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--- |
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license: cc-by-nc-4.0 |
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base_model: mlabonne/NeuralMonarch-7B |
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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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model-index: |
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- name: AlphaMonarch-laser |
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results: [] |
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datasets: |
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- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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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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![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/62S_ExHO6NKCM3NhPDrds.jpeg) |
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AlphaMonarch-laser is a new DPO merge using laserQLoRA that retains all the reasoning abilities of the very best merges and significantly improves its |
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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) |
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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). |
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* [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) |
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* [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha) |
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</details><br> |
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## Evaluation data |
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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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🏆 Evaluation |
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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: 75.9% without mmlu |
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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 |63.03|± | 1.68| |
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| | |mc2 |78.39|± | 1.37| |
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### BigBench Reasoning Test |
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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 | | | | |
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| bigbench_geometric_shapes | 0| 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: 49.08% |
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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 | 66.29 | _ | 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: 68.75% |
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### AGIEVAL |
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Here is the converted table in the required format, including multiplication of all values by 100 and calculating the average for the value column: |
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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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| | | acc\_norm | 26.38 | 2.77 | |
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| agieval\_logiqa\_en | 0| acc | 38.25 | 1.91 | |
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| | | acc\_norm | 38.09 | 1.90 | |
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| agieval\_lsat\_ar | 0| acc | 23.91 | 2.82 | |
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| | | acc\_norm | 23.48 | 2.80 | |
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| agieval\_lsat\_lr | 0| acc | 52.75 | 2.21 | |
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| | | acc\_norm | 53.92 | 2.21 | |
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| agieval\_lsat\_rc | 0 | acc | 66.91 | 2.87 | |
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| | | acc\_norm | 67.29 | 2.87 | |
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| agieval\_sat\_en | 0| acc | 78.64 | 2.86 | |
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| | | 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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| | | acc\_norm | 44.17 | 3.47 | |
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| agieval\_sat\_math | 0| acc | 33.18 | 3.18 | |
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| | | acc\_norm | 31.36 | 3.14 | |
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Average: 47.44% |
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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) |