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:
- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
language:
- en
library_name: transformers
pipeline_tag: text-generation
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
🏆 Evaluation
Task | Version | Metric | Value | Stderr | |
---|---|---|---|---|---|
arc_challenge | 0 | acc | 70.30 | ± | 1.33 |
acc_norm | 73.12 | ± | 1.29 | ||
hellaswag | 0 | acc | 71.80 | ± | 0.44 |
acc_norm | 89.20 | ± | 0.30 | ||
gsm8k | 0 | acc | 66.71 | ± | 1.29 |
winogrande | 0 | acc | 84.60 | ± | 1.01 |
mmlu | 0 | acc | 64.69 | ± | 1.00 |
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 0.6630 _ 0.0138 acc_norm 0.6826 _ 0.0136 arc_easy 0 acc 0.8657 _ 0.0070 acc_norm 0.8081 _ 0.0081 boolq 1 acc 0.8716 _ 0.0059 hellaswag 0 acc 0.6960 _ 0.0046 acc_norm 0.8745 _ 0.0033 openbookqa 0 acc 0.3920 _ 0.0219 acc_norm 0.4960 _ 0.0224 piqa 0 acc 0.8303 _ 0.0088 acc_norm 0.8487 _ 0.0084 winogrande 0 acc 0.8106 _ 0.0110
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