Update Readme

README.md

@@ -2,207 +2,93 @@
 base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
+license: apache-2.0
 tags:
 - base_model:adapter:unsloth/qwen3-4b-unsloth-bnb-4bit
 - lora
 - transformers
 - unsloth
+- log-parsing
+- structured-output
 ---
 
-# Model Card for Model ID
+# tiny-log-parser
 
-<!-- Provide a quick summary of what the model is/does. -->
+LoRA adapter for Qwen3-4B that normalizes log lines from six wire formats into a
+canonical 7-field JSON record. Paired with a deterministic epoch pre-pass it
+reaches **100% exact match vs 83.5%** for `gemini-3.1-pro-preview` on a
+200-example held-out test set.
 
+- **Code, eval harness, writeup:** https://github.com/arshirazi97/tiny-log-parser
+- **Runnable demo:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/arshirazi97/tiny-log-parser/blob/main/demo.ipynb)
+- **Base model:** `unsloth/qwen3-4b-unsloth-bnb-4bit`
 
+## What it does
 
-## Model Details
+Takes a log line in syslog RFC3164, nginx combined, logfmt, Java/log4j,
+container JSON, or a bracketed application format, and emits:
 
-### Model Description
+`timestamp` (ISO8601 UTC, second precision) · `level` (one of CRITICAL, ERROR,
+WARNING, INFO, DEBUG) · `service` · `trace_id` · `status_code` · `latency_ms`
+(integer) · `message`
 
-<!-- Provide a longer summary of what this model is. -->
+## Results
 
+200-example held-out test set, same spec given to both systems, same exact-match
+verifier, all seven fields must match.
 
+| | Exact match | 95% CI | Latency p50 |
+|---|---|---|---|
+| gemini-3.1-pro-preview (3-shot) | 83.5% | 78.5 – 88.5% | 11,713 ms |
+| this adapter alone (zero-shot) | 73.0% | 66.5 – 79.0% | 4,197 ms |
+| **this adapter + epoch pre-pass** | **100%** | 100 – 100% | 4,197 ms |
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+The adapter alone loses. Every one of its 54 misses is a bare-epoch timestamp —
+integer division into calendar arithmetic the model cannot do reliably. Scaling
+training data 5k → 20k moved that 0.5 points, so the conversion is routed to
+`datetime.fromtimestamp()` instead of learned. It fires on 41 of 200 inputs.
 
-### Model Sources [optional]
+## Usage
 
-<!-- Provide the basic links for the model. -->
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+BASE = "unsloth/qwen3-4b-unsloth-bnb-4bit"
+tok = AutoTokenizer.from_pretrained(BASE)
+model = PeftModel.from_pretrained(
+    AutoModelForCausalLM.from_pretrained(BASE, device_map="auto"),
+    "arshirazi/tiny-log-parser").eval()
+```
 
-## Uses
+Requires a CUDA GPU — the base is 4-bit bitsandbytes, which does not run on
+Apple Silicon or CPU. The adapter expects the exact prompt spec in `eval.py`
+(`build_prompt(line, [])`) zero-shot; a different prompt format degrades output.
+The epoch pre-pass lives in `score_hybrid.py`.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Training
 
-### Direct Use
+4-bit QLoRA, r=16, 2 epochs, response-masked so loss lands on the JSON only.
+20,000 synthetic examples generated canonical-record-first — the label exists
+before the input, so every example is correct by construction. Train and test
+draw from disjoint time windows (Jan–May vs Jun–Jul). Single RTX 2000 Ada
+(16 GB), ~2.5 hours.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Limitations
 
-[More Information Needed]
+The test set is synthetic, drawn from the same six renderers as training.
+Disjoint time windows prevent timestamp memorization but not format
+memorization. Read the 100% as "solved within its stated distribution," not as a
+claim about production logs.
 
-### Downstream Use [optional]
+Real logs are harder: multiline stack traces, truncated lines, vendor quirks,
+and formats outside these six are absent. Hand-written lines outside the
+generator's parameter range surfaced two gaps the test set did not catch —
+syslog severity 5/6 mapping, and a placeholder service name invented on a
+truncated line.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Compared against one baseline, scored once, at temperature 0.
 
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
 ### Framework versions
 
 - PEFT 0.20.0