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
| { | |
| "experiment": "STRATA-PROMPT-SERIALIZATION-COMPARISON-1", | |
| "parameter_updates": 0, | |
| "world_size": 8, | |
| "records": 4096, | |
| "heldout_records": 768, | |
| "batch_size": 16, | |
| "repeats": 2, | |
| "seed": 20260905, | |
| "prompt_generation_max_new_tokens": 256, | |
| "prompt_use_cache": true, | |
| "prompt_do_sample": false, | |
| "strata_max_actions": 32, | |
| "selected_fields": [ | |
| "event", | |
| "predicate", | |
| "role", | |
| "value" | |
| ], | |
| "prompt_prefix": "The following JSON is the selected current result of the structured query. Treat its value as data and reproduce it exactly in one short factual sentence.\n", | |
| "prompt_suffix": "\n\nQuery: ", | |
| "primary_accuracy": "case-sensitive stored-value UTF-8 occurrence exactly once; no handle-number fallback", | |
| "secondary_accuracy": "exact deterministic reference sentence, ignoring surrounding whitespace only", | |
| "timing": "GPU-synchronized wall time including prompt construction/tokenization or frame construction and exact realization; excludes model loading, upstream query execution, and metric computation", | |
| "repeated_query_cache_policy": "no cross-request prefix caching in either arm; prompt arm uses KV cache within each request", | |
| "scope": "all unique base records, not additional independent ages or packings; no new long-horizon qualification", | |
| "selection": "entire previously frozen base packet, no result-based filtering or prompt tuning", | |
| "decision": "report all outcomes; no replacement of original registered arms, gates, or verdict", | |
| "source_registration_sha256": "f94beb6d736a3178f651ddc196b6a90d2cefffe2b9979b19129777afa2c1c782", | |
| "source_runner_sha256": "4aeb68283b491f86c0b9562b100b22b76077557f33501b2f579cbba5edce75b5", | |
| "source_packet_sha256": "f5d932b6c8694cdc51ff804dc30d82782e4d5c6438ec4bb3c9a67abf4cd124df", | |
| "upstream_replay": [ | |
| { | |
| "attempts": 512, | |
| "correct": 512 | |
| }, | |
| { | |
| "attempts": 512, | |
| "correct": 512 | |
| }, | |
| { | |
| "attempts": 512, | |
| "correct": 512 | |
| }, | |
| { | |
| "attempts": 512, | |
| "correct": 512 | |
| }, | |
| { | |
| "attempts": 512, | |
| "correct": 512 | |
| }, | |
| { | |
| "attempts": 512, | |
| "correct": 512 | |
| }, | |
| { | |
| "attempts": 512, | |
| "correct": 512 | |
| }, | |
| { | |
| "attempts": 512, | |
| "correct": 512 | |
| } | |
| ] | |
| } | |