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
File size: 1,010 Bytes
1d60d59 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | #!/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()
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