qwen2.5-hassaniya-lora
This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct on the hassaniya_alpaca_train and the hassaniya_sharegpt_train datasets. It achieves the following results on the evaluation set:
- Loss: 1.4983
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: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9216 | 0.8715 | 50 | 1.7769 |
| 1.2697 | 1.7495 | 100 | 1.5439 |
| 0.9717 | 2.6275 | 150 | 1.5017 |
Framework versions
- PEFT 0.14.0
- Transformers 4.51.3
- Pytorch 2.10.0+cu128
- Datasets 2.21.0
- Tokenizers 0.21.1
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from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct") model = PeftModel.from_pretrained(base_model, "adeltlb/qwen2.5-hassaniya-lora")