--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-32B tags: - generated_from_trainer model-index: - name: EVA-Qwen2.5-32B-SFFT-v0.1 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml base_model: Qwen/Qwen2.5-32B load_in_8bit: false load_in_4bit: false strict: false plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true # 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/Sonnet3-5-charcard-names-filtered-sharegpt.jsonl type: sharegpt - path: datasets/deduped_SynthRP-Gens_processed_09-25-2024-ShareGPT_converted_cleaned.jsonl type: sharegpt - path: datasets/Gryphe-4o-WP-filtered-sharegpt.jsonl type: sharegpt - path: datasets/deduped_not_samantha_norefusals.jsonl type: sharegpt - path: datasets/SystemChat_subset_filtered_sharegpt.jsonl type: sharegpt chat_template: chatml shuffle_merged_datasets: true val_set_size: 0.001 output_dir: ./EVA-Qwen2.5-32B-SFFT-v0.1 sequence_len: 9216 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.63.mlp.down_proj - model.layers.49.mlp.down_proj - model.layers.48.mlp.down_proj - model.layers.45.mlp.down_proj - model.layers.44.mlp.down_proj - model.layers.47.mlp.down_proj - model.layers.46.mlp.down_proj - model.layers.43.mlp.down_proj - model.layers.8.mlp.down_proj - model.layers.11.mlp.down_proj - model.layers.19.mlp.down_proj - model.layers.35.mlp.down_proj - model.layers.20.mlp.down_proj - model.layers.52.mlp.down_proj - model.layers.39.mlp.down_proj - model.layers.62.mlp.down_proj - model.layers.50.mlp.down_proj - model.layers.29.mlp.down_proj - model.layers.16.mlp.down_proj - model.layers.28.mlp.down_proj - model.layers.53.mlp.down_proj - model.layers.30.mlp.down_proj - model.layers.31.mlp.down_proj - model.layers.32.mlp.down_proj - model.layers.7.mlp.down_proj - model.layers.36.mlp.down_proj - model.layers.12.mlp.down_proj - model.layers.18.mlp.down_proj - model.layers.37.mlp.down_proj - model.layers.38.mlp.down_proj - model.layers.14.mlp.down_proj - model.layers.13.mlp.down_proj # mlp.gate_proj layers - model.layers.43.mlp.gate_proj - model.layers.61.mlp.gate_proj - model.layers.60.mlp.gate_proj - model.layers.44.mlp.gate_proj - model.layers.62.mlp.gate_proj - model.layers.28.mlp.gate_proj - model.layers.29.mlp.gate_proj - model.layers.45.mlp.gate_proj - model.layers.37.mlp.gate_proj - model.layers.35.mlp.gate_proj - model.layers.59.mlp.gate_proj - model.layers.36.mlp.gate_proj - model.layers.30.mlp.gate_proj - model.layers.48.mlp.gate_proj - model.layers.38.mlp.gate_proj - model.layers.27.mlp.gate_proj - model.layers.31.mlp.gate_proj - model.layers.34.mlp.gate_proj - model.layers.58.mlp.gate_proj - model.layers.33.mlp.gate_proj - model.layers.39.mlp.gate_proj - model.layers.26.mlp.gate_proj - model.layers.32.mlp.gate_proj - model.layers.46.mlp.gate_proj - model.layers.42.mlp.gate_proj - model.layers.49.mlp.gate_proj - model.layers.57.mlp.gate_proj - model.layers.50.mlp.gate_proj - model.layers.47.mlp.gate_proj - model.layers.56.mlp.gate_proj - model.layers.63.mlp.gate_proj - model.layers.55.mlp.gate_proj # mlp.up_proj layers - model.layers.61.mlp.up_proj - model.layers.60.mlp.up_proj - model.layers.32.mlp.up_proj - model.layers.59.mlp.up_proj - model.layers.58.mlp.up_proj - model.layers.57.mlp.up_proj - model.layers.44.mlp.up_proj - model.layers.28.mlp.up_proj - model.layers.35.mlp.up_proj - model.layers.36.mlp.up_proj - model.layers.29.mlp.up_proj - model.layers.31.mlp.up_proj - model.layers.34.mlp.up_proj - model.layers.55.mlp.up_proj - model.layers.49.mlp.up_proj - model.layers.30.mlp.up_proj - model.layers.53.mlp.up_proj - model.layers.43.mlp.up_proj - model.layers.56.mlp.up_proj - model.layers.33.mlp.up_proj - model.layers.54.mlp.up_proj - model.layers.62.mlp.up_proj - model.layers.27.mlp.up_proj - model.layers.51.mlp.up_proj - model.layers.52.mlp.up_proj - model.layers.37.mlp.up_proj - model.layers.45.mlp.up_proj - model.layers.26.mlp.up_proj - model.layers.42.mlp.up_proj - model.layers.50.mlp.up_proj - model.layers.48.mlp.up_proj - model.layers.39.mlp.up_proj # self_attn.k_proj layers - model.layers.63.self_attn.k_proj - model.layers.55.self_attn.k_proj - model.layers.60.self_attn.k_proj - model.layers.7.self_attn.k_proj - model.layers.12.self_attn.k_proj - model.layers.13.self_attn.k_proj - model.layers.57.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.14.self_attn.k_proj - model.layers.51.self_attn.k_proj - model.layers.53.self_attn.k_proj - model.layers.54.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.61.self_attn.k_proj - model.layers.18.self_attn.k_proj - model.layers.30.self_attn.k_proj - model.layers.9.self_attn.k_proj - model.layers.24.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.10.self_attn.k_proj - model.layers.58.self_attn.k_proj - model.layers.56.self_attn.k_proj - model.layers.15.self_attn.k_proj - model.layers.32.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.8.self_attn.k_proj - model.layers.59.self_attn.k_proj - model.layers.11.self_attn.k_proj - model.layers.48.self_attn.k_proj - model.layers.16.self_attn.k_proj - model.layers.50.self_attn.k_proj # self_attn.o_proj layers - model.layers.15.self_attn.o_proj - model.layers.23.self_attn.o_proj - model.layers.31.self_attn.o_proj - model.layers.30.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.24.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.28.self_attn.o_proj - model.layers.34.self_attn.o_proj - model.layers.33.self_attn.o_proj - model.layers.25.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.29.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.26.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.27.self_attn.o_proj - model.layers.35.self_attn.o_proj - model.layers.20.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.36.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.37.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.11.self_attn.o_proj - model.layers.54.self_attn.o_proj - model.layers.5.self_attn.o_proj - model.layers.38.self_attn.o_proj - model.layers.6.self_attn.o_proj - model.layers.8.self_attn.o_proj - model.layers.9.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.45.self_attn.q_proj - model.layers.54.self_attn.q_proj - model.layers.35.self_attn.q_proj - model.layers.48.self_attn.q_proj - model.layers.61.self_attn.q_proj - model.layers.52.self_attn.q_proj - model.layers.50.self_attn.q_proj - model.layers.60.self_attn.q_proj - model.layers.56.self_attn.q_proj - model.layers.58.self_attn.q_proj - model.layers.42.self_attn.q_proj - model.layers.59.self_attn.q_proj - model.layers.44.self_attn.q_proj - model.layers.55.self_attn.q_proj - model.layers.57.self_attn.q_proj - model.layers.41.self_attn.q_proj - model.layers.36.self_attn.q_proj - model.layers.39.self_attn.q_proj - model.layers.4.self_attn.q_proj - model.layers.43.self_attn.q_proj - model.layers.34.self_attn.q_proj - model.layers.46.self_attn.q_proj - model.layers.49.self_attn.q_proj - model.layers.40.self_attn.q_proj - model.layers.25.self_attn.q_proj - model.layers.51.self_attn.q_proj - model.layers.17.self_attn.q_proj - model.layers.37.self_attn.q_proj - model.layers.53.self_attn.q_proj # self_attn.v_proj layers - model.layers.55.self_attn.v_proj - model.layers.31.self_attn.v_proj - model.layers.47.self_attn.v_proj - model.layers.45.self_attn.v_proj - model.layers.49.self_attn.v_proj - model.layers.48.self_attn.v_proj - model.layers.15.self_attn.v_proj - model.layers.30.self_attn.v_proj - model.layers.7.self_attn.v_proj - model.layers.44.self_attn.v_proj - model.layers.29.self_attn.v_proj - model.layers.51.self_attn.v_proj - model.layers.50.self_attn.v_proj - model.layers.14.self_attn.v_proj - model.layers.54.self_attn.v_proj - model.layers.32.self_attn.v_proj - model.layers.43.self_attn.v_proj - model.layers.10.self_attn.v_proj - model.layers.46.self_attn.v_proj - model.layers.38.self_attn.v_proj - model.layers.57.self_attn.v_proj - model.layers.22.self_attn.v_proj - model.layers.39.self_attn.v_proj - model.layers.6.self_attn.v_proj - model.layers.23.self_attn.v_proj - model.layers.58.self_attn.v_proj - model.layers.53.self_attn.v_proj - model.layers.40.self_attn.v_proj - model.layers.24.self_attn.v_proj - model.layers.9.self_attn.v_proj - model.layers.25.self_attn.v_proj - model.layers.5.self_attn.v_proj wandb_project: EVA-Qwen2.5-32B-SFFT-v0.1 wandb_entity: wandb_watch: wandb_name: Unit-01 wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 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_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-Qwen2.5-32B-SFFT-v0.1 This model is a fine-tuned version of [Qwen/Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0347 ## 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: 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: 20 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.4186 | 0.0068 | 1 | 1.4219 | | 1.1535 | 0.2532 | 37 | 1.0748 | | 1.0027 | 0.5064 | 74 | 1.0227 | | 0.9742 | 0.7596 | 111 | 1.0011 | | 0.7284 | 1.0120 | 148 | 1.0106 | | 0.6779 | 1.2666 | 185 | 1.0126 | | 0.6424 | 1.5211 | 222 | 1.0009 | | 0.6818 | 1.7756 | 259 | 0.9927 | | 0.4779 | 2.0233 | 296 | 1.0260 | | 0.4423 | 2.2782 | 333 | 1.0372 | | 0.4613 | 2.5332 | 370 | 1.0365 | | 0.4332 | 2.7881 | 407 | 1.0347 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1