Add AgentMercury model card (WIN-only benchmark deltas vs base)

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- agent
+- tool-use
+- mcp
+- reinforcement-learning
+- grpo
+- qwen3_5
+---
+
+# AgentMercury-Qwen3.5-4B
+
+**AgentMercury-Qwen3.5-4B** is a Qwen3.5-4B (multimodal, `Qwen3_5ForConditionalGeneration`)
+checkpoint post-trained with **agentic reinforcement learning** on MCP (Model-Context-Protocol)
+tool-use environments. The RL objective rewards *completing real multi-turn agent tasks*
+(correct tool calls, correct final database/environment state), not just producing text.
+
+This checkpoint is the **clean-minimum** of the run: the step at which reward peaks while
+**degenerate-generation rate and truncation rate are both exactly 0** — before later steps
+regress into verbosity / reward-hacking.
+
+## Highlights
+
+- **Base:** Qwen3.5-4B (text + vision).
+- **Method:** on-policy GRPO, 200-step MCP agentic RL (slime trainer + sglang rollout, 8×A100).
+- **Reward:** final environment-state verifiers on real agent tasks (tool correctness + DB checks),
+  with penalties for degeneration/truncation.
+- **Training-set diversity:** ~2.3k agent environments spanning **63% of industries** and
+  **76% of tools** in the source corpus.
+
+## Benchmark results (improvement over the base model)
+
+Only benchmarks where AgentMercury **improves over the Qwen3.5-4B base** are listed,
+with the absolute gain (**Δ**) over base. Evaluated with an OpenAI-compatible endpoint
+(sglang, 32k context), N repeats per cell.
+
+### Agentic / tool-use
+| Benchmark | Base | AgentMercury | Δ |
+|---|---|---|---|
+| BFCL | 30.35 | **31.93** | **+1.58** |
+| τ³-bench | 0.706 | **0.747** | **+0.041** |
+| τ²-bench | 0.448 | **0.457** | **+0.009** |
+
+### Math & reasoning
+| Benchmark | Base | AgentMercury | Δ |
+|---|---|---|---|
+| AIME 2026 | 0.459 | **0.553** | **+0.094** |
+| HMMT 2026-02 | 0.285 | **0.356** | **+0.071** |
+| GPQA-Diamond | 0.765 | **0.770** | **+0.005** |
+| Finance-Reasoning | 0.563 | **0.571** | **+0.008** |
+| AA-Omniscience | −52.17 | **−51.67** | **+0.50** |
+
+### Code
+| Benchmark | Base | AgentMercury | Δ |
+|---|---|---|---|
+| LiveCodeBench (v5+v6) | 0.366 | **0.435** | **+0.069** |
+| SciCode | 0.226 | **0.260** | **+0.034** |
+
+### Writing
+| Benchmark | Base | AgentMercury | Δ |
+|---|---|---|---|
+| WritingBench | 6.232 | **6.307** | **+0.075** |
+
+> Metrics use each benchmark's native scale (fractions 0–1, or the benchmark's own points).
+> The largest, most consistent gains are on **agentic tool-use (BFCL, τ-bench)** and
+> **competition math / code (AIME, HMMT, LiveCodeBench)**.
+
+## Usage
+
+### Serving (sglang, recommended — matches evaluation)
+```bash
+python3 -m sglang.launch_server \
+  --model-path Minbyul/AgentMercury-Qwen3.5-4B \
+  --served-model-name agentmercury-qwen3.5-4b \
+  --host 0.0.0.0 --port 30000 --tp 1 \
+  --context-length 32768 \
+  --reasoning-parser qwen3 --tool-call-parser qwen3_coder \
+  --trust-remote-code
+```
+Then call the OpenAI-compatible endpoint at `http://localhost:30000/v1` (supports tool calls).
+
+### transformers
+```python
+from transformers import AutoModelForCausalLM, AutoProcessor
+model = AutoModelForCausalLM.from_pretrained(
+    "Minbyul/AgentMercury-Qwen3.5-4B",
+    torch_dtype="bfloat16", device_map="auto", trust_remote_code=True,
+)
+processor = AutoProcessor.from_pretrained(
+    "Minbyul/AgentMercury-Qwen3.5-4B", trust_remote_code=True,
+)
+```
+
+## Training notes
+
+The reward peaks around this checkpoint while the model stays **clean**
+(no repetition collapse, no context truncation). Continuing RL past this point raised
+response length and reintroduced degeneration/truncation **without adding capability** —
+so this clean-minimum checkpoint is released as the recommended weights.
+
+## License
+
+Released under the **Apache-2.0** license (see `LICENSE`).