# EVA Qwen2.5-72B v0.0

A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-72B on mixture of synthetic and natural data.
It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model.

Model is available for inference on Featherless.AI

Note: using quantized KV cache with Qwen2.5 is not recommended and can lead to degraded output quality. On the other hand, Qwen's KV cache is already light enough, so using f16 for it shouldn't be problematic.

Note #2: due to some unexpected effects of data normalization, some artifacting in form of randomly appearring sequence of â€” can appear in outputs sometimes, if penalties are too high. To avoid it, ban token number 158. Thanks to Cahvay/ALK for discovering this fix!

Prompt format is ChatML.

Recommended sampler values:

Temperature: 1
Typical-P: 0.9
Min-P: 0.05
Top-A: 0.2
Repetition Penalty: 1.03

Recommended SillyTavern presets (via CalamitousFelicitousness):

- [Context](https://huggingface.co/EVA-UNIT-01/EVA-Yi-1.5-9B-32K-V1/blob/main/%5BChatML%5D%20Roleplay-v1.9%20Context.json) - [Instruct and System Prompt](https://huggingface.co/EVA-UNIT-01/EVA-Yi-1.5-9B-32K-V1/blob/main/%5BChatML%5D%20Roleplay-v1.9%20Instruct.json)

Training data:

Celeste 70B 0.1 data mixture minus Opus Instruct subset. See that model's card for details.
Kalomaze's Opus_Instruct_25k dataset, filtered for refusals.
A subset (1k rows) of ChatGPT-4o-WritingPrompts by Gryphe
A subset (2k rows) of Sonnet3.5-Charcards-Roleplay by Gryphe
Synthstruct and SynthRP datasets by Epiculous

Training time and hardware:

12 hours on 8xMI300X

Model was trained by Kearm and Auri.

Special thanks:

to Gryphe, Lemmy, Kalomaze, Nopm and Epiculous for the data
to CalamitiousFelicitousness for providing free inference for public beta testing
and to Allura-org for support and feedback on EVA models.

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 ```