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 "jlpan/SteloCoder" \
--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": "jlpan/SteloCoder",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
moe_training
This is the final stage of training SteloCoder - MoE (Mixture of Experts) training. The dataset contains samples of code translation with five programming languages to python. The training/validation/testing data is processed and is souced from XLCoST dataset.
Model description
The final model is named SteloCoder, a model designed for code machine translation from multiple languages (C++, C#, Java, JavaScript, PHP) to Python. It is based on StarCoder to which we have added additional parameters using LoRA and MoE methods.
Intended uses & limitations
More information needed
Training and evaluation data
The data is processed sourced from XLCoST dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Rate |
|---|---|---|---|---|
| 0.1293 | 0.05 | 50 | 0.1218 | 5e-05 |
| 0.1332 | 0.1 | 100 | 0.1135 | 0.0000 |
| 0.1346 | 0.15 | 150 | 0.1117 | 0.0000 |
| 0.1336 | 0.2 | 200 | 0.1127 | 0.0000 |
| 0.1378 | 0.25 | 250 | 0.1116 | 0.0000 |
| 0.1321 | 0.3 | 300 | 0.1083 | 0.0000 |
| 0.1335 | 0.35 | 350 | 0.1075 | 0.0000 |
| 0.1316 | 0.4 | 400 | 0.1065 | 0.0000 |
| 0.1298 | 0.45 | 450 | 0.1062 | 0.0000 |
| 0.1331 | 0.5 | 500 | 0.1055 | 0.0000 |
| 0.1355 | 0.55 | 550 | 0.1048 | 0.0000 |
| 0.1299 | 0.6 | 600 | 0.1044 | 0.0000 |
| 0.1387 | 0.65 | 650 | 0.1048 | 0.0000 |
| 0.1278 | 0.7 | 700 | 0.1047 | 0.0000 |
| 0.1285 | 0.75 | 750 | 0.1045 | 0.0000 |
| 0.1278 | 0.8 | 800 | 0.1045 | 0.0000 |
| 0.1283 | 0.85 | 850 | 0.1045 | 0.0000 |
| 0.124 | 0.9 | 900 | 0.1043 | 0.0000 |
| 0.1258 | 0.95 | 950 | 0.1043 | 0.0000 |
| 0.1319 | 1.0 | 1000 | 0.1043 | 0.0 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
- Downloads last month
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Model tree for jlpan/SteloCoder
Base model
bigcode/starcoder
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "jlpan/SteloCoder" \ --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": "jlpan/SteloCoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'