transformers safetensors mistral feature-extraction generated_from_trainer axolotl instruct finetune chatml gpt4 synthetic data distillation text-generation conversational en dataset:argilla/OpenHermes2.5-dpo-binarized-alpha base_model:mlabonne/NeuralMonarch-7B base_model:finetune:mlabonne/NeuralMonarch-7B license:cc-by-nc-4.0 text-generation-inference endpoints_compatible region:us
---
base_model: mlabonne/NeuralMonarch-7B
datasets:
- argilla/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 DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset but achieves better performance then [mlabonne/AlphaMonarch-7B](https://huggingface.co/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](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/Jgxw1FZRx7nNAdSh7nYt1.png)

## 🏆 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

```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)