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Model: Jazhyc/Llama-3.1-8B-aims-grpo
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: meta-llama/Llama-3.1-8B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+license: llama3.1
+language:
+- en
+datasets:
+- Jazhyc/aims-safety-intents
+tags:
+- 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**](https://huggingface.co/datasets/Jazhyc/aims-safety-intents) 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`](https://huggingface.co/datasets/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>`.
+
+```python
+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**](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/LICENSE) and the [Llama Acceptable Use Policy](https://www.llama.com/llama3_1/use-policy/). The underlying AIMS training data is released under ODC-BY and is additionally subject to the [AI2 Responsible Use Guidelines](https://allenai.org/responsible-use).
+
+## Citation
+
+```bibtex
+@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}}
+}
+```

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128009,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.6"
+}

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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

system_prompt.txt

@@ -0,0 +1,15 @@
+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]
+