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AlphaMonarch-laser

Text Generation
Transformers
Safetensors
English
mistral
feature-extraction
Generated from Trainer
axolotl
instruct
finetune
chatml
gpt4
synthetic data
distillation
conversational
text-generation-inference
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---
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
---
<!-- 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)