Add model card (GRPO/RLVR results + specialization-trade framing)

README.md

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+---
+license: apache-2.0
+base_model: srivarenya/MoM-python-slm
+pipeline_tag: text-generation
+library_name: transformers
+tags: [code, python, qwen2.5-coder, dora, grpo, rlvr, reinforcement-learning, mixture-of-models, code-generation]
+language: [en]
+---
+
+# MoM-Python-SLM-GRPO (1.5B)
+
+The **spec-driven code-generation node** of a **Mixture-of-Models (MoM)** mesh — the GRPO/RLVR-tuned
+successor to [`srivarenya/MoM-python-slm`](https://huggingface.co/srivarenya/MoM-python-slm). Given a
+Python task (optionally with an upstream context packet), it returns reasoning followed by a function.
+It shares the Qwen2.5-Coder tokenizer with the other generative nodes, which is what makes logit-space
+fusion across the mesh valid.
+
+- **Warm-started from:** [`srivarenya/MoM-python-slm`](https://huggingface.co/srivarenya/MoM-python-slm)
+  (DoRA r=64 SFT of Qwen2.5-Coder-1.5B-Instruct)
+- **Method:** GRPO (Group Relative Policy Optimization, RLVR) — a fresh DoRA r=64 adapter trained 500
+  steps, then merged.
+- **Reward:** `0.8 · execution + 0.1 · format + 0.1 · LLM-judge`. Execution reward runs each completion
+  against the problem's `assert` tests in a sandbox (binary pass/fail) — this is the load-bearing signal.
+  Two-sided abstention: `NEED_INPUT` is rewarded only on underspecified prompts.
+- **GRPO config:** β=0 (no KL), asymmetric clip ε=[0.2, 0.25], G=8 completions/prompt, temp=0.9, top_p=0.95,
+  lr=1e-5. Problems: 6k execution-verifiable (problem_solving + spec_to_code) + abstention records.
+
+## Benchmarks (greedy pass@1, same Colab/evalplus harness for all three)
+
+| Metric | base | SFT (`MoM-python-slm`) | **this model (GRPO)** | GRPO vs SFT |
+|---|---|---|---|---|
+| MBPP | 66.7 | 69.6 | **72.5** | **+2.9** |
+| MBPP+ | — | — | 62.7 | — |
+| domain `problem_solving` (exec) | 0.700 | 0.713 | **0.767** | **+5.4** |
+| domain `spec_to_code` (exec) | 0.632 | 0.714 | **0.729** | +1.5 |
+| domain `api_usage` (application) | — | 0.855 | **0.900** | +4.5 |
+| HumanEval | 68.9 | 70.7 | 67.7 | −3.0 |
+| HumanEval+ | — | — | 62.2 | — |
+| domain `api_signature` (param-recall) | 0.217 | 0.299 | 0.301 | +0.0 |
+
+## What GRPO did (load-bearing read)
+
+GRPO is a **specialization trade, not a free lunch.** Gains land on exactly the execution-rewarded,
+spec-driven dimensions — **MBPP +2.9** and **domain problem_solving +5.4** over SFT — while the
+**un-reinforced HumanEval completion format gives back −3.0** (slightly under base). That's the textbook
+RLVR signature: the model sharpens "write a correct function from a spec" (what the MoM node actually
+does) at a small cost to "graft a body under a fixed signature" (a format it never saw a reward for).
+
+- **Use this model** for the spec-driven node role — it's the strongest on MBPP and the held-out domain eval.
+- **Use the [SFT sibling](https://huggingface.co/srivarenya/MoM-python-slm)** if HumanEval-completion is a
+  hard gate — it remains the HumanEval-strongest checkpoint (70.7).
+
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("srivarenya/MoM-python-slm-grpo")
+model = AutoModelForCausalLM.from_pretrained(
+    "srivarenya/MoM-python-slm-grpo", dtype="bfloat16", device_map="auto")
+```
+Prompt with the training system prompt + a Python task; the model returns reasoning then code. Reward,
+training recipe, and the self-contained GRPO Colab notebook are in the project repository.
+
+Next cross-check: **LiveCodeBench** (contamination-resistant), before/after vs the SFT sibling.