blackfish021

apertus-lightpath-lora-v2-e2

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README

License: apache-2.0

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 PeftModelfrom 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 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 (track_2b/).

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Model Details

Model Provider

blackfish021

Model Tree

Base

andreasmartin/apertus-v1.5-8b-text

Adapter

this model

Input Modalities

Text

Output Modalities