| --- |
| base_model: andreasmartin/apertus-v1.5-8b-text |
| library_name: peft |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-generation |
| tags: |
| - lora |
| - apertus |
| - optical-networks |
| - intent-based-networking |
| - hack-apertus |
| --- |
| |
| # Apertus 8B lightpath decider |
|
|
| A LoRA adapter that decides a lightpath request directly: given the request (source, |
| destination, bit rate), the candidate paths with their state (hops, length, free spectrum, |
| modulation, the free blocks), and an operator's instruction in plain language, it answers |
| `path=1 slot=120` or `reject`. Built for Hack Apertus 2026 (online, Track 2B) by the project |
| *Apertus Lightpath*. In the final system it is the |
| **fallback**: instructions the policy language can express are translated by a reader |
| (`blackfish021/apertus-lightpath-translator-v3`) and enforced by a rule engine; instructions |
| outside the language, or carried only in part, go to this decider, whose choice a guard checks |
| against the part the language does carry. |
|
|
| ## Model details |
|
|
| - **Base model:** `andreasmartin/apertus-v1.5-8b-text` (text-only conversion of |
| `swiss-ai/Apertus-v1.5-8B`). |
| - **Method:** QLoRA, 4-bit NF4 base, rank 32, alpha 32, 79.7 M trainable parameters, |
| completion-only loss; one A100 80 GB, 138 minutes (two epochs, the second pass of two). Greedy |
| decoding constrained to the decision line. |
| - **Training data:** 12,000 decisions from Optical Networking Gym episodes (preset |
| `jocn_benchmark`, nobel-eu, seeds 101–106) labelled by rule-based teachers that follow the |
| intent: no intent, four single intents (avoid a link, fewest hops, least loaded, fewest slots) |
| and two intent pairs, with the avoid-link cases taught as filter-then-rank. Test episodes use |
| other seeds. |
| - **Files:** `adapter_model.safetensors` and `adapter_config.json` at the root are the released |
| adapter; `adapter-epoch1/` and `checkpoint/` are training artefacts. |
| - **Developed by:** Wadood Ranjha. **License:** Apache-2.0. |
|
|
| ## Results (from the project's results files) |
|
|
| - Closed loop at 390 Erlang, three seeds, violations of the intent in % of requests: avoid link |
| 3.3, fewest hops 0.6, least loaded 0.7, fewest slots 0.1, unseen wording 1.0, intent pairs |
| never seen together 12.9 (first fit: 24.5 / 10.4 / 43.2 / 0.1 / 19.3 / 33.9; the base model |
| without fine-tuning answers invalidly 88–98 % of the time). |
| - Offline agreement with the teacher's admissible set: 98.9 % on new traffic, 98.7 % on unseen |
| wording, 85.8 % on unseen pairs. |
| - It follows three instruction types it was never trained on (pass through a node 94.0 %, avoid |
| a node 83.5 %, a given candidate 84.0 % compliance, where an instruction-blind choice complies |
| on 16.5 / 32.5 / 11.5 %) and is no better than the shortest candidate on hop or length limits. |
| - On the third round's probes it complies on 91.1 % of requests against 99.6 % for the reader |
| with the rule engine; composition is its weakness (one clause 96.2 %, three clauses 76.2 %), |
| which is why the system translates first and decides only what it cannot translate. |
| - A decider trained on the reader's own sentences (`blackfish021/apertus-lightpath-decider-v3-e2`) |
| complies on 96.4 % of the same probes, so about two thirds of that gap is training data; in |
| closed loop it still breaks 14.1 % of in-language requests against the reader's 0.9 %. |
|
|
| ## How to use |
|
|
| ```python |
| from peft import PeftModel |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| base = "andreasmartin/apertus-v1.5-8b-text" |
| tok = AutoTokenizer.from_pretrained(base) |
| model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(base), "blackfish021/apertus-lightpath-lora-v2-e2") |
| ``` |
|
|
| The prompt is rendered by `src/prompt.py` of the [project repository](https://github.com/theBlackfish01/ApertusNetworkGym) from the simulator's request |
| analysis; `src/policies.py` parses the answer. A GGUF of the adapter for llama.cpp is in |
| `blackfish021/apertus-lightpath-translator-v3-gguf` (`lightpath-decider-v2-lora-f16.gguf`). |
|
|
| ## Intended use and limits |
|
|
| Research and prototyping on a simulator: the model ranks candidate paths the simulator has |
| already checked for feasibility, it computes no physics. Trained on one topology and one traffic |
| model; English instructions; synthetic data. Other models' role: Claude (Anthropic) wrote the |
| project's code and documentation under the author's direction and the wording templates; no |
| other model is part of the system. |
|
|
| Repository: [github.com/theBlackfish01/ApertusNetworkGym](https://github.com/theBlackfish01/ApertusNetworkGym) (`track_2b/`). |
|
|
