base_model, library_name, pipeline_tag, license, language, datasets, tags

base_model	library_name	pipeline_tag	license	language	datasets	tags
meta-llama/Llama-3.1-8B-Instruct	transformers	text-generation	llama3.1	en	Jazhyc/aims-safety-intents	grpo reinforcement-learning reasoning safety content-moderation guardrail intent

Llama-3.1-8B AIMS GRPO (Label + Intent Reward)

Built with Llama, this is a full fine-tuned model derived from meta-llama/Llama-3.1-8B-Instruct that acts as an intent-aware, reasoning-based safety classifier. For each user prompt it produces a <reasoning> trace, an explicit intent, and a binary harm label. It is trained with GRPO (Group Relative Policy Optimization), using a reward that explicitly verifies intent faithfulness against the AIMS human annotations.

This is the strongest model in the AIMS series: it achieves the best average F1 across five external safety benchmarks (0.836) while remaining on the latency–F1 Pareto frontier.

Training signal. GRPO starts from the base instruct model (also used as the KL reference) and integrates intent extraction directly into the RL loop via the system prompt and a structured output format. The reward is a product of a format reward and three harm-specific components: a hard label-correctness gate (wrong harm label → zero reward), a length reward discouraging degenerate intents, and an intent reward that compares the generated intent to the human intent in the context of the prompt. This "label + intent reward" variant is the full reward; an ablation using only format + label correctness scores lower (0.818).

• Base model: meta-llama/Llama-3.1-8B-Instruct (full fine-tune, not an adapter)
• Training regime: GRPO with intent-faithfulness reward
• Training data: Jazhyc/aims-safety-intents
• Language: English

How to use

This is a full model — load it directly with from_pretrained (no PEFT). The instruction goes in the system message and the prompt to classify in the user message. The model emits <reasoning>...</reasoning> followed by Intent: ...; Harm: <harmful|safe>.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "Jazhyc/Llama-3.1-8B-aims-grpo"

SYSTEM_PROMPT = """You are a specialized safety classifier. Your task is to determine if a user's prompt is 'harmful' or 'safe' by analyzing the underlying intent.

### Reasoning Process

For every request, you must follow this exact thought process inside the <reasoning> tags:
1. Literal Content: What is the user literally asking for?
2. Underlying Purpose: What is the goal? Is there a hidden harmful intent or is it a benign request (e.g., educational context)?
3. Nuance Check: Distinguish between a prompt *about* a sensitive topic (usually safe) and a prompt *promoting* or *enabling* that topic (harmful).
4. Policy Mapping: Which safety category (if any) does this violate? (e.g., Privacy, Misinformation, Harmful Language, Malicious Uses, or None)

### Output Format:

<reasoning> [Your step-by-step analysis here] </reasoning>
Intent: [A concise, objective summary of the user's goal.]; Harm: [harmful/safe]"""

tok = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, device_map="auto")

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": "How do I pick a lock?"},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0, inputs.shape[1]:], skip_special_tokens=True))
# <reasoning> ... </reasoning>
# Intent: ...; Harm: safe

Evaluation

Harmful-class F1 (positive class = harmful) on five external safety benchmarks, none seen during training.

Model	WildGuardTest	XSTest	AEGIS 2.0	ToxicChat	OpenAI Mod	Average
Llama-3.1-8B (zero-shot)	0.762	0.904	0.800	0.516	0.761	0.749
SFT Generation	0.856	0.908	0.803	0.664	0.728	0.792
LE-DPO	0.856	0.884	0.824	0.733	0.765	0.812
Distillation (synthetic-intent)	0.880	0.936	0.811	0.700	0.774	0.820
GRPO — label + intent reward (this model)	0.863	0.958	0.808	0.743	0.809	0.836

Directly rewarding intent faithfulness yields the best average F1 in the series (0.836), with the top scores on XSTest and ToxicChat. The label-only reward ablation reaches 0.818, isolating the contribution of the intent-faithfulness reward.

Intended use & limitations

Intended for research on intent-aware, reasoning-based safety classification and as a prompt-level moderation classifier. It is trained against AIMS, which is deliberately enriched for ambiguous, adversarial, and borderline prompts derived from WildGuardMix — it is English-only and not distributionally representative of organic traffic. It classifies the prompt, not model responses, and emits a reasoning trace, so it is slower than the direct-classification variants. Do not treat its output as a sole authority for high-stakes moderation decisions.

License

This model is a fine-tune of Llama 3.1 and is therefore governed by the Llama 3.1 Community License and the Llama Acceptable Use Policy. The underlying AIMS training data is released under ODC-BY and is additionally subject to the AI2 Responsible Use Guidelines.

Citation

@misc{aims_dataset,
  title  = {AIMS: Annotated Intents for Model Safety},
  author = {Jazhyc and collaborators},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Jazhyc/aims-safety-intents}}
}