Finetuning Overview:
Model Used: mistralai/Mistral-7B-v0.1
Dataset: HuggingFaceH4/no_robots
Dataset Insights:
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's no-code LLM finetuner, 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
$2.525for 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|> </s> <|user|> [USER PROMPT] </s> <|assistant|> [ASSISTANT ANSWER] </s>
Train loss :
Benchmarking results :
license: apache-2.0
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