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--- |
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library_name: peft |
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tags: |
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- meta-llama |
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- code |
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- instruct |
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- WizardLM |
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- Mistral-7B-v0.1 |
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datasets: |
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- WizardLM/WizardLM_evol_instruct_70k |
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base_model: mistralai/Mistral-7B-v0.1 |
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license: apache-2.0 |
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--- |
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### Finetuning Overview: |
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**Model Used:** mistralai/Mistral-7B-v0.1 |
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**Dataset:** WizardLM/WizardLM_evol_instruct_70k |
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#### Dataset Insights: |
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The WizardLM/WizardLM_evol_instruct_70k dataset, tailored specifically for enhancing interactive capabilities, it was developed using EVOL-Instruct method.Which will basically enhance a smaller dataset, with tougher quesitons for the LLM to perform |
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#### Finetuning Details: |
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With the utilization of [MonsterAPI](https://monsterapi.ai)'s [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm), this finetuning: |
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- Was achieved with great cost-effectiveness. |
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- Completed in a total duration of 5hrs 18mins for 1 epoch using an A6000 48GB GPU. |
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- Costed `$10` for the entire epoch. |
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#### Hyperparameters & Additional Details: |
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- **Epochs:** 1 |
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- **Cost Per Epoch:** $10 |
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- **Total Finetuning Cost:** $10 |
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- **Model Path:** mistralai/Mistral-7B-v0.1 |
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- **Learning Rate:** 0.0002 |
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- **Data Split:** 90% train 10% validation |
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- **Gradient Accumulation Steps:** 4 |
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--- |
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Prompt Structure |
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``` |
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### INSTRUCTION: |
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[instruction] |
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### RESPONSE: |
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[output] |
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``` |
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Training loss : |
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 |
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--- |
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#### Benchmark Results |
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 |
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``` |
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ARC (arc_challenge, acc_norm) 0.5543 |
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HellaSwag (hellaswag, acc_norm) 0.7979 |
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TruthfulQA (truthfulqa_mc2) 0.4781 |
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``` |
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license: apache-2.0 |
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