--- license: cc-by-nc-4.0 base_model: mlabonne/NeuralMonarch-7B tags: - generated_from_trainer - axolotl - mistral - instruct - finetune - chatml - gpt4 - synthetic data - distillation model-index: - name: AlphaMonarch-laser results: [] datasets: - mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha language: - en library_name: transformers pipeline_tag: text-generation --- # AlphaMonarch-laser ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/62S_ExHO6NKCM3NhPDrds.jpeg) AlphaMonarch-laser is a new DPO merge using laserQLoRA that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. This model uses [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) as its base model, finetuned on only half of the layers using laserQLoRA. The preference dataset used for DPO is [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha). * [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) * [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha)
## Evaluation data Task Version Metric Value StdErr --------------------------------------------------------------------- agieval_aqua_rat 0 acc 28.35% 2.83% agieval_aqua_rat 0 acc_norm 26.38% 2.77% agieval_logiqa_en 0 acc 38.25% 1.91% agieval_logiqa_en 0 acc_norm 38.10% 1.90% agieval_lsat_ar 0 acc 23.91% 2.82% agieval_lsat_ar 0 acc_norm 23.48% 2.80% agieval_lsat_lr 0 acc 52.75% 2.21% agieval_lsat_lr 0 acc_norm 53.92% 2.21% agieval_lsat_rc 0 acc 66.91% 2.87% agieval_lsat_rc 0 acc_norm 67.29% 2.87% agieval_sat_en 0 acc 78.64% 2.86% agieval_sat_en 0 acc_norm 78.64% 2.86% agieval_sat_en_without_passage 0 acc 45.15% 3.48% agieval_sat_en_without_passage 0 acc_norm 44.17% 3.47% agieval_sat_math 0 acc 33.18% 3.18% agieval_sat_math 0 acc_norm 31.36% 3.14% 🏆 Evaluation | Task | Version | Metric | Value | StdErr | |---------------------------------|---------|--------------|--------|--------| | agieval_aqua_rat | 0 | acc | 28.35% | 2.83% | | agieval_aqua_rat | 0 | acc_norm | 26.38% | 2.77% | | agieval_logiqa_en | 0 | acc | 38.25% | 1.91% | | agieval_logiqa_en | 0 | acc_norm | 38.10% | 1.90% | | agieval_lsat_ar | 0 | acc | 23.91% | 2.82% | | agieval_lsat_ar | 0 | acc_norm | 23.48% | 2.80% | | agieval_lsat_lr | 0 | acc | 52.75% | 2.21% | | agieval_lsat_lr | 0 | acc_norm | 53.92% | 2.21% | | agieval_lsat_rc | 0 | acc | 66.91% | 2.87% | | agieval_lsat_rc | 0 | acc_norm | 67.29% | 2.87% | | agieval_sat_en | 0 | acc | 78.64% | 2.86% | | agieval_sat_en | 0 | acc_norm | 78.64% | 2.86% | | agieval_sat_en_without_passage | 0 | acc | 45.15% | 3.48% | | agieval_sat_en_without_passage | 0 | acc_norm | 44.17% | 3.47% | | agieval_sat_math | 0 | acc | 33.18% | 3.18% | | agieval_sat_math | 0 | acc_norm | 31.36% | 3.14% | Average: 75.9% without mmlu ### TruthfulQA | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |63.03|± | 1.68| | | |mc2 |78.39|± | 1.37| ### BigBench Reasoning Test | Task | Version | Metric | Value | | Stderr| |------------------------------------------------|---------|------------------------|-----------|---|-------| | bigbench_causal_judgement | 0| multiple_choice_grade | 60.00 | _ | 3.56 | | bigbench_date_understanding | 0| multiple_choice_grade | 62.06 | _ | 2.53 | | bigbench_disambiguation_qa | 0| multiple_choice_grade | 54.26 | _ | 3.11 | | bigbench_geometric_shapes | 0| multiple_choice_grade | 23.96 | _ | 2.26 | | ... | | exact_str_match | | | | | bigbench_geometric_shapes | 0| exact_str_match | 0.00 | _ | 0.00 | | bigbench_logical_deduction_five_objects | 0| multiple_choice_grade | 32.80 | _ | 2.10 | | bigbench_logical_deduction_seven_objects | 0| multiple_choice_grade | 23.86 | _ | 1.61 | | bigbench_logical_deduction_three_objects | 0| multiple_choice_grade | 59.33 | _ | 2.84 | | bigbench_movie_recommendation | 0| multiple_choice_grade | 58.00 | _ | 2.21 | | bigbench_navigate | 0| multiple_choice_grade | 56.00 | _ | 1.57 | | bigbench_reasoning_about_colored_objects | 0| multiple_choice_grade | 69.20 | _ | 1.03 | | bigbench_ruin_names | 0| multiple_choice_grade | 55.36 | _ | 2.35 | | bigbench_salient_translation_error_detection | 0| multiple_choice_grade | 41.48 | _ | 1.56 | | bigbench_snarks | 0| multiple_choice_grade | 73.48 | _ | 3.29 | | bigbench_sports_understanding | 0| multiple_choice_grade | 76.06 | _ | 1.36 | | bigbench_temporal_sequences | 0| multiple_choice_grade | 55.50 | _ | 1.57 | | bigbench_tracking_shuffled_objects_five_objects| 0| multiple_choice_grade | 23.28 | _ | 1.20 | | bigbench_tracking_shuffled_objects_seven_objects| 0| multiple_choice_grade | 19.37 | _ | 0.94 | | bigbench_tracking_shuffled_objects_three_objects| 0| multiple_choice_grade | 59.33 | _ | 2.84 | Average: 49.08% ### GPT4ALL | Task | Version | Metric | Value | | Stderr | |------------|---------|--------|-------|---|--------| | arc\_challenge | 0 | acc | 66.29 | _ | 1.38 | | | | acc\_norm | 68.26 | _ | 1.36 | | arc\_easy | 0 | acc | 86.57 | _ | 0.70 | | | | acc\_norm | 80.81 | _ | 0.81 | | boolq | 1 | acc | 87.16 | _ | 0.59 | | hellaswag | 0 | acc | 69.60 | _ | 0.46 | | | | acc\_norm | 87.45 | _ | 0.33 | | openbookqa | 0 | acc | 39.20 | _ | 2.19 | | | | acc\_norm | 49.60 | _ | 2.24 | | piqa | 0 | acc | 83.03 | _ | 0.88 | | | | acc\_norm | 84.87 | _ | 0.84 | | winogrande | 0 | acc | 81.06 | _ | 1.10 | Average: 68.75% ### AGIEVAL Here is the converted table in the required format, including multiplication of all values by 100 and calculating the average for the value column: | Task | Version | Metric | Value | StdErr | | --- | --- | --- | --- | --- | | agieval\_aqua\_rat | 0| acc | 28.35 | 2.83 | | | | acc\_norm | 26.38 | 2.77 | | agieval\_logiqa\_en | 0| acc | 38.25 | 1.91 | | | | acc\_norm | 38.09 | 1.90 | | agieval\_lsat\_ar | 0| acc | 23.91 | 2.82 | | | | acc\_norm | 23.48 | 2.80 | | agieval\_lsat\_lr | 0| acc | 52.75 | 2.21 | | | | acc\_norm | 53.92 | 2.21 | | agieval\_lsat\_rc | 0 | acc | 66.91 | 2.87 | | | | acc\_norm | 67.29 | 2.87 | | agieval\_sat\_en | 0| acc | 78.64 | 2.86 | | | | acc\_norm | 78.64 | 2.86 | | agieval\_sat\_en\_without\_passage | 0| acc | 45.15 | 3.48 | | | | acc\_norm | 44.17 | 3.47 | | agieval\_sat\_math | 0| acc | 33.18 | 3.18 | | | | acc\_norm | 31.36 | 3.14 | Average: 47.44% ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1080 ### 📝 Axolotl Configuration ```yaml base_model: mlabonne/NeuralMonarch-7B model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: true strict: false rl: dpo chat_template: chatml datasets: - path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha split: train type: chatml.intel dataset_prepared_path: val_set_size: 0.01 output_dir: ./out adapter: qlora lora_model_dir: sequence_len: 1800 sample_packing: false pad_to_sequence_len: false lora_r: 16 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - layers.1.self_attn.q_proj - layers.0.self_attn.q_proj - layers.15.self_attn.q_proj - layers.12.self_attn.q_proj - layers.11.self_attn.q_proj - layers.14.self_attn.q_proj - layers.9.self_attn.q_proj - layers.16.self_attn.q_proj - layers.30.self_attn.q_proj - layers.18.self_attn.q_proj - layers.13.self_attn.q_proj - layers.10.self_attn.q_proj - layers.7.self_attn.q_proj - layers.8.self_attn.q_proj - layers.4.self_attn.q_proj - layers.19.self_attn.q_proj - layers.27.self_attn.k_proj - layers.24.self_attn.k_proj - layers.25.self_attn.k_proj - layers.22.self_attn.k_proj - layers.26.self_attn.k_proj - layers.29.self_attn.k_proj - layers.23.self_attn.k_proj - layers.28.self_attn.k_proj - layers.21.self_attn.k_proj - layers.31.self_attn.k_proj - layers.30.self_attn.k_proj - layers.20.self_attn.k_proj - layers.5.self_attn.k_proj - layers.19.self_attn.k_proj - layers.17.self_attn.k_proj - layers.18.self_attn.k_proj - layers.19.self_attn.v_proj - layers.24.self_attn.v_proj - layers.18.self_attn.v_proj - layers.5.self_attn.v_proj - layers.3.self_attn.v_proj - layers.16.self_attn.v_proj - layers.23.self_attn.v_proj - layers.27.self_attn.v_proj - layers.25.self_attn.v_proj - layers.26.self_attn.v_proj - layers.20.self_attn.v_proj - layers.6.self_attn.v_proj - layers.15.self_attn.v_proj - layers.17.self_attn.v_proj - layers.29.self_attn.v_proj - layers.22.self_attn.v_proj - layers.12.self_attn.o_proj - layers.9.self_attn.o_proj - layers.14.self_attn.o_proj - layers.0.self_attn.o_proj - layers.6.self_attn.o_proj - layers.8.self_attn.o_proj - layers.10.self_attn.o_proj - layers.11.self_attn.o_proj - layers.13.self_attn.o_proj - layers.24.self_attn.o_proj - layers.7.self_attn.o_proj - layers.15.self_attn.o_proj - layers.5.self_attn.o_proj - layers.17.self_attn.o_proj - layers.25.self_attn.o_proj - layers.4.self_attn.o_proj - layers.31.mlp.gate_proj - layers.30.mlp.gate_proj - layers.4.mlp.gate_proj - layers.3.mlp.gate_proj - layers.29.mlp.gate_proj - layers.28.mlp.gate_proj - layers.6.mlp.gate_proj - layers.27.mlp.gate_proj - layers.5.mlp.gate_proj - layers.26.mlp.gate_proj - layers.25.mlp.gate_proj - layers.7.mlp.gate_proj - layers.2.mlp.gate_proj - layers.24.mlp.gate_proj - layers.23.mlp.gate_proj - layers.10.mlp.gate_proj - layers.6.mlp.up_proj - layers.4.mlp.up_proj - layers.5.mlp.up_proj - layers.27.mlp.up_proj - layers.25.mlp.up_proj - layers.26.mlp.up_proj - layers.17.mlp.up_proj - layers.24.mlp.up_proj - layers.7.mlp.up_proj - layers.10.mlp.up_proj - layers.3.mlp.up_proj - layers.11.mlp.up_proj - layers.23.mlp.up_proj - layers.9.mlp.up_proj - layers.14.mlp.up_proj - layers.18.mlp.up_proj - layers.19.mlp.down_proj - layers.20.mlp.down_proj - layers.18.mlp.down_proj - layers.21.mlp.down_proj - layers.29.mlp.down_proj - layers.1.mlp.down_proj - layers.22.mlp.down_proj - layers.28.mlp.down_proj - layers.23.mlp.down_proj - layers.30.mlp.down_proj - layers.17.mlp.down_proj - layers.4.mlp.down_proj - layers.2.mlp.down_proj - layers.15.mlp.down_proj - layers.5.mlp.down_proj wandb_project: axolotl wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 5e-7 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 100 evals_per_epoch: 1 eval_table_size: eval_table_max_new_tokens: 128 save_steps: 1080 max_steps: 1080 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.17.0 - Tokenizers 0.15.0 - axolotl: 0.4.0 [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)