Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "ninagroot/GPT2-705-RUN1" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ninagroot/GPT2-705-RUN1",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
GPT2-705M
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.5538
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: 0.00025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 9.7407 | 0.57 | 1 | 9.7354 |
| 8.0949 | 1.71 | 3 | 9.2987 |
| 8.037 | 2.86 | 5 | 7.9942 |
| 8.4143 | 4.0 | 7 | 8.3825 |
| 7.7196 | 4.57 | 8 | 8.7978 |
| 7.2632 | 5.71 | 10 | 7.6261 |
| 6.9715 | 6.86 | 12 | 7.4135 |
| 6.4835 | 8.0 | 14 | 8.2776 |
| 7.1529 | 8.57 | 15 | 7.0085 |
| 6.1255 | 9.71 | 17 | 6.8228 |
| 5.9176 | 10.86 | 19 | 6.5603 |
| 5.5785 | 12.0 | 21 | 6.3862 |
| 5.4833 | 12.57 | 22 | 6.3011 |
| 5.1483 | 13.71 | 24 | 6.0480 |
| 4.9268 | 14.86 | 26 | 6.0532 |
| 4.6602 | 16.0 | 28 | 5.7750 |
| 4.5647 | 16.57 | 29 | 5.7046 |
| 4.3202 | 17.71 | 31 | 5.5333 |
| 4.1764 | 18.86 | 33 | 5.5809 |
| 4.1745 | 20.0 | 35 | 5.4089 |
| 4.0056 | 20.57 | 36 | 5.3978 |
| 3.8024 | 21.71 | 38 | 5.4085 |
| 3.5845 | 22.86 | 40 | 5.3279 |
| 3.4378 | 24.0 | 42 | 5.3881 |
| 3.3361 | 24.57 | 43 | 5.2754 |
| 3.2585 | 25.71 | 45 | 5.2913 |
| 3.168 | 26.86 | 47 | 5.4232 |
| 2.9045 | 28.0 | 49 | 5.5044 |
| 2.8709 | 28.57 | 50 | 5.5538 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
- 17
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ninagroot/GPT2-705-RUN1" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ninagroot/GPT2-705-RUN1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'