--- duplicated_from: localmodels/LLM --- # 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