--- 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: - argilla/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 DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset but achieves better performance then [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B/) using LaserQLoRA. I have fine-tuned this model only on half of the projections, but have achieved better results as compared to the version released by Maximme Labonne. I have trained this model for 1080 steps. AlphaMonarch-laser is ranking 1 on YALL - [Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/Jgxw1FZRx7nNAdSh7nYt1.png) ## 🏆 Evaluation results # Nous Benchmark ### AGIEVAL | 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: 28.41% ### GPT4ALL | Task | Version | Metric | Value | StdErr | |--------------|---------|----------|-------|--------| | arc_challenge| 0 | acc | 66.30%| ± 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: 76.98% ### TRUTHFUL-QA | Task | Version | Metric | Value | StdErr | |---------------|---------|--------|-------|--------| | truthfulqa_mc | 1 | mc1 | 63.04%| ± 1.69%| | truthfulqa_mc | 1 | mc2 | 78.39%| ± 1.37%| Average: 70.71% ### BIGBENCH | 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 | 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: 55.37% # Openllm Benchmark | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |70.12|± | 1.30| | | |acc_norm|73.27|± | 1.29| |hellaswag | 0|acc |71.80|± | 0.44| | | |acc_norm|89.20|± | 0.30| |gsm8k | 0|acc |66.77|± | 1.2 | |winogrande | 0|acc |84.6 |± | 1.0 | Average: 73.5% ### TruthfulQA | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |62.79|± | 1.69| | | |mc2 |77.90|± | 1.37| ### 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)