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theakomolafe
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Model Details
Model Provider
theakomolafe
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Evaluated on EleriBench's locked, 1,500-example test set, against two frontier few-shot baselines on the identical prompt and output schema:
| model | verdict acc | anomaly macro-F1 | ECE (10-bin) | cost / 1k audits |
|---|---|---|---|---|
| eleri-1.5b | 0.972 | 0.989 | 0.050 | $0.04–0.62 * |
| claude-haiku-4-5 (few-shot) | 0.808 | 0.684 | 0.128 | $5.26 |
| gpt-4o-mini (few-shot) | 0.682 | 0.392 | 0.240 | $0.60 |
* 0.62/1kisthetraining−timeevaluationharness′snaiveper−callGPU−timeestimate;0.04–0.08/1k is measured cost in a production serving setup (batched, constrained decoding via vLLM). Full per-anomaly breakdown and methodology in the .
python
from vllm import LLM, SamplingParamsfrom vllm.lora.request import LoRARequestfrom vllm.sampling_params import StructuredOutputsParamsllm = LLM(model="Qwen/Qwen2.5-1.5B-Instruct", enable_lora=True, max_lora_rank=16)lora_request = LoRARequest("eleri", 1, "<path to this adapter>")# See EleriBench's schemas/python/prompt.py and output_schema.py for the# exact system prompt and JSON schema this model was trained against —# both matter for getting these results; a different prompt/schema will# give different (likely worse) results.outputs = llm.generate(prompts,SamplingParams(structured_outputs=StructuredOutputsParams(json=ELERI_VERDICT_JSON_SCHEMA)),lora_request=lora_request,)
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct", torch_dtype="bfloat16")model = PeftModel.from_pretrained(base, "theakomolafe/eleri-1.5b")tokenizer = AutoTokenizer.from_pretrained("theakomolafe/eleri-1.5b")
Without the exact system prompt and constrained-JSON output schema from
EleriBench, you'll get
free-text generations, not the structured verdicts this model was tuned to
produce — grab schemas/python/prompt.py and
schemas/python/output_schema.py from that repo before using this
adapter for anything.
30,000 synthetic, label-first-generated examples (TransactionRecord →
EleriVerdict pairs) across 16 spend categories, 15 anomaly types, and 3
verdicts, covering x402, AP2, ACP, and Stripe-shaped payment records. Full
generation methodology, the label-quality bugs found and fixed during this
model's own development, and the exact test-set hash are documented in
EleriBench's datagen/.
Built for one narrow task: verifying whether a specific payment matches a specific stated authorization. It is not a general chat model, not a fraud model for arbitrary transactions without a mandate to check against, and its outputs should inform a review process, not autonomously approve, block, or move money — that's the boundary Sawa (the product this model was built for) enforces in practice: Eleri audits after the fact and flags what looks off; it never has the ability to act on a payment itself.
The 30,000-example training set is synthetic. It's realistic by construction (protocol-accurate field shapes, a curated vendor/purpose pool per category, label-first generation so every ground truth is exact rather than inferred) but it is not real transaction data, and real-world distribution shift is untested beyond EleriBench's own held-out split.
Apache 2.0, same as the base model (Qwen2.5-1.5B-Instruct).