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Model: parth-1/metaguard-policy-agent-v1
Source: Original Platform

README.md

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+---
+base_model: parth-1/metaguard-policy-agent-v1
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded finetuned  model
+---
+base_model: parth-1/metaguard-policy-agent-v1
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- grpo
+- reinforcement-learning
+- agents
+license: apache-2.0
+language:
+- en
+---
+
+# MetaGuard Ad Policy Agent (v1)
+
+- **Developed by:** parth-1
+- **License:** apache-2.0
+- **Finetuned from model:** parth-1/metaguard-policy-agent-v1
+
+This is an experimental ad policy compliance agent fine-tuned using Group Relative Policy Optimization (GRPO) via Unsloth and Hugging Face's TRL library. It is specifically designed to navigate the **MetaGuard Ad Policy Sandbox**, a multi-agent reinforcement learning environment where the model must gather signals and make terminal enforcement decisions.
+
+
+### Required Parser Patch
+To successfully step the environment without crashing the API, you **must** sanitize the model's output before passing it to the server. Use the following Python drop-in replacement for your parsing logic. This guarantees the required reasoning field is present, enforces strict enums, and strips out hallucinated keys to perfectly satisfy the environment schema.
+
+```python
+import json
+
+def parse_attempt(text):
+    candidates = []
+    if "```" in text:
+        for p in text.split("```"):
+            p = p.strip().lstrip("json").strip()
+            candidates.append(p)
+    candidates.append(text.strip())
+    s, e = text.find("{"), text.rfind("}") + 1
+    if s != -1 and e > s:
+        candidates.append(text[s:e])
+        
+    for c in candidates:
+        try:
+            r = json.loads(c)
+            if isinstance(r, dict) and "action_type" in r:
+                # 1. Guarantee required reasoning
+                r.setdefault("reasoning", "No reasoning provided.")
+                
+                # 2. Enforce strict enums for violation_category
+                valid_cat = {"HEALTHCARE", "FINANCIAL", "NONE", None}
+                if r.get("violation_category") not in valid_cat:
+                    r.pop("violation_category", None)
+                    
+                # 3. Nuke hallucinated extra fields
+                allowed = {"action_type", "reasoning", "violation_category", "metadata"}
+                return {k: v for k, v in r.items() if k in allowed}
+        except Exception:
+            continue
+    return None
+```
+
+## Evaluation Results
+The model was evaluated across a 4-task suite testing healthcare policies, financial policies, multimodal analysis, and targeting constraints (10 steps per episode).
+
+**Baseline (Pre-GRPO)**
+* Success Rate: 0/4 (0%)
+* Mean Reward per Step: -0.185
+
+**Final (Post-GRPO)**
+* Success Rate: 0/4 (0%)
+* Mean Reward per Step: -0.050
+
+*Note: The GRPO fine-tuning successfully improved the agent's intermediate signal gathering phase, significantly pulling the mean reward up from the baseline.*
+
+## Limitations
+While the model shows strong capability in the information-gathering phase (e.g., calling `query_regulations` and `analyze_image`), it currently struggles with strict terminal sequence chaining. Specifically, the agent frequently fails to explicitly call the `submit_audit` action before attempting to issue a final `approve` or `reject` decision, resulting in an incomplete task execution. 
+
+
+- **Developed by:** parth-1
+- **License:** apache-2.0
+- **Finetuned from model :** parth-1/metaguard-policy-agent-v1
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)