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 modeling_ss4m.py from aimlessdrivel/test: direct link, hf CLI and curl.
- Browser
- Download file 725 Bytes
-
https://huggingface.co/aimlessdrivel/test/resolve/main/modeling_ss4m.py
- Command line
-
hf download hf://aimlessdrivel/test/modeling_ss4m.py
-
curl -L -o modeling_ss4m.py https://huggingface.co/aimlessdrivel/test/resolve/main/modeling_ss4m.py
725 Bytes
| from transformers import PreTrainedModel | |
| from .configuration_ss4m import SimpleStories4MConfig | |
| from .nano_gpt_model import NanoGPT | |
| class SimpleStories4MModel(PreTrainedModel): | |
| config_class = SimpleStories4MConfig | |
| def __init__(self, config): | |
| super().__init__(config) | |
| hyperparameters = { | |
| "vocab_size": config.vocab_size, | |
| "block_size": config.block_size, | |
| "n_embed": config.n_embed, | |
| "n_heads": config.n_heads, | |
| "n_layers": config.n_layers, | |
| "dropout": config.dropout, | |
| } | |
| self.model = NanoGPT(hyperparameters) | |
| def forward(self, tensor, targets=None): | |
| return self.model(tensor, targets) |