AlphaMonarch-laser

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 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.

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	Metric	Value	StdErr
agieval_aqua_rat	acc	28.35%	2.83%
agieval_aqua_rat	acc_norm	26.38%	2.77%
agieval_logiqa_en	acc	38.25%	1.91%
agieval_logiqa_en	acc_norm	38.10%	1.90%
agieval_lsat_ar	acc	23.91%	2.82%
agieval_lsat_ar	acc_norm	23.48%	2.80%
agieval_lsat_lr	acc	52.75%	2.21%
agieval_lsat_lr	acc_norm	53.92%	2.21%
agieval_lsat_rc	acc	66.91%	2.87%
agieval_lsat_rc	acc_norm	67.29%	2.87%
agieval_sat_en	acc	78.64%	2.86%
agieval_sat_en	acc_norm	78.64%	2.86%
agieval_sat_en_without_passage	acc	45.15%	3.48%
agieval_sat_en_without_passage	acc_norm	44.17%	3.47%
agieval_sat_math	acc	33.18%	3.18%
agieval_sat_math	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

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

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