LoRA adapter for Mistral Instruct 7B v0.2
Training details
- Optimizer: awbn8
- Computational datatype: fp16
- Learning rate: 0.00005
- LoRA rank: 1024
- LoRA alpha multiplier: 2
- LoRA dropout: 0.05
- Batch size: 2
- Gradient accumulation: 8
- Gradient checkpointing: Enabled
- Number of epochs: 3
- Context size: 3072
- Quantization: q4bit
Input type: template
Output type: template
LoRA adapter for Mistral Instruct 7B v0.2
Training details
- Optimizer: awbn8
- Computational datatype: fp16
- Learning rate: 0.00005
- LoRA rank: 1024
- LoRA alpha multiplier: 2
- LoRA dropout: 0.05
- Batch size: 2
- Gradient accumulation: 8
- Gradient checkpointing: Enabled
- Number of epochs: 3
- Context size: 3072
- Quantization: q4bit
Input type: template
Output type: template
LoRA adapter for Mistral Instruct 7B v0.2
Training details
- Optimizer: awbn8
- Computational datatype: fp16
- Learning rate: 0.00005
- LoRA rank: 1024
- LoRA alpha multiplier: 2
- LoRA dropout: 0.05
- Batch size: 2
- Gradient accumulation: 8
- Gradient checkpointing: Enabled
- Number of epochs: 3
- Context size: 3072
- Quantization: q4bit
Input type: template
Output type: template
LoRA adapter for Mistral Instruct 7B v0.2
Training details
- Optimizer: awbn8
- Computational datatype: fp16
- Learning rate: 0.00005
- LoRA rank: 1024
- LoRA alpha multiplier: 2
- LoRA dropout: 0.05
- Batch size: 2
- Gradient accumulation: 8
- Gradient checkpointing: Enabled
- Number of epochs: 3
- Context size: 3072
- Quantization: q4bit
Input type: template
Output type: template
LoRA adapter for Mistral Instruct 7B v0.2
Training details
- Optimizer: awbn8
- Computational datatype: fp16
- Learning rate: 0.00005
- LoRA rank: 1024
- LoRA alpha multiplier: 2
- LoRA dropout: 0.05
- Batch size: 2
- Gradient accumulation: 8
- Gradient checkpointing: Enabled
- Number of epochs: 3
- Context size: 3072
- Quantization: q4bit
Input type: template
Output type: template
base_model: mistralai/Mistral-7B-Instruct-v0.2
library_name: peft
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