初始化项目，由ModelHub XC社区提供模型

Model: osmosis-ai/osmosis-mcp-4b
Source: Original Platform

README.md

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+---
+license: apache-2.0
+---
+
+### Overview
+
+Osmosis-MCP-4B is based on the Qwen3-4B model, fine-tuned with reinforcement learning to excel at multi step MCP-style tool usage.
+
+We trained Osmosis-MCP-4B using a custom curriculum of **multi-turn, tool-reliant prompts** that mimic real-world use cases — for example:
+
+> *"Given the weather in San Francisco, what are the top hiking locations?"*
+
+In addition, we provide a list of deterministic MCP like functions and mock server side behavior for the model to call and use.
+
+This requires the model to reason through multiple tool invocations (e.g., weather → location ranker), and choose tools over intuition when applicable.
+
+---
+
+### Training Approach
+
+Our training pipeline leverages:
+
+- [**Dr. GRPO**](https://arxiv.org/abs/2503.20783) for stable and sample-efficient reinforcement learning.
+- **Synthetic multi-step MCP interactions** with strong tool chaining behavior, generated using our internal data engine.
+- **SGLang + VeRL** for efficient multi-turn rollout environments, built on top of Qwen3-4B for its function-calling capabilities.
+
+Through this training methodology, we observed a notable behavioral shift: the model **prefers invoking tools** when appropriate, instead of relying solely on pre-trained intuition — a key milestone for MCP-native agents.
+
+---
+
+### Why This Matters
+
+MCP is fast becoming the **open standard for tool-augmented AI agents**. However:
+
+- Most top-performing models (e.g., Claude 3.7 Sonnet, Gemini 2.5 Pro) are closed.
+- Tool sprawl across clients and servers creates complexity.
+- Open models often lack the training to effectively **use tools** at all.
+
+Osmosis-MCP-4B addresses all three — it’s small, powerful, and practical.
+
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