| # Run OpenSML-Base-175M |
|
|
| The package contains BF16 `model.safetensors`, the frozen tokenizer, configuration, |
| raw text-completion runner and native architecture/kernel source. The verified |
| platform is Apple Silicon macOS with Python 3.11 and stock MLX 0.32.3. |
|
|
| ```bash |
| python3 -m venv .venv |
| source .venv/bin/activate |
| python -m pip install huggingface_hub |
| # Authenticate if access requires it: hf auth login |
| hf download OpenSML/OpenSML-Base-175M --local-dir ./OpenSML-Base-175M |
| cd OpenSML-Base-175M |
| python -m pip install -r requirements.txt |
| python inference.py --prompt "Plants need sunlight because" --max-tokens 64 |
| ``` |
|
|
| For a file prefix and machine-readable output: |
|
|
| ```bash |
| python inference.py --prompt-file prompt.txt --max-tokens 128 --json |
| ``` |
|
|
| Default decoding is greedy, temperature 0. Sampling is available via |
| `--temperature 0.7 --seed 42`; packaging checks use greedy decoding. Input is a |
| raw document prefix. No user/assistant header, chat template, JSON Schema or |
| extraction adapter is applied. The output is a continuation, not an instruction- |
| tuned assistant response. |
|
|
| The runner defaults to the adjacent `model.safetensors`, checks its weight hash, |
| verifies/extracts `native_bundle.zip` to a temporary directory and loads the exact |
| native model/tokenizer. Generation runs offline after download and dependency |
| installation. Prompt plus generation budget must fit 2,048 tokens; overlong |
| requests are rejected rather than truncated. Generation stops on document-end, |
| a reserved structural token, or the token limit. |
|
|
| Use this supplied native runner. `transformers.AutoModel` and `mlx_lm.load` are |
| not supported auto-loading paths for this custom architecture. CPU-only, CUDA, |
| quantization and framework conversions have not been validated. The root |
| `tokenizer.json` and `tokenizer_config.json` mirror the bundle's frozen files; |
| the native bundle also includes all tokenizer integrity companions. |
|
|
| Verify all package files on macOS: |
|
|
| ```bash |
| shasum -a 256 -c SHA256SUMS |
| ``` |
|
|
| The source archive contains `pretraining/sml_v1/model.py`, tokenization/common |
| modules and the matching packed SwiGLU/CE kernels. Its full source inventory is |
| recorded in `SOURCE_FILES.json`. These can be extracted with |
| `python -m zipfile -e native_bundle.zip native_bundle` for inspection or use in a |
| custom fine-tuning implementation. This download is an inference package, not a |
| one-command reproduction of the distributed pretraining corpus/optimizer state. |
|
|
| Base completions can repeat, invent facts or continue with irrelevant document |
| text. The recorded document loss does not guarantee correct QA, instruction |
| following or extraction. See the model card and report for measured scope. |
|
|
|
|
| ## JSONLite descendant |
|
|
| The [OpenSML-JSONLite-175M-Instruct](https://huggingface.co/OpenSML/OpenSML-JSONLite-175M) descendant uses 100,096,883 additional structured-data continued-pretraining tokens followed by one joint full-weight SFT. The main checkpoint inherits 32,000 SFT presentations. On the observed original 1,444-request extraction panel it scores 89.54% complete correctness, 94.91% field accuracy, 93.75% extracted-value F1, 96.13% missing-field accuracy and 99.93% schema validity. Broader structural diagnostics remain weaker: 183/384 complete requests (47.66%). These are descendant results, not scores of this base. See the [JSONLite technical report](https://huggingface.co/OpenSML/OpenSML-JSONLite-175M/blob/main/TECHNICAL_REPORT.md). |
|
|
| JSONLite's supplied runner targets short English text and flat string-based schemas. Broader JSON tasks, arbitrary domains, chat and multilingual reliability are not established. Check schema validity and source-grounded meaning separately. The original base weights, tokenizer and inference runtime are unchanged. |
|
|
