Text Generation
Transformers
strata
persistent-memory
structured-memory
neuro-symbolic
exact-value-copying
Instructions to use nur-dev/strata-native-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nur-dev/strata-native-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nur-dev/strata-native-lm")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nur-dev/strata-native-lm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nur-dev/strata-native-lm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nur-dev/strata-native-lm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nur-dev/strata-native-lm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nur-dev/strata-native-lm
- SGLang
How to use nur-dev/strata-native-lm 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 "nur-dev/strata-native-lm" \ --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": "nur-dev/strata-native-lm", "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 "nur-dev/strata-native-lm" \ --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": "nur-dev/strata-native-lm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nur-dev/strata-native-lm with Docker Model Runner:
docker model run hf.co/nur-dev/strata-native-lm
Download verify.py from nur-dev/strata-native-lm: direct link, hf CLI and curl.
- Browser
- Download file 1.01 kB
-
https://huggingface.co/nur-dev/strata-native-lm/resolve/main/verify.py
- Command line
-
hf download hf://nur-dev/strata-native-lm/verify.py
-
curl -L -o verify.py https://huggingface.co/nur-dev/strata-native-lm/resolve/main/verify.py
1.01 kB
| #!/usr/bin/env python3 | |
| """Verify the SHA-256 manifest for the STRATA Native LM v1 release.""" | |
| from __future__ import annotations | |
| import hashlib | |
| from pathlib import Path | |
| def digest(path: Path) -> str: | |
| value = hashlib.sha256() | |
| with path.open("rb") as handle: | |
| for block in iter(lambda: handle.read(1024 * 1024), b""): | |
| value.update(block) | |
| return value.hexdigest() | |
| def main() -> None: | |
| root = Path(__file__).resolve().parent | |
| manifest = root / "MANIFEST.sha256" | |
| failures: list[str] = [] | |
| entries = 0 | |
| for line in manifest.read_text(encoding="utf-8").splitlines(): | |
| if not line: | |
| continue | |
| expected, relative = line.split(" ", 1) | |
| path = root / relative | |
| entries += 1 | |
| if not path.is_file() or digest(path) != expected: | |
| failures.append(relative) | |
| if failures: | |
| raise SystemExit(f"verification failed: {failures}") | |
| print(f"VERIFIED {entries} files") | |
| if __name__ == "__main__": | |
| main() | |