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")# pip install -U transformers accelerate # 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: 2,265 Bytes
19c1f65 | 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | {
"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
}
]
}
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