How to use from
vLLMInstall from pip and serve model
# Install vLLM from pip:
pip install vllm# Start the vLLM server:
vllm serve "Wade5/MyModel"# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Wade5/MyModel",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/Wade5/MyModelQuick Links
MyModel
This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2093
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9491 | 0.2693 | 500 | 0.6303 |
| 0.6241 | 0.5385 | 1000 | 0.5958 |
| 0.5923 | 0.8078 | 1500 | 0.5590 |
| 0.5584 | 1.0770 | 2000 | 0.5180 |
| 0.5264 | 1.3463 | 2500 | 0.4764 |
| 0.5164 | 1.6155 | 3000 | 0.4459 |
| 0.5046 | 1.8848 | 3500 | 0.4069 |
| 0.3944 | 2.1540 | 4000 | 0.3134 |
| 0.3362 | 2.4233 | 4500 | 0.2675 |
| 0.32 | 2.6925 | 5000 | 0.2293 |
| 0.3115 | 2.9618 | 5500 | 0.2093 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Wade5/MyModel
Base model
deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
# Gated model: Login with a HF token with gated access permission hf auth login