metadata
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
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. We 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. We have trained this model for 1080 steps.
AlphaMonarch-laser is ranking 1 on YALL - Yet Another LLM Leaderboard.
🏆 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
