Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("dakwi/chessgpt-medium-m")
model = AutoModelForCausalLM.from_pretrained("dakwi/chessgpt-medium-m")Quick Links
chessgpt-medium-m
This model is a fine-tuned version of gpt2-medium on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9777
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.6517 | 0.08 | 250 | 1.5222 |
| 1.4486 | 0.16 | 500 | 1.3596 |
| 1.3463 | 0.24 | 750 | 1.2706 |
| 1.2623 | 0.32 | 1000 | 1.1986 |
| 1.2115 | 0.4 | 1250 | 1.1483 |
| 1.167 | 0.48 | 1500 | 1.1158 |
| 1.1327 | 0.56 | 1750 | 1.0757 |
| 1.1058 | 0.64 | 2000 | 1.0555 |
| 1.0798 | 0.72 | 2250 | 1.0320 |
| 1.0585 | 0.8 | 2500 | 1.0099 |
| 1.0435 | 0.88 | 2750 | 0.9929 |
| 1.0276 | 0.96 | 3000 | 0.9811 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for dakwi/chessgpt-medium-m
Base model
openai-community/gpt2-medium
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dakwi/chessgpt-medium-m")