--- library_name: peft tags: - code - instruct - mistral datasets: - HuggingFaceH4/no_robots base_model: mistralai/Mistral-7B-v0.1 license: apache-2.0 --- ### Finetuning Overview: **Model Used:** mistralai/Mistral-7B-v0.1 **Dataset:** HuggingFaceH4/no_robots #### Dataset Insights: [No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. #### Finetuning Details: With the utilization of [MonsterAPI](https://monsterapi.ai)'s [no-code LLM finetuner](https://monsterapi.ai/finetuning), this finetuning: - Was achieved with great cost-effectiveness. - Completed in a total duration of 1h 15m 3s for 2 epochs using an A6000 48GB GPU. - Costed `$2.525` for the entire 2 epochs. #### Hyperparameters & Additional Details: - **Epochs:** 2 - **Cost Per Epoch:** $1.26 - **Total Finetuning Cost:** $2.525 - **Model Path:** mistralai/Mistral-7B-v0.1 - **Learning Rate:** 0.0002 - **Data Split:** 100% train - **Gradient Accumulation Steps:** 64 - **lora r:** 64 - **lora alpha:** 16 #### Prompt Structure ``` <|system|> <|user|> [USER PROMPT] <|assistant|> [ASSISTANT ANSWER] ``` #### Train loss : ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ba46aa0a9866b28cb19a14/Badi_wgZLBsUdeIScEKs9.png) ### Benchmarking results : ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6313732454e6e5d9f0f797cd/ialM-cJygMgMgczskzicX.png) --- license: apache-2.0