--- library_name: transformers license: other base_model: Qwen/Qwen2.5-72B tags: - generated_from_trainer model-index: - name: EVA-Qwen2.5-72B-SFFT-v0.0 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: Qwen/Qwen2.5-72B load_in_8bit: false load_in_4bit: false strict: false plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: false liger_fused_linear_cross_entropy: false # plugins: # - axolotl.integrations.spectrum.SpectrumPlugin # spectrum_top_fraction: 0.5 # # Optional if using a pre-scanned model as your base_model. Useful if using a model mirror # spectrum_model_name: Qwen/Qwen2.5-32B datasets: - path: datasets/deduped_Synthstruct-Gens_processed_sharegpt_converted_cleaned.jsonl type: sharegpt - path: datasets/opus-instruct-22k-no_refusals-filtered.jsonl type: sharegpt - path: datasets/Celeste_Filtered.jsonl type: sharegpt - path: datasets/Gryphe-S3-5-Charcards-names-2k.jsonl type: sharegpt - path: datasets/deduped_SynthRP-Gens_processed_09-25-2024-ShareGPT_converted_cleaned.jsonl type: sharegpt - path: datasets/deduped_Gryphe-4o-WP-1k.jsonl type: sharegpt - path: datasets/deduped_not_samantha_norefusals.jsonl type: sharegpt chat_template: chatml shuffle_merged_datasets: true val_set_size: 0.001 output_dir: ./EVA-Qwen2.5-72B-SFFT-v0.0 sequence_len: 8192 sample_packing: true eval_sample_packing: false pad_to_sequence_len: true # adapter: qlora # lora_model_dir: # lora_r: 64 # lora_alpha: 128 # lora_dropout: 0.05 # lora_target_linear: true # peft_use_dora: true unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ # mlp.down_proj layers - model.layers.62.mlp.down_proj - model.layers.64.mlp.down_proj - model.layers.63.mlp.down_proj - model.layers.66.mlp.down_proj - model.layers.65.mlp.down_proj - model.layers.67.mlp.down_proj - model.layers.68.mlp.down_proj - model.layers.31.mlp.down_proj - model.layers.60.mlp.down_proj - model.layers.69.mlp.down_proj - model.layers.61.mlp.down_proj - model.layers.59.mlp.down_proj - model.layers.30.mlp.down_proj - model.layers.70.mlp.down_proj - model.layers.32.mlp.down_proj - model.layers.34.mlp.down_proj - model.layers.33.mlp.down_proj - model.layers.76.mlp.down_proj - model.layers.72.mlp.down_proj - model.layers.71.mlp.down_proj - model.layers.58.mlp.down_proj - model.layers.75.mlp.down_proj - model.layers.29.mlp.down_proj - model.layers.56.mlp.down_proj - model.layers.26.mlp.down_proj - model.layers.35.mlp.down_proj - model.layers.28.mlp.down_proj - model.layers.57.mlp.down_proj - model.layers.77.mlp.down_proj - model.layers.36.mlp.down_proj - model.layers.27.mlp.down_proj - model.layers.25.mlp.down_proj - model.layers.78.mlp.down_proj - model.layers.37.mlp.down_proj - model.layers.73.mlp.down_proj - model.layers.55.mlp.down_proj - model.layers.54.mlp.down_proj - model.layers.74.mlp.down_proj - model.layers.24.mlp.down_proj - model.layers.53.mlp.down_proj # mlp.gate_proj layers - model.layers.78.mlp.gate_proj - model.layers.77.mlp.gate_proj - model.layers.76.mlp.gate_proj - model.layers.79.mlp.gate_proj - model.layers.75.mlp.gate_proj - model.layers.74.mlp.gate_proj - model.layers.73.mlp.gate_proj - model.layers.72.mlp.gate_proj - model.layers.71.mlp.gate_proj - model.layers.70.mlp.gate_proj - model.layers.69.mlp.gate_proj - model.layers.57.mlp.gate_proj - model.layers.54.mlp.gate_proj - model.layers.55.mlp.gate_proj - model.layers.68.mlp.gate_proj - model.layers.63.mlp.gate_proj - model.layers.53.mlp.gate_proj - model.layers.44.mlp.gate_proj - model.layers.45.mlp.gate_proj - model.layers.49.mlp.gate_proj - model.layers.58.mlp.gate_proj - model.layers.46.mlp.gate_proj - model.layers.56.mlp.gate_proj - model.layers.67.mlp.gate_proj - model.layers.62.mlp.gate_proj - model.layers.50.mlp.gate_proj - model.layers.64.mlp.gate_proj - model.layers.52.mlp.gate_proj - model.layers.40.mlp.gate_proj - model.layers.43.mlp.gate_proj - model.layers.48.mlp.gate_proj - model.layers.66.mlp.gate_proj - model.layers.47.mlp.gate_proj - model.layers.59.mlp.gate_proj - model.layers.65.mlp.gate_proj - model.layers.61.mlp.gate_proj - model.layers.60.mlp.gate_proj - model.layers.42.mlp.gate_proj - model.layers.51.mlp.gate_proj - model.layers.41.mlp.gate_proj # mlp.up_proj layers - model.layers.70.mlp.up_proj - model.layers.69.mlp.up_proj - model.layers.71.mlp.up_proj - model.layers.68.mlp.up_proj - model.layers.72.mlp.up_proj - model.layers.67.mlp.up_proj - model.layers.66.mlp.up_proj - model.layers.73.mlp.up_proj - model.layers.46.mlp.up_proj - model.layers.63.mlp.up_proj - model.layers.75.mlp.up_proj - model.layers.76.mlp.up_proj - model.layers.74.mlp.up_proj - model.layers.45.mlp.up_proj - model.layers.62.mlp.up_proj - model.layers.64.mlp.up_proj - model.layers.65.mlp.up_proj - model.layers.44.mlp.up_proj - model.layers.53.mlp.up_proj - model.layers.47.mlp.up_proj - model.layers.49.mlp.up_proj - model.layers.48.mlp.up_proj - model.layers.57.mlp.up_proj - model.layers.43.mlp.up_proj - model.layers.42.mlp.up_proj - model.layers.56.mlp.up_proj - model.layers.61.mlp.up_proj - model.layers.54.mlp.up_proj - model.layers.40.mlp.up_proj - model.layers.55.mlp.up_proj - model.layers.77.mlp.up_proj - model.layers.60.mlp.up_proj - model.layers.41.mlp.up_proj - model.layers.35.mlp.up_proj - model.layers.37.mlp.up_proj - model.layers.58.mlp.up_proj - model.layers.34.mlp.up_proj - model.layers.38.mlp.up_proj - model.layers.33.mlp.up_proj - model.layers.39.mlp.up_proj # self_attn.k_proj layers - model.layers.36.self_attn.k_proj - model.layers.79.self_attn.k_proj - model.layers.35.self_attn.k_proj - model.layers.34.self_attn.k_proj - model.layers.37.self_attn.k_proj - model.layers.33.self_attn.k_proj - model.layers.38.self_attn.k_proj - model.layers.39.self_attn.k_proj - model.layers.74.self_attn.k_proj - model.layers.77.self_attn.k_proj - model.layers.41.self_attn.k_proj - model.layers.69.self_attn.k_proj - model.layers.32.self_attn.k_proj - model.layers.78.self_attn.k_proj - model.layers.30.self_attn.k_proj - model.layers.70.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.42.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.31.self_attn.k_proj - model.layers.68.self_attn.k_proj - model.layers.66.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.65.self_attn.k_proj - model.layers.44.self_attn.k_proj - model.layers.40.self_attn.k_proj - model.layers.63.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.24.self_attn.k_proj - model.layers.26.self_attn.k_proj - model.layers.67.self_attn.k_proj - model.layers.75.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.57.self_attn.k_proj - model.layers.64.self_attn.k_proj - model.layers.71.self_attn.k_proj - model.layers.61.self_attn.k_proj - model.layers.72.self_attn.k_proj - model.layers.73.self_attn.k_proj # self_attn.o_proj layers - model.layers.69.self_attn.o_proj - model.layers.39.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.42.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.15.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.38.self_attn.o_proj - model.layers.23.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.29.self_attn.o_proj - model.layers.41.self_attn.o_proj - model.layers.44.self_attn.o_proj - model.layers.46.self_attn.o_proj - model.layers.45.self_attn.o_proj - model.layers.43.self_attn.o_proj - model.layers.49.self_attn.o_proj - model.layers.30.self_attn.o_proj - model.layers.26.self_attn.o_proj - model.layers.25.self_attn.o_proj - model.layers.37.self_attn.o_proj - model.layers.47.self_attn.o_proj - model.layers.11.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.28.self_attn.o_proj - model.layers.20.self_attn.o_proj - model.layers.27.self_attn.o_proj - model.layers.53.self_attn.o_proj - model.layers.52.self_attn.o_proj - model.layers.35.self_attn.o_proj - model.layers.71.self_attn.o_proj - model.layers.10.self_attn.o_proj - model.layers.3.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.24.self_attn.o_proj - model.layers.68.self_attn.o_proj - model.layers.48.self_attn.o_proj # self_attn.q_proj layers - model.layers.1.self_attn.q_proj - model.layers.2.self_attn.q_proj - model.layers.3.self_attn.q_proj - model.layers.0.self_attn.q_proj - model.layers.5.self_attn.q_proj - model.layers.4.self_attn.q_proj - model.layers.6.self_attn.q_proj - model.layers.8.self_attn.q_proj - model.layers.7.self_attn.q_proj - model.layers.9.self_attn.q_proj - model.layers.10.self_attn.q_proj - model.layers.68.self_attn.q_proj - model.layers.25.self_attn.q_proj - model.layers.12.self_attn.q_proj - model.layers.54.self_attn.q_proj - model.layers.55.self_attn.q_proj - model.layers.61.self_attn.q_proj - model.layers.18.self_attn.q_proj - model.layers.49.self_attn.q_proj - model.layers.66.self_attn.q_proj - model.layers.72.self_attn.q_proj - model.layers.11.self_attn.q_proj - model.layers.52.self_attn.q_proj - model.layers.64.self_attn.q_proj - model.layers.15.self_attn.q_proj - model.layers.60.self_attn.q_proj - model.layers.50.self_attn.q_proj - model.layers.59.self_attn.q_proj - model.layers.53.self_attn.q_proj - model.layers.48.self_attn.q_proj - model.layers.57.self_attn.q_proj - model.layers.70.self_attn.q_proj - model.layers.17.self_attn.q_proj - model.layers.67.self_attn.q_proj - model.layers.71.self_attn.q_proj - model.layers.62.self_attn.q_proj - model.layers.51.self_attn.q_proj - model.layers.19.self_attn.q_proj - model.layers.58.self_attn.q_proj - model.layers.13.self_attn.q_proj # self_attn.v_proj layers - model.layers.23.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.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.42.self_attn.v_proj - model.layers.48.self_attn.v_proj - model.layers.57.self_attn.v_proj - model.layers.58.self_attn.v_proj - model.layers.61.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.69.self_attn.v_proj - model.layers.70.self_attn.v_proj - model.layers.74.self_attn.v_proj - model.layers.75.self_attn.v_proj - model.layers.72.self_attn.v_proj - model.layers.39.self_attn.v_proj - model.layers.41.self_attn.v_proj - model.layers.40.self_attn.v_proj - model.layers.33.self_attn.v_proj - model.layers.59.self_attn.v_proj - model.layers.16.self_attn.v_proj - model.layers.15.self_attn.v_proj - model.layers.76.self_attn.v_proj - model.layers.24.self_attn.v_proj - model.layers.68.self_attn.v_proj - model.layers.67.self_attn.v_proj - model.layers.55.self_attn.v_proj - model.layers.44.self_attn.v_proj wandb_project: EVA-Qwen2.5-72B-SFFT-v0.0 wandb_entity: wandb_watch: wandb_name: Unit-00 wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 3 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.00005 max_grad_norm: 3 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: "unsloth" # gradient_checkpointing_kwargs: # use_reentrant: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 20 evals_per_epoch: 4 saves_per_epoch: 2 save_total_limit: 1 save_safetensors: true hub_model_id: hub_strategy: debug: deepspeed: deepspeed_configs/zero3_bf16.json weight_decay: 0.1 # fsdp: # - full_shard # - auto_wrap # fsdp_config: # fsdp_limit_all_gathers: true # fsdp_sync_module_states: false # fsdp_offload_params: true # fsdp_cpu_ram_efficient_loading: true # fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP # fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer # fsdp_activation_checkpointing: true # fsdp_state_dict_type: SHARDED_STATE_DICT # Changed from FULL_STATE_DICT # fsdp_sharding_strategy: FULL_SHARD # fsdp_forward_prefetch: false # Added # fsdp_backward_prefetch: "BACKWARD_PRE" # Added # fsdp_backward_prefetch_limit: 1 # Added # fsdp_mixed_precision: BF16 # Added ```

# EVA-Qwen2.5-72B-SFFT-v0.0 This model is a fine-tuned version of [Qwen/Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2818 ## 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: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.3286 | 0.0142 | 1 | 2.9734 | | 1.0713 | 0.2562 | 18 | 3.7951 | | 0.9051 | 0.5125 | 36 | 3.3342 | | 0.8746 | 0.7687 | 54 | 3.2625 | | 0.6216 | 1.0214 | 72 | 3.2244 | | 0.6158 | 1.2786 | 90 | 3.2810 | | 0.57 | 1.5357 | 108 | 3.2375 | | 0.5213 | 1.7929 | 126 | 3.1606 | | 0.3178 | 2.0427 | 144 | 3.2384 | | 0.2809 | 2.2989 | 162 | 3.2971 | | 0.3067 | 2.5552 | 180 | 3.2886 | | 0.3005 | 2.8114 | 198 | 3.2818 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.0+rocm6.1 - Datasets 3.0.1 - Tokenizers 0.20.1