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 "nnpy/Nape-0" \
--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": "nnpy/Nape-0",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Nape-0
Nape series are small models that tries to exihibit much capabilities. The model is still in training process. This is very early preview.
You can load it as follows:
from transformers import LlamaForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nnpy/Nape-0")
model = LlamaForCausalLM.from_pretrained("nnpy/Nape-0")
Training
It took 1 days to train 3 epochs on 4x A6000s using native deepspeed.
assistant role: You are Semica, a helpful AI assistant.
user: {prompt}
assistant:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 30.93 |
| ARC (25-shot) | 32.68 |
| HellaSwag (10-shot) | 58.68 |
| MMLU (5-shot) | 24.88 |
| TruthfulQA (0-shot) | 38.99 |
| Winogrande (5-shot) | 57.3 |
| GSM8K (5-shot) | 0.08 |
| DROP (3-shot) | 3.89 |
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "nnpy/Nape-0" \ --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": "nnpy/Nape-0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'