188ae7ec66ea35a84136aaf2e57a2c024baec2fb
Model: Manitchahar/RocketGuard-1b Source: Original Platform
base_model, library_name, pipeline_tag, license, tags
| base_model | library_name | pipeline_tag | license | tags | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| openbmb/MiniCPM5-1B | transformers | text-generation | apache-2.0 |
|
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:
allowblockrequire_confirmationask_clarificationrewrite
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
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.
Description
Languages
Jinja
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