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. 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.
🏆 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
