sentinel-coder-qlora
This model is a fine-tuned version of Qwen/Qwen2.5-Coder-1.5B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1132
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4273 | 0.7030 | 50 | 0.4052 |
| 0.1574 | 1.4060 | 100 | 0.2152 |
| 0.1124 | 2.1090 | 150 | 0.1263 |
| 0.0864 | 2.8120 | 200 | 0.1132 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.3.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for ravindraog/sentinel-coder-qlora
Base model
Qwen/Qwen2.5-1.5B Finetuned
Qwen/Qwen2.5-Coder-1.5B Finetuned
Qwen/Qwen2.5-Coder-1.5B-Instruct
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "ravindraog/sentinel-coder-qlora")