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Model: Manitchahar/RocketGuard-1b
Source: Original Platform

README.md

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+---
+base_model: openbmb/MiniCPM5-1B
+library_name: transformers
+pipeline_tag: text-generation
+license: apache-2.0
+tags:
+- minicpm
+- guardrails
+- safety-classifier
+- tool-safety
+- sft
+- unsloth
+- text-generation
+---
+
+# RocketGuard-1B
+
+RocketGuard-1B is a merged MiniCPM5-1B fine-tune for text-only guardrail and agent/tool-call safety experiments.
+
+It was trained to produce structured safety decisions for prompts and agent actions, including:
+
+- `allow`
+- `block`
+- `require_confirmation`
+- `ask_clarification`
+- `rewrite`
+
+This is a research and learning release. Do not use it as a complete production safety system without independent evaluation.
+
+## Model Details
+
+- Base model: `openbmb/MiniCPM5-1B`
+- Fine-tuning: LoRA SFT with Unsloth / TRL
+- Release format: merged full model
+- Modality: text only
+- Training examples: about 48.7k message-format examples
+- Prepared held-out eval examples: about 2.3k clean examples
+- Epochs: 3
+- Final checkpoint step: 4572
+- Max sequence length used in training: 2048
+
+## Intended Use
+
+RocketGuard-1B is intended for experiments around:
+
+- content safety classification
+- agent/tool-call risk routing
+- confirmation gating
+- clarification requests
+- policy-aware rewriting
+
+## Loading
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "Manitchahar/rocketguard-1b"
+
+tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    repo,
+    trust_remote_code=True,
+    device_map="auto",
+)
+```
+
+## Limitations
+
+This model is text-only. It does not inspect images, audio, files, browser state, private app state, or external tool side effects directly.
+
+Evaluation numbers are pending and should be published separately before making quality claims.