--- license: llama3 base_model: meta-llama/Meta-Llama-3-70B tags: - generated_from_trainer model-index: - name: workspace/axolotl/llama-70b results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: meta-llama/Meta-Llama-3-70B model_type: LlamaForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false # load_in_4bit: true strict: false datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml chat_template: chatml # adapter: qlora # lora_r: 128 # lora_alpha: 16 # lora_modules_to_save: [embed_tokens, lm_head] # lora_dropout: 0.05 # lora_target_linear: true unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ # mlp.down_proj layers - model.layers.40.mlp.down_proj - model.layers.44.mlp.down_proj - model.layers.45.mlp.down_proj - model.layers.46.mlp.down_proj - model.layers.43.mlp.down_proj - model.layers.52.mlp.down_proj - model.layers.47.mlp.down_proj - model.layers.48.mlp.down_proj - model.layers.39.mlp.down_proj - model.layers.49.mlp.down_proj - model.layers.38.mlp.down_proj - model.layers.53.mlp.down_proj - model.layers.41.mlp.down_proj - model.layers.35.mlp.down_proj - model.layers.51.mlp.down_proj - model.layers.42.mlp.down_proj - model.layers.37.mlp.down_proj - model.layers.50.mlp.down_proj - model.layers.60.mlp.down_proj - model.layers.76.mlp.down_proj - model.layers.54.mlp.down_proj - model.layers.36.mlp.down_proj - model.layers.57.mlp.down_proj - model.layers.56.mlp.down_proj - model.layers.55.mlp.down_proj - model.layers.77.mlp.down_proj - model.layers.59.mlp.down_proj - model.layers.61.mlp.down_proj - model.layers.58.mlp.down_proj - model.layers.65.mlp.down_proj - model.layers.75.mlp.down_proj - model.layers.64.mlp.down_proj - model.layers.62.mlp.down_proj - model.layers.68.mlp.down_proj - model.layers.19.mlp.down_proj - model.layers.66.mlp.down_proj # mlp.gate_proj layers - model.layers.70.mlp.gate_proj - model.layers.71.mlp.gate_proj - model.layers.67.mlp.gate_proj - model.layers.58.mlp.gate_proj - model.layers.55.mlp.gate_proj - model.layers.57.mlp.gate_proj - model.layers.56.mlp.gate_proj - model.layers.66.mlp.gate_proj - model.layers.72.mlp.gate_proj - model.layers.69.mlp.gate_proj - model.layers.52.mlp.gate_proj - model.layers.54.mlp.gate_proj - model.layers.62.mlp.gate_proj - model.layers.60.mlp.gate_proj - model.layers.74.mlp.gate_proj - model.layers.59.mlp.gate_proj - model.layers.68.mlp.gate_proj - model.layers.61.mlp.gate_proj - model.layers.73.mlp.gate_proj - model.layers.53.mlp.gate_proj - model.layers.51.mlp.gate_proj - model.layers.63.mlp.gate_proj - model.layers.48.mlp.gate_proj - model.layers.49.mlp.gate_proj - model.layers.64.mlp.gate_proj - model.layers.50.mlp.gate_proj - model.layers.65.mlp.gate_proj - model.layers.47.mlp.gate_proj - model.layers.44.mlp.gate_proj - model.layers.45.mlp.gate_proj - model.layers.75.mlp.gate_proj - model.layers.46.mlp.gate_proj - model.layers.43.mlp.gate_proj - model.layers.77.mlp.gate_proj - model.layers.41.mlp.gate_proj - model.layers.42.mlp.gate_proj # mlp.up_proj layers - model.layers.70.mlp.up_proj - model.layers.67.mlp.up_proj - model.layers.66.mlp.up_proj - model.layers.69.mlp.up_proj - model.layers.62.mlp.up_proj - model.layers.65.mlp.up_proj - model.layers.63.mlp.up_proj - model.layers.68.mlp.up_proj - model.layers.71.mlp.up_proj - model.layers.64.mlp.up_proj - model.layers.61.mlp.up_proj - model.layers.58.mlp.up_proj - model.layers.59.mlp.up_proj - model.layers.57.mlp.up_proj - model.layers.55.mlp.up_proj - model.layers.72.mlp.up_proj - model.layers.54.mlp.up_proj - model.layers.60.mlp.up_proj - model.layers.56.mlp.up_proj - model.layers.73.mlp.up_proj - model.layers.50.mlp.up_proj - model.layers.51.mlp.up_proj - model.layers.53.mlp.up_proj - model.layers.74.mlp.up_proj - model.layers.52.mlp.up_proj - model.layers.49.mlp.up_proj - model.layers.30.mlp.up_proj - model.layers.34.mlp.up_proj - model.layers.47.mlp.up_proj - model.layers.46.mlp.up_proj - model.layers.48.mlp.up_proj - model.layers.38.mlp.up_proj - model.layers.45.mlp.up_proj - model.layers.43.mlp.up_proj - model.layers.29.mlp.up_proj - model.layers.42.mlp.up_proj # self_attn.k_proj layers - model.layers.72.self_attn.k_proj - model.layers.75.self_attn.k_proj - model.layers.71.self_attn.k_proj - model.layers.74.self_attn.k_proj - model.layers.44.self_attn.k_proj - model.layers.31.self_attn.k_proj - model.layers.33.self_attn.k_proj - model.layers.34.self_attn.k_proj - model.layers.76.self_attn.k_proj - model.layers.78.self_attn.k_proj - model.layers.77.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.18.self_attn.k_proj - model.layers.60.self_attn.k_proj - model.layers.17.self_attn.k_proj - model.layers.56.self_attn.k_proj - model.layers.2.self_attn.k_proj - model.layers.21.self_attn.k_proj - model.layers.19.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.52.self_attn.k_proj - model.layers.35.self_attn.k_proj - model.layers.73.self_attn.k_proj - model.layers.15.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.20.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.36.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.37.self_attn.k_proj - model.layers.30.self_attn.k_proj - model.layers.16.self_attn.k_proj - model.layers.32.self_attn.k_proj - model.layers.41.self_attn.k_proj - model.layers.26.self_attn.k_proj # self_attn.o_proj layers - model.layers.50.self_attn.o_proj - model.layers.61.self_attn.o_proj - model.layers.46.self_attn.o_proj - model.layers.53.self_attn.o_proj - model.layers.54.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.42.self_attn.o_proj - model.layers.49.self_attn.o_proj - model.layers.41.self_attn.o_proj - model.layers.68.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.45.self_attn.o_proj - model.layers.11.self_attn.o_proj - model.layers.67.self_attn.o_proj - model.layers.48.self_attn.o_proj - model.layers.51.self_attn.o_proj - model.layers.64.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.47.self_attn.o_proj - model.layers.0.self_attn.o_proj - model.layers.20.self_attn.o_proj - model.layers.63.self_attn.o_proj - model.layers.15.self_attn.o_proj - model.layers.5.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.52.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.10.self_attn.o_proj - model.layers.62.self_attn.o_proj - model.layers.56.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.6.self_attn.o_proj - model.layers.7.self_attn.o_proj # self_attn.q_proj layers - model.layers.2.self_attn.q_proj - model.layers.4.self_attn.q_proj - model.layers.46.self_attn.q_proj - model.layers.5.self_attn.q_proj - model.layers.7.self_attn.q_proj - model.layers.6.self_attn.q_proj - model.layers.9.self_attn.q_proj - model.layers.10.self_attn.q_proj - model.layers.1.self_attn.q_proj - model.layers.18.self_attn.q_proj - model.layers.62.self_attn.q_proj - model.layers.8.self_attn.q_proj - model.layers.15.self_attn.q_proj - model.layers.14.self_attn.q_proj - model.layers.16.self_attn.q_proj - model.layers.31.self_attn.q_proj - model.layers.19.self_attn.q_proj - model.layers.17.self_attn.q_proj - model.layers.33.self_attn.q_proj - model.layers.35.self_attn.q_proj - model.layers.12.self_attn.q_proj - model.layers.21.self_attn.q_proj - model.layers.27.self_attn.q_proj - model.layers.34.self_attn.q_proj - model.layers.13.self_attn.q_proj - model.layers.56.self_attn.q_proj - model.layers.11.self_attn.q_proj - model.layers.52.self_attn.q_proj - model.layers.54.self_attn.q_proj - model.layers.28.self_attn.q_proj - model.layers.30.self_attn.q_proj - model.layers.20.self_attn.q_proj - model.layers.29.self_attn.q_proj - model.layers.37.self_attn.q_proj - model.layers.23.self_attn.q_proj - model.layers.75.self_attn.q_proj # self_attn.v_proj layers - model.layers.11.self_attn.v_proj - model.layers.17.self_attn.v_proj - model.layers.37.self_attn.v_proj - model.layers.40.self_attn.v_proj - model.layers.41.self_attn.v_proj - model.layers.42.self_attn.v_proj - model.layers.43.self_attn.v_proj - model.layers.44.self_attn.v_proj - model.layers.45.self_attn.v_proj - model.layers.46.self_attn.v_proj - model.layers.48.self_attn.v_proj - model.layers.49.self_attn.v_proj - model.layers.50.self_attn.v_proj - model.layers.51.self_attn.v_proj - model.layers.53.self_attn.v_proj - model.layers.54.self_attn.v_proj - model.layers.55.self_attn.v_proj - model.layers.57.self_attn.v_proj - model.layers.58.self_attn.v_proj - model.layers.59.self_attn.v_proj - model.layers.60.self_attn.v_proj - model.layers.61.self_attn.v_proj - model.layers.62.self_attn.v_proj - model.layers.63.self_attn.v_proj - model.layers.64.self_attn.v_proj - model.layers.65.self_attn.v_proj - model.layers.66.self_attn.v_proj - model.layers.67.self_attn.v_proj - model.layers.69.self_attn.v_proj - model.layers.75.self_attn.v_proj - model.layers.18.self_attn.v_proj - model.layers.78.self_attn.v_proj - model.layers.68.self_attn.v_proj - model.layers.47.self_attn.v_proj - model.layers.38.self_attn.v_proj - model.layers.71.self_attn.v_proj # model.norm layers dataset_prepared_path: last_run_prepared val_set_size: 0.01 output_dir: /workspace/axolotl/llama-70b sequence_len: 4096 sample_packing: true pad_to_sequence_len: true wandb_project: llama-3 wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 5 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: deepspeed_configs/zero3_bf16.json weight_decay: 0.00 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" pad_token: "<|end_of_text|>" tokens: - "<|im_start|>" - "<|im_end|>" ```

# workspace/axolotl/llama-70b This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4808 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.7659 | 0.0004 | 1 | 0.7454 | | 0.5006 | 0.2501 | 587 | 0.4817 | | 0.4807 | 0.5002 | 1174 | 0.4698 | | 0.4758 | 0.7503 | 1761 | 0.4627 | | 0.4969 | 1.0004 | 2348 | 0.4558 | | 0.3604 | 1.2346 | 2935 | 0.4635 | | 0.3817 | 1.4847 | 3522 | 0.4572 | | 0.377 | 1.7348 | 4109 | 0.4533 | | 0.3695 | 1.9849 | 4696 | 0.4487 | | 0.2676 | 2.2187 | 5283 | 0.4825 | | 0.255 | 2.4688 | 5870 | 0.4814 | | 0.2851 | 2.7189 | 6457 | 0.4808 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1