Instructions to use nnpy/Nape-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nnpy/Nape-0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nnpy/Nape-0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nnpy/Nape-0") model = AutoModelForCausalLM.from_pretrained("nnpy/Nape-0") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nnpy/Nape-0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nnpy/Nape-0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nnpy/Nape-0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nnpy/Nape-0
- SGLang
How to use nnpy/Nape-0 with SGLang:
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 }'Use 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 }' - Docker Model Runner
How to use nnpy/Nape-0 with Docker Model Runner:
docker model run hf.co/nnpy/Nape-0
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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