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OpenAssistant SFT 7 Llama 30B GGML

These files are GGML format model files for OpenAssistant SFT 7 Llama 30B.

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

Repositories available

Compatibility

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.

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 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
OpenAssistant-SFT-7-Llama-30B.ggmlv3.q2_K.bin	q2_K	2	13.60 GB	16.10 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-SFT-7-Llama-30B.ggmlv3.q3_K_L.bin	q3_K_L	3	17.20 GB	19.70 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-SFT-7-Llama-30B.ggmlv3.q3_K_M.bin	q3_K_M	3	15.64 GB	18.14 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-SFT-7-Llama-30B.ggmlv3.q3_K_S.bin	q3_K_S	3	13.98 GB	16.48 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
OpenAssistant-SFT-7-Llama-30B.ggmlv3.q4_0.bin	q4_0	4	18.30 GB	20.80 GB	Original llama.cpp quant method, 4-bit.
OpenAssistant-SFT-7-Llama-30B.ggmlv3.q4_K_M.bin	q4_K_M	4	19.57 GB	22.07 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-SFT-7-Llama-30B.ggmlv3.q4_K_S.bin	q4_K_S	4	18.30 GB	20.80 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
OpenAssistant-SFT-7-Llama-30B.ggmlv3.q5_0.bin	q5_0	5	22.37 GB	24.87 GB	Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
OpenAssistant-SFT-7-Llama-30B.ggmlv3.q5_1.bin	q5_1	5	24.40 GB	26.90 GB	Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
OpenAssistant-SFT-7-Llama-30B.ggmlv3.q5_K_M.bin	q5_K_M	5	23.02 GB	25.52 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-SFT-7-Llama-30B.ggmlv3.q5_K_S.bin	q5_K_S	5	22.37 GB	24.87 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
OpenAssistant-SFT-7-Llama-30B.ggmlv3.q6_K.bin	q6_K	6	26.69 GB	29.19 GB	New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors

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-SFT-7-Llama-30B.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

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:

TheBloke AI's Discord server

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
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Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

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Thank you to all my generous patrons and donaters!

Original model card: OpenAssistant SFT 7 Llama 30B

OpenAssistant LLaMA 30B SFT 7

Due to the license attached to LLaMA models by Meta AI it is not possible to directly distribute LLaMA-based models. Instead we provide XOR weights for the OA models.

Thanks to Mick for writing the xor_codec.py script which enables this process

The Process

Note: This process applies to oasst-sft-7-llama-30b model. The same process can be applied to other models in future, but the checksums will be different..

This process is tested only on Linux (specifically Ubuntu). Some users have reported that the process does not work on Windows. We recommend using WSL if you only have a Windows machine.

To use OpenAssistant LLaMA-Based Models, you should have a copy of the original LLaMA model weights and add them to a llama subdirectory here. If you cannot obtain the original LLaMA, see the note in italic below for a possible alternative.

Ensure your LLaMA 30B checkpoint matches the correct md5sums:

f856e9d99c30855d6ead4d00cc3a5573  consolidated.00.pth
d9dbfbea61309dc1e087f5081e98331a  consolidated.01.pth
2b2bed47912ceb828c0a37aac4b99073  consolidated.02.pth
ea0405cdb5bc638fee12de614f729ebc  consolidated.03.pth
4babdbd05b8923226a9e9622492054b6  params.json

If you do not have a copy of the original LLaMA weights and cannot obtain one, you may still be able to complete this process. Some users have reported that this model can be used as a base for the XOR conversion. This will also allow you to skip to Step 7. However, we only support conversion starting from LLaMA original checkpoint and cannot provide support if you experience issues with this alternative approach.

Important: Follow these exact steps to convert your original LLaMA checkpoint to a HuggingFace Transformers-compatible format. If you use the wrong versions of any dependency, you risk ending up with weights which are not compatible with the XOR files.

Create a clean Python 3.10 virtual environment & activate it:

python3.10 -m venv xor_venv
source xor_venv/bin/activate

Clone transformers repo and switch to tested version:

git clone https://github.com/huggingface/transformers.git
cd transformers
git checkout d04ec99bec8a0b432fc03ed60cea9a1a20ebaf3c
pip install .

Install exactly these dependency versions:

pip install torch==1.13.1 accelerate==0.18.0 sentencepiece==0.1.98 protobuf==3.20.1

Check pip freeze output:

accelerate==0.18.0
certifi==2022.12.7
charset-normalizer==3.1.0
filelock==3.12.0
huggingface-hub==0.13.4
idna==3.4
numpy==1.24.2
nvidia-cublas-cu11==11.10.3.66
nvidia-cuda-nvrtc-cu11==11.7.99
nvidia-cuda-runtime-cu11==11.7.99
nvidia-cudnn-cu11==8.5.0.96
packaging==23.1
protobuf==3.20.1
psutil==5.9.5
PyYAML==6.0
regex==2023.3.23
requests==2.28.2
sentencepiece==0.1.98
tokenizers==0.13.3
torch==1.13.1
tqdm==4.65.0
transformers @ file:///mnt/data/koepf/transformers
typing_extensions==4.5.0
urllib3==1.26.15

While in transformers repo root, run HF LLaMA conversion script:

python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir <input_path_llama_base>  --output_dir <output_path_llama30b_hf> --model_size 30B

Run find . -type f -exec md5sum "{}" + in the conversion target directory (output_dir). This should produce exactly the following checksums if your files are correct:

462a2d07f65776f27c0facfa2affb9f9  ./pytorch_model-00007-of-00007.bin
e1dc8c48a65279fb1fbccff14562e6a3  ./pytorch_model-00003-of-00007.bin
9cffb1aeba11b16da84b56abb773d099  ./pytorch_model-00001-of-00007.bin
aee09e21813368c49baaece120125ae3  ./generation_config.json
92754d6c6f291819ffc3dfcaf470f541  ./pytorch_model-00005-of-00007.bin
3eddc6fc02c0172d38727e5826181adb  ./pytorch_model-00004-of-00007.bin
eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model
99762d59efa6b96599e863893cf2da02  ./pytorch_model-00006-of-00007.bin
598538f18fed1877b41f77de034c0c8a  ./config.json
fdb311c39b8659a5d5c1991339bafc09  ./tokenizer.json
fecfda4fba7bfd911e187a85db5fa2ef  ./pytorch_model.bin.index.json
edd1a5897748864768b1fab645b31491  ./tokenizer_config.json
6b2e0a735969660e720c27061ef3f3d3  ./special_tokens_map.json
5cfcb78b908ffa02e681cce69dbe4303  ./pytorch_model-00002-of-00007.bin

Important: You should now have the correct LLaMA weights and be ready to apply the XORs. If the checksums above do not match yours, there is a problem.

Once you have LLaMA weights in the correct format, you can apply the XOR decoding:

python xor_codec.py oasst-sft-7-llama-30b/ oasst-sft-7-llama-30b-xor/ llama30b_hf/

You should expect to see one warning message during execution:

Exception when processing 'added_tokens.json'

This is normal. If similar messages appear for other files, something has gone wrong.

Now run find . -type f -exec md5sum "{}" + in the output directory (here oasst-sft-6-llama-30b). You should get a file with exactly these checksums:

8ae4537c64a1ef202d1d82eb0d356703  ./pytorch_model-00007-of-00007.bin
d84f99d23369e159e50cb0597b6c9673  ./pytorch_model-00003-of-00007.bin
f7de50a725d678eb65cc3dced727842f  ./pytorch_model-00001-of-00007.bin
27b0dc092f99aa2efaf467b2d8026c3f  ./added_tokens.json
aee09e21813368c49baaece120125ae3  ./generation_config.json
31a2b04b139f4af043ad04478f1497f5  ./pytorch_model-00005-of-00007.bin
a16a2dfacbde77a1659a7c9df7966d0a  ./pytorch_model-00004-of-00007.bin
eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model
baa778a8679d47b085446faf97b72758  ./pytorch_model-00006-of-00007.bin
b2d64f2198ab7b53e3b8d12fbcadeb3c  ./config.json
deb33dd4ffc3d2baddcce275a00b7c1b  ./tokenizer.json
76d47e4f51a8df1d703c6f594981fcab  ./pytorch_model.bin.index.json
ed59bfee4e87b9193fea5897d610ab24  ./tokenizer_config.json
704373f0c0d62be75e5f7d41d39a7e57  ./special_tokens_map.json
e836168cdbbb74db51d04f25ed6408ce  ./pytorch_model-00002-of-00007.bin

If so you have successfully decoded the weights and should be able to use the model with HuggingFace Transformers. If your checksums do not match those above, there is a problem.

Configuration

llama-30b-sft-7:
  dtype: fp16
  log_dir: "llama_log_30b"
  learning_rate: 1e-5
  model_name: /home/ubuntu/Open-Assistant/model/model_training/.saved/llama-30b-super-pretrain/checkpoint-3500
  #model_name: OpenAssistant/llama-30b-super-pretrain
  output_dir: llama_model_30b
  deepspeed_config: configs/zero3_config_sft.json
  weight_decay: 0.0
  residual_dropout: 0.0
  max_length: 2048
  use_flash_attention: true
  warmup_steps: 20
  gradient_checkpointing: true
  gradient_accumulation_steps: 12
  per_device_train_batch_size: 2
  per_device_eval_batch_size: 3
  eval_steps: 101
  save_steps: 485
  num_train_epochs: 4
  save_total_limit: 3
  use_custom_sampler: true
  sort_by_length: false
  #save_strategy: steps
  save_strategy: epoch
  datasets:
    - oasst_export:
        lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
        input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz
        val_split: 0.05
    - vicuna:
        val_split: 0.05
        max_val_set: 800
        fraction: 1.0
    - dolly15k:
        val_split: 0.05
        max_val_set: 300
    - grade_school_math_instructions:
        val_split: 0.05
    - code_alpaca:
        val_split: 0.05
        max_val_set: 250

OASST dataset paper: https://arxiv.org/abs/2304.07327