transformers safetensors generated_from_trainer axolotl mistral instruct finetune chatml gpt4 synthetic data distillation text-generation en dataset:mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha base_model:mlabonne/NeuralMonarch-7B base_model:finetune:mlabonne/NeuralMonarch-7B license:cc-by-nc-4.0 endpoints_compatible region:us
---
base_model: mlabonne/NeuralMonarch-7B
datasets:
- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
language:
- en
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
tags:
- generated_from_trainer
- axolotl
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: AlphaMonarch-laser
  results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# AlphaMonarch-laser 

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/62S_ExHO6NKCM3NhPDrds.jpeg)

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](https://huggingface.co/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](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha).


* [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B)
* [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha)

</details><br>


## 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                            | 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: 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

```yaml
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

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)