Text Generation
Transformers
Safetensors
llama
Generated from Trainer
text-generation-inference
4-bit precision
bitsandbytes
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Chat-Error/Testing_orca")
model = AutoModelForCausalLM.from_pretrained("Chat-Error/Testing_orca")Quick Links
qlora-out
This model is a fine-tuned version of microsoft/Orca-2-13b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5966
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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.645 | 0.23 | 100 | 1.6467 |
| 1.6459 | 0.45 | 200 | 1.6197 |
| 1.5916 | 0.68 | 300 | 1.6115 |
| 1.3587 | 0.9 | 400 | 1.6088 |
| 1.5232 | 1.12 | 500 | 1.6029 |
| 1.7568 | 1.34 | 600 | 1.6010 |
| 1.6536 | 1.57 | 700 | 1.5978 |
| 1.7821 | 1.8 | 800 | 1.5966 |
Framework versions
- Transformers 4.35.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1
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Model tree for Chat-Error/Testing_orca
Base model
microsoft/Orca-2-13b
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Chat-Error/Testing_orca")