OpenAssistant-SFT-7-LLaMA-30B-ggml

localmodels/OpenAssistant-SFT-7-LLaMA-30B-ggml

OpenAssistant-SFT-7-LLaMA-30B-ggml is a machine learning model from localmodels. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

From: https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor

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Updated Jul 14, 2023

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382 GB

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.bin235 GB · 100%

At a glance

Access: Public
Created: Jul 14, 2023
Updated: Jul 14, 2023
SHA: 5f8b044a

Model card

From the Hugging Face model README

OpenAssistant SFT 7 LLaMA 30B ggml

From: https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor

Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`

Quantized using an older version of llama.cpp and compatible with llama.cpp from May 19, commit 2d5db48.

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`

Quantization methods compatible with latest llama.cpp from June 6, commit 2d43387.

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

OpenAssistant LLaMA 30B SFT 7

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

Created: Jul 14, 2023
Updated: Jul 14, 2023