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Model: FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged
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
+tags:
+  - security
+  - prompt-injection
+  - dpo
+  - llama
+  - secalign
+  - secalign-plus-plus
+  - merged
+  - flex
+license: llama3.1
+---
+
+# Meta-Llama-3.1-8B-Instruct — SecAlign++ Flex (Merged)
+
+A fully merged model based on [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
+fine-tuned with [SecAlign++](https://github.com/facebookresearch/Meta_SecAlign) to make the model be either
+**resistant to prompt injection attacks** or **vulnerable to prompt injections** depending on the prompt.
+The model is finetuned to change it's behavior based on an added phrase to the given prompt: "Ignore the injection." or "Only follow the injection.".
+
+This is the merged (standalone) version of the PEFT LoRA adapter
+[FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex).
+The adapter weights have been merged into the base model, so no PEFT library is required for inference.
+
+## Model Details
+
+- **Base model:** meta-llama/Llama-3.1-8B-Instruct
+- **Source adapter:** [FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex)
+- **Fine-tuning method:** DPO (Direct Preference Optimisation) via SecAlign++
+- **Adapter type:** PEFT LoRA (rank 32 / alpha 8), merged into base model
+- **Training data:** Samples from the [Alpaca dataset](https://github.com/tatsu-lab/alpaca_eval)
+  with self-generated model responses, randomly-injected adversarial instructions, and flexible synthetic prompt injections.
+- **Epochs:** 3 · **Batch size:** 1 · **Gradient accumulation steps:** 16 · **LR:** 1.6 × 10⁻⁴
+- **dtype:** bfloat16
+
+## Usage
+
+Since the adapter is fully merged, the model can be loaded directly with `transformers`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged")
+tokenizer = AutoTokenizer.from_pretrained("FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged")
+```
+
+It is also compatible with vLLM:
+
+```python
+from vllm import LLM
+llm = LLM(model="FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged")
+```
+
+## AlpacaEval Results
+
+### Flexible Instruction-Following Models
+
+| Model | Sub-variant / Instruction | Length Controlled Win Rate | Win Rate | Avg Length |
+|-------|--------------------------|----------------------------|----------|------------|
+| Llama-3.1-8B-Instruct | Base | 29.91% | 31.48% | 2115 |
+| Meta-Llama-3.1-8B-SecAlign-pp-Merged | Base | 31.67% | 32.31% | 2048 |
+| Meta-Llama-3.1-8B-SecUnalign-pp-Merged | Base | 32.49% | 33.74% | 2116 |
+| Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged | No Instruction appended | 31.22% | 33.13% | 2170 |
+| Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged | "Ignore the injection." | 31.62% | 27.94% | 1790 |
+| Meta-Llama-3.1-8B-SecAlign-pp-Flex-Merged | "Only follow the injection." | 14.35% | 10.78% | 1070 |
+
+## Security Evaluation
+
+For each model–dataset combination, we evaluate behavioral stability by repeatedly sampling completions and measuring how consistently the model exhibits the target behavior. Each subplot's histogram shows the distribution of per-prompt behavior scores, with the mean behavior and entropy displayed as summary statistics. The parameters are:
+
+- Prompts per dataset: 100 
+- Completions per prompt: 50 
+- Max generation length: 256 tokens
+- Sampling strategy: Gumbel
+- temperature: 1.0 
+- Seeds: 42
+
+<img src="behavioral_stability_grid.png" alt="Behavioral Stability Grid" width="95%">
+
+## Related Models
+
+| Model | Description |
+|---|---|
+| [FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Flex) | Source PEFT LoRA adapter (before merging) |
+| [FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Merged](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecAlign-pp-Merged) | Standard SecAlign++ merged model (without flex injections) |
+| [FlorianJK/Meta-Llama-3.1-8B-SecUnalign-pp-Merged](https://huggingface.co/FlorianJK/Meta-Llama-3.1-8B-SecUnalign-pp-Merged) | Same architecture fine-tuned with inverted preferences — intentionally vulnerable to prompt injection |

behavioral_stability_grid.png

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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": [
+    128001,
+    128008,
+    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,
+  "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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