166_sampling_llama2_prompt.json
Model Configuration
llama2-13b-orca-v2-8k:
rng_seed: 0xe1291f21
show_dataset_stats: true
random_offset_probability: 0.0
use_custom_sampler: true
sort_by_length: false
dtype: fp16
log_dir: /mnt/data/ikka/data_cache/llama2_13b_orcav2_logs
output_dir: /mnt/data/ikka/data_cache/llama2_13b_orcav2
learning_rate: 1e-5
model_name: conceptofmind/LLongMA-2-13b
deepspeed_config: configs/zero_config_pretrain.json
weight_decay: 0.000001
max_length: 8192
warmup_steps: 100
peft_model: false
use_flash_attention: true
gradient_checkpointing: true
gradient_accumulation_steps: 4
per_device_train_batch_size: 2
per_device_eval_batch_size: 1
residual_dropout: 0.0
eval_steps: 200
save_steps: 200
num_train_epochs: 1
save_total_limit: 4
superhot: false
superhot_config:
type: linear
scaling_factor: 2
datasets:
- orca-chat: # shahules786/orca-chat
data_files: orca-chat-gpt4-8k.json
max_val_set: 5000
val_split: 0.1
- evol-codealpaca-v1: # theblackcat102/evol-codealpaca-v1
fill_min_length: 20000
val_split: 0.1
- megacode: # rombodawg/MegaCodeTraining112k
fill_min_length: 24000
val_split: 0.1
max_val_set: 1000
- evol_instruct_code: # nickrosh/Evol-Instruct-Code-80k-v1
fill_min_length: 24000
val_split: 0.1
max_val_set: 1000