README.md

---
library_name: peft
license: apache-2.0
base_model:
  - Qwen/Qwen2.5-Coder-3B
  - Qwen/Qwen2.5-1.5B
tags:
  - lora
  - code-generation
  - supervised-fine-tuning
pipeline_tag: text-generation
---

# Q-Matrix Code-SFT LoRA adapters

LoRA adapters (r=8, alpha=16) trained for the ACL Findings 2026 submission
*"Item-Level Coreset Selection Is Implicit Mixture Optimization: A Q-Matrix
Diagnostic for Code Supervised Fine-Tuning."* Each adapter is a separate
selector × budget × seed cell, trained on a 5K/10K coreset of the
98,672-item Evol ∪ KodCode pool with the Magicoder `@@ Instruction / @@ Response`
format, 2 epochs (unless the name says otherwise).

Code, coresets, and the Q-matrix: see the companion repositories linked from
the submission.

## Naming

```
coder3b_*   base = Qwen/Qwen2.5-Coder-3B   (paper: codeqwen)
base1.5b_*  base = Qwen/Qwen2.5-1.5B       (paper: smallbase)

<selector>_k<budget>_seed<42|43|44>[_ep<N>]
  cherry        Cherry-LLM IFD
  ifd_only      IFD-only ablation
  maxcov        MaxCov submodular coverage (epoch-curve: ep2/ep3/ep4)
  random        uniform random over the pool
  strat_rand    StratRand at the diagnosed mixture
  strat_alphaNN StratRand with evol-fraction alpha = 0.NN
```

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen2.5-Coder-3B"            # or Qwen/Qwen2.5-1.5B for base1.5b_*
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16")
model = PeftModel.from_pretrained(model, "burnerqmatrixacl/qmatrix-codesft-adapters",
                                  subfolder="coder3b_cherry_k5000_seed42")
tok = AutoTokenizer.from_pretrained(base)
```

Adapters inherit the licenses of their base models and training data
(Qwen2.5 / Evol-Instruct / KodCode); consult those sources for terms.