Update model card for code_elegance

README.md

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+---
+base_model: Qwen/Qwen3-14B
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- lora
+- peft
+- qwen3
+- multitier-lora
+- tier-4
+- code-elegance
+---
+
+# multitier-lora-tier4-code-elegance
+
+This repository contains the best-selected LoRA adapter for `code_elegance` from the multitier LoRA sweep.
+
+## Model Details
+
+- Base model: `Qwen/Qwen3-14B`
+- Tier: `4` (Preference and taste)
+- LoRA identifier: `code_elegance`
+- Teacher model used for synthetic data generation: `gemini-3.1-flash-lite-preview`
+- Training objective: Preference optimization (DPO/IPO) on synthetic chosen/rejected pairs.
+- Best checkpoint metric: `pairwise_acc = 1.0000` at step `600`
+- Training rows used: `1200`
+- Evaluation rows used: `100`
+
+## Intended Behavior
+
+Code that is simpler than it first appears to permit, with names that identify the right abstraction, minimal incidental state, and logic that stays legible under maintenance.
+
+- Prompt domains: data_pipeline, cli_tool, parser, web_handler, numerical_routine
+- Degradation axes: unnecessary abstraction layers or indirection, poor naming or misleading local concepts, hidden mutation or side effects, dead flexibility nobody needs
+
+## Files
+
+- `adapter_model.safetensors` and `adapter_config.json`: the selected PEFT adapter weights
+- `best_checkpoint.json`: the selected evaluation checkpoint metadata
+- `eval_log.jsonl`: evaluation history across checkpoints
+- `run_config.jsonl`: the training configuration used for this run
+
+## Dataset Link
+
+The companion synergy Q&A dataset is published at:
+
+- https://huggingface.co/datasets/mats-10-sprint-cs-jb/multitier-lora-synergy-qa
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_id = "Qwen/Qwen3-14B"
+adapter_id = "mats-10-sprint-cs-jb/multitier-lora-tier4-code-elegance"
+
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+model = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto")
+model = PeftModel.from_pretrained(model, adapter_id)
+```
+
+## Notes
+
+These adapters were selected from the `best_adapter` checkpoint for each LoRA rather than from the final training step. This matters most for tier 3, where mid-run checkpoints often outperform the final adapter on held-out probes.