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--- |
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language: |
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- en |
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license: cc-by-nc-4.0 |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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- axolotl |
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- mistral |
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- instruct |
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- finetune |
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- chatml |
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- gpt4 |
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- synthetic data |
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- distillation |
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datasets: |
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- argilla/OpenHermes2.5-dpo-binarized-alpha |
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base_model: mlabonne/NeuralMonarch-7B |
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pipeline_tag: text-generation |
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model-index: |
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- name: AlphaMonarch-laser |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# AlphaMonarch-laser |
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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. |
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AlphaMonarch-laser is ranking 1 on YALL - [Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). |
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## 🏆 Evaluation results |
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# Nous Benchmark |
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### AGIEVAL |
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| Task | Version | Metric | Value | StdErr | |
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|---------------------------------|---------|--------------|--------|--------| |
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| agieval_aqua_rat | 0 | acc | 28.35% | 2.83% | |
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| agieval_aqua_rat | 0 | acc_norm | 26.38% | 2.77% | |
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| agieval_logiqa_en | 0 | acc | 38.25% | 1.91% | |
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| agieval_logiqa_en | 0 | acc_norm | 38.10% | 1.90% | |
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| agieval_lsat_ar | 0 | acc | 23.91% | 2.82% | |
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| agieval_lsat_ar | 0 | acc_norm | 23.48% | 2.80% | |
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| agieval_lsat_lr | 0 | acc | 52.75% | 2.21% | |
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| agieval_lsat_lr | 0 | acc_norm | 53.92% | 2.21% | |
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| agieval_lsat_rc | 0 | acc | 66.91% | 2.87% | |
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| agieval_lsat_rc | 0 | acc_norm | 67.29% | 2.87% | |
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| agieval_sat_en | 0 | acc | 78.64% | 2.86% | |
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| agieval_sat_en | 0 | acc_norm | 78.64% | 2.86% | |
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| agieval_sat_en_without_passage | 0 | acc | 45.15% | 3.48% | |
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| agieval_sat_en_without_passage | 0 | acc_norm | 44.17% | 3.47% | |
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| agieval_sat_math | 0 | acc | 33.18% | 3.18% | |
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| agieval_sat_math | 0 | acc_norm | 31.36% | 3.14% | |
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Average: 28.41% |
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### GPT4ALL |
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| Task | Version | Metric | Value | StdErr | |
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|--------------|---------|----------|-------|--------| |
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| arc_challenge| 0 | acc | 66.30%| ± 1.38%| |
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| | | acc_norm | 68.26%| ± 1.36%| |
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| arc_easy | 0 | acc | 86.57%| ± 0.70%| |
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| | | acc_norm | 80.81%| ± 0.81%| |
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| boolq | 1 | acc | 87.16%| ± 0.59%| |
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| hellaswag | 0 | acc | 69.60%| ± 0.46%| |
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| | | acc_norm | 87.45%| ± 0.33%| |
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| openbookqa | 0 | acc | 39.20%| ± 2.19%| |
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| | | acc_norm | 49.60%| ± 2.24%| |
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| piqa | 0 | acc | 83.03%| ± 0.88%| |
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| | | acc_norm | 84.87%| ± 0.84%| |
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| winogrande | 0 | acc | 81.06%| ± 1.10%| |
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Average: 76.98% |
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### TRUTHFUL-QA |
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| Task | Version | Metric | Value | StdErr | |
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|---------------|---------|--------|-------|--------| |
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| truthfulqa_mc | 1 | mc1 | 63.04%| ± 1.69%| |
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| truthfulqa_mc | 1 | mc2 | 78.39%| ± 1.37%| |
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Average: 70.71% |
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### BIGBENCH |
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| Task | Version | Metric | Value | StdErr | |
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|------------------------------------------------|---------|-----------------------|-------|--------------------| |
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| bigbench_causal_judgement | 0 | multiple_choice_grade| 60.00%| ± 3.56% | |
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| bigbench_date_understanding | 0 | multiple_choice_grade| 62.06%| ± 2.53% | |
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| bigbench_disambiguation_qa | 0 | multiple_choice_grade| 54.26%| ± 3.11% | |
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| bigbench_geometric_shapes | 0 | multiple_choice_grade| 23.96%| ± 2.26% | |
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| | | exact_str_match | 0.00% | ± 0.00% | |
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| bigbench_logical_deduction_five_objects | 0 | multiple_choice_grade| 32.80%| ± 2.10% | |
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| bigbench_logical_deduction_seven_objects | 0 | multiple_choice_grade| 23.86%| ± 1.61% | |
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| bigbench_logical_deduction_three_objects | 0 | multiple_choice_grade| 59.33%| ± 2.84% | |
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| bigbench_movie_recommendation | 0 | multiple_choice_grade| 58.00%| ± 2.21% | |
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| bigbench_navigate | 0 | multiple_choice_grade| 56.00%| ± 1.57% | |
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| bigbench_reasoning_about_colored_objects | 0 | multiple_choice_grade| 69.20%| ± 1.03% | |
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| bigbench_ruin_names | 0 | multiple_choice_grade| 55.36%| ± 2.35% | |
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| bigbench_salient_translation_error_detection | 0 | multiple_choice_grade| 41.48%| ± 1.56% | |
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| bigbench_snarks | 0 | multiple_choice_grade| 73.48%| ± 3.29% | |
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| bigbench_sports_understanding | 0 | multiple_choice_grade| 76.06%| ± 1.36% | |
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| bigbench_temporal_sequences | 0 | multiple_choice_grade| 55.50%| ± 1.57% | |
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| bigbench_tracking_shuffled_objects_five_objects| 0 | multiple_choice_grade| 23.28%| ± 1.20% | |
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| bigbench_tracking_shuffled_objects_seven_objects| 0 | multiple_choice_grade| 19.37%| ± 0.94% | |
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| bigbench_tracking_shuffled_objects_three_objects| 0 | multiple_choice_grade| 59.33%| ± 2.84% | |
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Average: 55.37% |
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# Openllm Benchmark |
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| Task |Version| Metric |Value| |Stderr| |
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|-------------|------:|--------|----:|---|-----:| |
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|arc_challenge| 0|acc |70.12|± | 1.30| |
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| | |acc_norm|73.27|± | 1.29| |
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|hellaswag | 0|acc |71.80|± | 0.44| |
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| | |acc_norm|89.20|± | 0.30| |
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|gsm8k | 0|acc |66.77|± | 1.2 | |
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|winogrande | 0|acc |84.6 |± | 1.0 | |
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Average: 73.5% |
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### TruthfulQA |
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| Task |Version|Metric|Value| |Stderr| |
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|-------------|------:|------|----:|---|-----:| |
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|truthfulqa_mc| 1|mc1 |62.79|± | 1.69| |
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| | |mc2 |77.90|± | 1.37| |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-07 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 1080 |
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### 📝 Axolotl Configuration |
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```yaml |
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base_model: mlabonne/NeuralMonarch-7B |
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model_type: MistralForCausalLM |
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tokenizer_type: LlamaTokenizer |
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is_mistral_derived_model: true |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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rl: dpo |
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chat_template: chatml |
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datasets: |
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- path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha |
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split: train |
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type: chatml.intel |
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dataset_prepared_path: |
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val_set_size: 0.01 |
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output_dir: ./out |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 1800 |
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sample_packing: false |
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pad_to_sequence_len: false |
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lora_r: 16 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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lora_target_modules: |
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- layers.1.self_attn.q_proj |
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- layers.0.self_attn.q_proj |
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- layers.15.self_attn.q_proj |
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- layers.12.self_attn.q_proj |
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- layers.11.self_attn.q_proj |
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- layers.14.self_attn.q_proj |
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- layers.9.self_attn.q_proj |
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- layers.16.self_attn.q_proj |
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- layers.30.self_attn.q_proj |
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- layers.18.self_attn.q_proj |
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- layers.13.self_attn.q_proj |
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- layers.10.self_attn.q_proj |
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- layers.7.self_attn.q_proj |
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- layers.8.self_attn.q_proj |
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- layers.4.self_attn.q_proj |
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- layers.19.self_attn.q_proj |
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- layers.27.self_attn.k_proj |
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- layers.24.self_attn.k_proj |
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- layers.25.self_attn.k_proj |
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- layers.22.self_attn.k_proj |
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- layers.26.self_attn.k_proj |
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- layers.29.self_attn.k_proj |
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- layers.23.self_attn.k_proj |
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- layers.28.self_attn.k_proj |
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- layers.21.self_attn.k_proj |
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- layers.31.self_attn.k_proj |
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- layers.30.self_attn.k_proj |
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- layers.20.self_attn.k_proj |
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- layers.5.self_attn.k_proj |
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- layers.19.self_attn.k_proj |
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- layers.17.self_attn.k_proj |
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- layers.18.self_attn.k_proj |
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- layers.19.self_attn.v_proj |
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- layers.24.self_attn.v_proj |
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- layers.18.self_attn.v_proj |
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- layers.5.self_attn.v_proj |
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- layers.3.self_attn.v_proj |
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- layers.16.self_attn.v_proj |
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- layers.23.self_attn.v_proj |
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- layers.27.self_attn.v_proj |
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- layers.25.self_attn.v_proj |
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- layers.26.self_attn.v_proj |
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- layers.20.self_attn.v_proj |
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- layers.6.self_attn.v_proj |
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- layers.15.self_attn.v_proj |
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- layers.17.self_attn.v_proj |
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- layers.29.self_attn.v_proj |
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- layers.22.self_attn.v_proj |
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- layers.12.self_attn.o_proj |
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- layers.9.self_attn.o_proj |
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- layers.14.self_attn.o_proj |
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- layers.0.self_attn.o_proj |
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- layers.6.self_attn.o_proj |
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- layers.8.self_attn.o_proj |
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- layers.10.self_attn.o_proj |
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- layers.11.self_attn.o_proj |
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- layers.13.self_attn.o_proj |
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- layers.24.self_attn.o_proj |
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- layers.7.self_attn.o_proj |
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- layers.15.self_attn.o_proj |
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- layers.5.self_attn.o_proj |
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- layers.17.self_attn.o_proj |
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- layers.25.self_attn.o_proj |
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- layers.4.self_attn.o_proj |
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- layers.31.mlp.gate_proj |
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- layers.30.mlp.gate_proj |
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- layers.4.mlp.gate_proj |
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- layers.3.mlp.gate_proj |
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- layers.29.mlp.gate_proj |
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- layers.28.mlp.gate_proj |
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- layers.6.mlp.gate_proj |
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- layers.27.mlp.gate_proj |
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- layers.5.mlp.gate_proj |
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- layers.26.mlp.gate_proj |
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- layers.25.mlp.gate_proj |
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- layers.7.mlp.gate_proj |
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- layers.2.mlp.gate_proj |
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- layers.24.mlp.gate_proj |
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- layers.23.mlp.gate_proj |
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- layers.10.mlp.gate_proj |
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- layers.6.mlp.up_proj |
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- layers.4.mlp.up_proj |
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- layers.5.mlp.up_proj |
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- layers.27.mlp.up_proj |
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- layers.25.mlp.up_proj |
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- layers.26.mlp.up_proj |
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- layers.17.mlp.up_proj |
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- layers.24.mlp.up_proj |
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- layers.7.mlp.up_proj |
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- layers.10.mlp.up_proj |
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- layers.3.mlp.up_proj |
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- layers.11.mlp.up_proj |
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- layers.23.mlp.up_proj |
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- layers.9.mlp.up_proj |
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- layers.14.mlp.up_proj |
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- layers.18.mlp.up_proj |
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- layers.19.mlp.down_proj |
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- layers.20.mlp.down_proj |
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- layers.18.mlp.down_proj |
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- layers.21.mlp.down_proj |
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- layers.29.mlp.down_proj |
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- layers.1.mlp.down_proj |
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- layers.22.mlp.down_proj |
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- layers.28.mlp.down_proj |
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- layers.23.mlp.down_proj |
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- layers.30.mlp.down_proj |
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- layers.17.mlp.down_proj |
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- layers.4.mlp.down_proj |
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- layers.2.mlp.down_proj |
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- layers.15.mlp.down_proj |
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- layers.5.mlp.down_proj |
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wandb_project: axolotl |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 8 |
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micro_batch_size: 1 |
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num_epochs: 1 |
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optimizer: paged_adamw_32bit |
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lr_scheduler: cosine |
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learning_rate: 5e-7 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: false |
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tf32: true |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 100 |
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evals_per_epoch: 1 |
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eval_table_size: |
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eval_table_max_new_tokens: 128 |
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save_steps: 1080 |
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max_steps: 1080 |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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``` |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.0 |
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- axolotl: 0.4.0 |
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[<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) |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_abideen__AlphaMonarch-laser) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |76.00| |
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|AI2 Reasoning Challenge (25-Shot)|73.12| |
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|HellaSwag (10-Shot) |89.21| |
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|MMLU (5-Shot) |64.43| |
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|TruthfulQA (0-shot) |77.90| |
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|Winogrande (5-shot) |84.61| |
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|GSM8k (5-shot) |66.72| |
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