Model Card — logx v0.1.0

Query your nginx logs in plain English — fully offline, read-only, 15M params.

How to use

Via the CLI (recommended — includes validation + safe execution):

Hit ⭐ https://github.com/devang007/logX

git clone https://github.com/devang007/logX && cd logX
./install.sh          # downloads this model zip automatically
logx -q "top 5 ips" -src /var/log/nginx/access.log

Run directly with 🤗 Transformers:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Devang007/logX")
model = AutoModelForSeq2SeqLM.from_pretrained("Devang007/logX")
ids = tok("parse: how many 502s in the last hour", return_tensors="pt").input_ids
print(tok.decode(model.generate(ids, max_length=128)[0], skip_special_tokens=True))
# {"action":"count","source":"nginx_access","filters":[{"field":"status","op":"eq","value":"502"}],"time":{"last":"1h"}}

But this would only give you the commands to run , parser and cli commands would run those dsl on terminal , so to really query your logs run it on cli.

NL → nginx-log-query translation model. Fine-tune of google/t5-efficient-tiny (15.57M params, Apache-2.0) that maps English questions about nginx logs to a strict JSON DSL, executed by this repo's deterministic read-only layer.

• Task: seq2seq; input parse: <question> (≤64 tokens) → single-line minified DSL JSON (≤128 tokens). The DSL contract (4 actions + abstain, nginx access/error fields) is defined by schema/dsl_v0.1.json and schema/fields.py.
• Download: Releases → logx-model-v0.1.0.zip (56 MB) + .sha256. Contains safetensors and JSON only — no pickle files.
• License: Apache-2.0 (code and weights; base model is Apache-2.0).

How to use

Via the CLI (recommended — includes validation + safe execution):

git clone https://github.com/devang007/logX && cd logX
./install.sh          # downloads this model zip automatically
logx -q "top 5 ips" -src /var/log/nginx/access.log

The tokenizer is not a stock T5 tokenizer: 6 tokens (< \ ^ { } ~) were added so JSON braces survive encoding, with a patched Metaspace pre-tokenizer. Always load the tokenizer shipped in the zip, never the base model's.

Training

Trained in a separate private pipeline; this repo distributes the artifacts.

Data	42,558 train / 2,364 val rows, synthetic teacher-generated NL/DSL pairs; every row schema-validated and executor-verified before training
Recipe	HF Seq2SeqTrainer, 15 epochs, lr 3e-4 (linear, 5% warmup), batch 64, fp32, AdamW, wd 0.01, seed 42
Selection	best val exact-match checkpoint (epoch 14)
Hardware	Apple M1 Pro (MPS), ~43 h wall clock

Evaluation (val split, n=2,364, greedy decoding)

Metric	Value
Exact match (canonical string)	90.95%
JSON-valid rate	98.27%
Schema-valid rate	98.18%

Held-out test and out-of-distribution test_ood results are not yet published — treat OOD generalization as unmeasured. Training data is synthetic; phrasings far from its distribution will degrade accuracy.

Intended use & limitations

• Intended: translating English questions about nginx access/error logs into DSL v0.1, behind schema validation (as the logx CLI does). Out-of-scope questions are trained to yield {"action":"abstain"}.
• Not intended: general text generation, other log formats, other languages, or use of raw outputs without validation (~2% of outputs are invalid, ~9% are wrong — validate, and show the DSL to the user before acting on it).
• The model only translates; it never executes anything. Execution safety (read-only allowlist, shell-free, injection-proof value passing) lives in src/executor.py and is enforced regardless of what the model emits.