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 "qqplot23/BASE_short" \
--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": "qqplot23/BASE_short",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
BASE_short
This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.3241
- Ppl: 28.7555
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: 4
- eval_batch_size: 4
- seed: 22554
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Ppl |
|---|---|---|---|---|
| 4.4673 | 1.25 | 4000 | 4.2416 | 71.9939 |
| 3.8603 | 2.5 | 8000 | 3.7253 | 43.0163 |
| 3.638 | 3.75 | 12000 | 3.5322 | 35.4396 |
| 3.5229 | 5.01 | 16000 | 3.4322 | 32.0556 |
| 3.4377 | 6.26 | 20000 | 3.3749 | 30.2611 |
| 3.3972 | 7.51 | 24000 | 3.3411 | 29.2534 |
| 3.3688 | 8.76 | 28000 | 3.3241 | 28.7555 |
Framework versions
- Transformers 4.35.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
- Downloads last month
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Model tree for qqplot23/BASE_short
Base model
openai-community/gpt2
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "qqplot23/BASE_short" \ --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": "qqplot23/BASE_short", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'