Text Generation
Transformers
Safetensors
English
simple_stories_4m
text-generation-inference
custom_code
Instructions to use aimlessdrivel/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aimlessdrivel/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aimlessdrivel/test", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("aimlessdrivel/test", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aimlessdrivel/test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aimlessdrivel/test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aimlessdrivel/test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aimlessdrivel/test
- SGLang
How to use aimlessdrivel/test 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 "aimlessdrivel/test" \ --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": "aimlessdrivel/test", "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 "aimlessdrivel/test" \ --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": "aimlessdrivel/test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aimlessdrivel/test with Docker Model Runner:
docker model run hf.co/aimlessdrivel/test
Download configuration_ss4m.py from aimlessdrivel/test: direct link, hf CLI and curl.
- Browser
- Download file 598 Bytes
-
https://huggingface.co/aimlessdrivel/test/resolve/main/configuration_ss4m.py
- Command line
-
hf download hf://aimlessdrivel/test/configuration_ss4m.py
-
curl -L -o configuration_ss4m.py https://huggingface.co/aimlessdrivel/test/resolve/main/configuration_ss4m.py
598 Bytes
| from transformers import PretrainedConfig | |
| class SimpleStories4MConfig(PretrainedConfig): | |
| model_type = "simple_stories_4m" | |
| def __init__( | |
| self, | |
| vocab_size: int = 2048, | |
| block_size: int = 1080, | |
| n_embed: int = 256, | |
| n_heads: int = 2, | |
| n_layers: int = 4, | |
| dropout: float = 0.1, | |
| **kwargs | |
| ): | |
| self.vocab_size = vocab_size | |
| self.block_size = block_size | |
| self.n_embed = n_embed | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.dropout = dropout | |
| super().__init__(**kwargs) |