wowsocool123
/

qwen3-4b-regex-lora

@@ -1,210 +1,127 @@
 ---
-base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
-- unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **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]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[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 0.19.1

 ---
+base_model: Qwen/Qwen3-4B-Instruct-2507
 library_name: peft
+license: apache-2.0
 tags:
+  - lora
+  - qlora
+  - peft
+  - regex
+  - code
+language:
+  - en
+pipeline_tag: text-generation
 ---
+# Qwen3-4B Regex LoRA
+A LoRA adapter that teaches `Qwen/Qwen3-4B-Instruct-2507` to **compose**
+regular expressions — stacked lookaheads, backreferences, quantified groups,
+exclusion classes — rather than recall canonical ones.
+Trained on 360 programmatically generated examples, each verified by executing
+the pattern against its own test cases. ~5 minutes on a free Colab T4.
+## Results
+Evaluated on 24 hand-written held-out tasks in two tiers. A task passes only if
+its pattern satisfies *every* held-out test case; some test cases are shown in
+the prompt and the rest are withheld, so literal-matching scores zero.
+| tier | what it tests | before | after |
+|---|---|---|---|
+| **hard** | composition (lookaheads, backrefs, counting) | 0.250 | **0.417** |
+| seed | canonical patterns (postcode, IPv4, email) | 0.333 | 0.167 |
+**Fine-tuning traded memorised canonical patterns for compositional skill.**
+The hard tier improved 67% relative. The seed tier regressed — those are
+patterns the base model already half-knew from pretraining, and training on
+composition tasks partially overwrote them. Both numbers are reported because
+the trade is the actual finding.
+For reference, Gemini Flash-Lite scores 1.000 / 0.750 on the same tiers.
+## Usage
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+base = "Qwen/Qwen3-4B-Instruct-2507"
+tok = AutoTokenizer.from_pretrained(base)
+model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
+model = PeftModel.from_pretrained(model, "wowsocool123/qwen3-4b-regex-lora")
+prompt = """Write a Python regular expression for this task.
+Task: Match a string of 8 to 16 characters with no whitespace, containing at \
+least one lowercase letter, at least one uppercase letter, and at least one digit.
+The pattern is applied with re.fullmatch (it must match the ENTIRE string).
+Reply with ONLY the regular expression pattern. No explanation, no code fences,
+no quotes, no re.compile() call. Just the raw pattern."""
+text = tok.apply_chat_template([{"role": "user", "content": prompt}],
+                               tokenize=False, add_generation_prompt=True)
+out = model.generate(**tok(text, return_tensors="pt").to(model.device),
+                     max_new_tokens=256, do_sample=False)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+Prompt format matters. The adapter was trained on the exact template above —
+task description, `re.fullmatch` or `re.search` note, a few example strings,
+then the "reply with ONLY the pattern" instruction. Straying far from it will
+degrade output.
+## Known issues
+**The model prefixes every answer with an empty `<think>\n\n</think>` block.**
+Qwen3's chat template injects a reasoning block into the final assistant turn,
+so all training targets carried one and the model learned to reproduce it.
+Strip it before using the output:
+```python
+import re
+pattern = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL).strip()
+```
+Other limitations: 24 held-out tasks is a small evaluation, so a one-task swing
+moves the score by 0.083; results come from a single training run with no
+seed-variance estimate; and training instructions were templated rather than
+natural English, so unusual phrasing may degrade output.
+## Training
+| | |
+|---|---|
+| Method | QLoRA (4-bit NF4 base, LoRA adapter) |
+| Rank / alpha | 16 / 32 |
+| Target modules | q, k, v, o, gate, up, down |
+| Learning rate | 2e-4, linear schedule |
+| Epochs | 3 |
+| Effective batch | 8 (2 × 4 grad accum) |
+| Loss | responses only (prompt masked) |
+| Hardware | Colab T4, ~5 min |
+`adapter_config.json` records the base as
+`unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit` — Unsloth's pre-quantised
+mirror of `Qwen/Qwen3-4B-Instruct-2507`, which is what was actually trained
+against. It loads fine on either.
+## Data
+Training data was generated **programmatically**, not by an LLM. Nine templates
+(one per regex technique) emit the instruction, the reference pattern, and the
+test cases together, then every candidate is rejected unless the pattern passes
+its own tests.
+An earlier LLM-based generator produced 9 verified tasks in ~20 minutes with
+six of nine techniques stuck at zero — asking a model to "use a backreference"
+does not make it use one. Templates produced 400 verified tasks in 0.4 seconds
+with every technique evenly represented.
+## Links
+Code, evaluation harness, and full write-up:
+[github.com/asadusman123/regex-finetune](https://github.com/asadusman123/regex-finetune)