AlphaMonarch-laser / README.md
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metadata
language:
  - en
license: cc-by-nc-4.0
library_name: transformers
tags:
  - generated_from_trainer
  - axolotl
  - mistral
  - instruct
  - finetune
  - chatml
  - gpt4
  - synthetic data
  - distillation
datasets:
  - argilla/OpenHermes2.5-dpo-binarized-alpha
base_model: mlabonne/NeuralMonarch-7B
pipeline_tag: text-generation
model-index:
  - name: AlphaMonarch-laser
    results: []

AlphaMonarch-laser

image/jpeg

AlphaMonarch-laser is a DPO fine-tuned of mlabonne/NeuralMonarch-7B using the argilla/OpenHermes2.5-dpo-binarized-alpha preference dataset but achieves better performance then 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. image/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

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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 76.00
AI2 Reasoning Challenge (25-Shot) 73.12
HellaSwag (10-Shot) 89.21
MMLU (5-Shot) 64.43
TruthfulQA (0-shot) 77.90
Winogrande (5-shot) 84.61
GSM8k (5-shot) 66.72