--- base_model: cognitivecomputations/mixtral-1x22b-base tags: - generated_from_trainer model-index: - name: 1x22b-out results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: cognitivecomputations/mixtral-1x22b-base model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer # trust_remote_code: true # load_in_8bit: true # 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 dataset_prepared_path: yi34b-prepared val_set_size: 0.01 output_dir: ./1x22b-out # adapter: qlora # lora_r: 16 # 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$ # # input_layernorm layers # - model.layers.0.input_layernorm # - model.layers.1.input_layernorm # - model.layers.2.input_layernorm # - model.layers.3.input_layernorm # - model.layers.4.input_layernorm # - model.layers.5.input_layernorm # - model.layers.6.input_layernorm # - model.layers.7.input_layernorm # - model.layers.8.input_layernorm # - model.layers.9.input_layernorm # - model.layers.10.input_layernorm # - model.layers.11.input_layernorm # - model.layers.12.input_layernorm # - model.layers.13.input_layernorm # - model.layers.14.input_layernorm # - model.layers.15.input_layernorm # - model.layers.16.input_layernorm # - model.layers.17.input_layernorm # - model.layers.18.input_layernorm # - model.layers.19.input_layernorm # - model.layers.20.input_layernorm # - model.layers.21.input_layernorm # - model.layers.22.input_layernorm # - model.layers.23.input_layernorm # # lm_head layers # # mlp.down_proj layers # - model.layers.17.mlp.down_proj # - model.layers.18.mlp.down_proj # - model.layers.19.mlp.down_proj # - model.layers.20.mlp.down_proj # - model.layers.21.mlp.down_proj # - model.layers.22.mlp.down_proj # - model.layers.23.mlp.down_proj # - model.layers.24.mlp.down_proj # - model.layers.25.mlp.down_proj # - model.layers.26.mlp.down_proj # - model.layers.27.mlp.down_proj # - model.layers.28.mlp.down_proj # - model.layers.29.mlp.down_proj # - model.layers.30.mlp.down_proj # - model.layers.31.mlp.down_proj # - model.layers.32.mlp.down_proj # - model.layers.33.mlp.down_proj # - model.layers.34.mlp.down_proj # - model.layers.35.mlp.down_proj # - model.layers.36.mlp.down_proj # - model.layers.37.mlp.down_proj # - model.layers.38.mlp.down_proj # - model.layers.39.mlp.down_proj # - model.layers.40.mlp.down_proj # # mlp.gate_proj layers # - model.layers.51.mlp.gate_proj # - model.layers.50.mlp.gate_proj # - model.layers.53.mlp.gate_proj # - model.layers.52.mlp.gate_proj # - model.layers.49.mlp.gate_proj # - model.layers.45.mlp.gate_proj # - model.layers.46.mlp.gate_proj # - model.layers.47.mlp.gate_proj # - model.layers.57.mlp.gate_proj # - model.layers.48.mlp.gate_proj # - model.layers.56.mlp.gate_proj # - model.layers.41.mlp.gate_proj # - model.layers.54.mlp.gate_proj # - model.layers.43.mlp.gate_proj # - model.layers.44.mlp.gate_proj # - model.layers.60.mlp.gate_proj # - model.layers.55.mlp.gate_proj # - model.layers.40.mlp.gate_proj # - model.layers.42.mlp.gate_proj # - model.layers.58.mlp.gate_proj # - model.layers.36.mlp.gate_proj # - model.layers.37.mlp.gate_proj # - model.layers.38.mlp.gate_proj # - model.layers.39.mlp.gate_proj # # mlp.up_proj layers # - model.layers.50.mlp.up_proj # - model.layers.51.mlp.up_proj # - model.layers.41.mlp.up_proj # - model.layers.49.mlp.up_proj # - model.layers.43.mlp.up_proj # - model.layers.44.mlp.up_proj # - model.layers.40.mlp.up_proj # - model.layers.45.mlp.up_proj # - model.layers.47.mlp.up_proj # - model.layers.48.mlp.up_proj # - model.layers.46.mlp.up_proj # - model.layers.42.mlp.up_proj # - model.layers.39.mlp.up_proj # - model.layers.36.mlp.up_proj # - model.layers.37.mlp.up_proj # - model.layers.38.mlp.up_proj # - model.layers.56.mlp.up_proj # - model.layers.57.mlp.up_proj # - model.layers.53.mlp.up_proj # - model.layers.31.mlp.up_proj # - model.layers.32.mlp.up_proj # - model.layers.34.mlp.up_proj # - model.layers.35.mlp.up_proj # - model.layers.33.mlp.up_proj # # model.embed_tokens layers # # model.norm layers # # post_attention_layernorm layers # - model.layers.0.post_attention_layernorm # - model.layers.1.post_attention_layernorm # - model.layers.2.post_attention_layernorm # - model.layers.3.post_attention_layernorm # - model.layers.4.post_attention_layernorm # - model.layers.5.post_attention_layernorm # - model.layers.6.post_attention_layernorm # - model.layers.7.post_attention_layernorm # - model.layers.8.post_attention_layernorm # - model.layers.9.post_attention_layernorm # - model.layers.10.post_attention_layernorm # - model.layers.11.post_attention_layernorm # - model.layers.12.post_attention_layernorm # - model.layers.13.post_attention_layernorm # - model.layers.14.post_attention_layernorm # - model.layers.15.post_attention_layernorm # - model.layers.16.post_attention_layernorm # - model.layers.17.post_attention_layernorm # - model.layers.18.post_attention_layernorm # - model.layers.19.post_attention_layernorm # - model.layers.20.post_attention_layernorm # - model.layers.21.post_attention_layernorm # - model.layers.22.post_attention_layernorm # - model.layers.23.post_attention_layernorm # # self_attn.k_proj layers # - model.layers.42.self_attn.k_proj # - model.layers.41.self_attn.k_proj # - model.layers.39.self_attn.k_proj # - model.layers.35.self_attn.k_proj # - model.layers.28.self_attn.k_proj # - model.layers.79.self_attn.k_proj # - model.layers.43.self_attn.k_proj # - model.layers.32.self_attn.k_proj # - model.layers.73.self_attn.k_proj # - model.layers.31.self_attn.k_proj # - model.layers.29.self_attn.k_proj # - model.layers.76.self_attn.k_proj # - model.layers.30.self_attn.k_proj # - model.layers.40.self_attn.k_proj # - model.layers.33.self_attn.k_proj # - model.layers.78.self_attn.k_proj # - model.layers.34.self_attn.k_proj # - model.layers.37.self_attn.k_proj # - model.layers.45.self_attn.k_proj # - model.layers.44.self_attn.k_proj # - model.layers.71.self_attn.k_proj # - model.layers.26.self_attn.k_proj # - model.layers.74.self_attn.k_proj # - model.layers.27.self_attn.k_proj # # self_attn.o_proj layers # - model.layers.35.self_attn.o_proj # - model.layers.34.self_attn.o_proj # - model.layers.37.self_attn.o_proj # - model.layers.33.self_attn.o_proj # - model.layers.31.self_attn.o_proj # - model.layers.27.self_attn.o_proj # - model.layers.38.self_attn.o_proj # - model.layers.24.self_attn.o_proj # - model.layers.39.self_attn.o_proj # - model.layers.43.self_attn.o_proj # - model.layers.29.self_attn.o_proj # - model.layers.0.self_attn.o_proj # - model.layers.50.self_attn.o_proj # - model.layers.32.self_attn.o_proj # - model.layers.45.self_attn.o_proj # - model.layers.30.self_attn.o_proj # - model.layers.60.self_attn.o_proj # - model.layers.23.self_attn.o_proj # - model.layers.18.self_attn.o_proj # - model.layers.67.self_attn.o_proj # - model.layers.57.self_attn.o_proj # - model.layers.20.self_attn.o_proj # - model.layers.76.self_attn.o_proj # - model.layers.28.self_attn.o_proj # # self_attn.q_proj layers # - model.layers.1.self_attn.q_proj # - model.layers.6.self_attn.q_proj # - model.layers.0.self_attn.q_proj # - model.layers.5.self_attn.q_proj # - model.layers.2.self_attn.q_proj # - model.layers.7.self_attn.q_proj # - model.layers.3.self_attn.q_proj # - model.layers.4.self_attn.q_proj # - model.layers.8.self_attn.q_proj # - model.layers.9.self_attn.q_proj # - model.layers.61.self_attn.q_proj # - model.layers.10.self_attn.q_proj # - model.layers.62.self_attn.q_proj # - model.layers.36.self_attn.q_proj # - model.layers.15.self_attn.q_proj # - model.layers.11.self_attn.q_proj # - model.layers.17.self_attn.q_proj # - model.layers.60.self_attn.q_proj # - model.layers.63.self_attn.q_proj # - model.layers.64.self_attn.q_proj # - model.layers.29.self_attn.q_proj # - model.layers.30.self_attn.q_proj # - model.layers.55.self_attn.q_proj # - model.layers.34.self_attn.q_proj # # self_attn.v_proj layers # - model.layers.12.self_attn.v_proj # - model.layers.16.self_attn.v_proj # - model.layers.18.self_attn.v_proj # - model.layers.19.self_attn.v_proj # - model.layers.20.self_attn.v_proj # - model.layers.21.self_attn.v_proj # - model.layers.22.self_attn.v_proj # - model.layers.23.self_attn.v_proj # - model.layers.24.self_attn.v_proj # - model.layers.25.self_attn.v_proj # - model.layers.26.self_attn.v_proj # - model.layers.27.self_attn.v_proj # - model.layers.28.self_attn.v_proj # - model.layers.29.self_attn.v_proj # - model.layers.30.self_attn.v_proj # - model.layers.31.self_attn.v_proj # - model.layers.32.self_attn.v_proj # - model.layers.33.self_attn.v_proj # - model.layers.34.self_attn.v_proj # - model.layers.35.self_attn.v_proj # - model.layers.36.self_attn.v_proj # - model.layers.37.self_attn.v_proj # - model.layers.38.self_attn.v_proj # - model.layers.39.self_attn.v_proj sequence_len: 16384 sample_packing: true pad_to_sequence_len: true # adapter: lora # lora_model_dir: # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: true # lora_fan_in_fan_out: wandb_project: dolphin-mixtral1x22b wandb_entity: wandb_watch: wandb_name: 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: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: /workspace/axolotl2/axolotl/1x22b-out/checkpoint-507 local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 4 save_total_limit: 2 debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.01 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" bos_token: "" # pad_token: "" unk_token: "" tokens: - "<|im_start|>" ```

# 1x22b-out This model is a fine-tuned version of [cognitivecomputations/mixtral-1x22b-base](https://huggingface.co/cognitivecomputations/mixtral-1x22b-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4572 ## 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: 10 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.9818 | 0.0015 | 1 | 0.9854 | | 0.4783 | 0.2499 | 169 | 0.5042 | | 0.464 | 0.4997 | 338 | 0.4755 | | 0.4561 | 0.7496 | 507 | 0.4593 | | 0.3981 | 0.9994 | 676 | 0.4553 | | 0.3725 | 1.2378 | 845 | 0.4525 | | 0.3624 | 1.4877 | 1014 | 0.4457 | | 0.359 | 1.7376 | 1183 | 0.4393 | | 0.375 | 1.9874 | 1352 | 0.4345 | | 0.2899 | 2.2260 | 1521 | 0.4488 | | 0.2848 | 2.4759 | 1690 | 0.4473 | | 0.2935 | 2.7257 | 1859 | 0.4470 | | 0.2065 | 2.9756 | 2028 | 0.4572 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1