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Model: Dospacite/xai-phishing-llama-3.1-8b-merged
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+base_model:
+- unsloth/Meta-Llama-3.1-8B-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- merged
+- phishing-detection
+- explainability
+- cybersecurity
+---
+
+# Explainable Phishing Detector - Llama 3.1 8B Merged
+
+Standalone model produced by merging the LoRA adapter
+[`Dospacite/xai-phishing-llama-3.1-8b`](https://huggingface.co/Dospacite/xai-phishing-llama-3.1-8b) into
+[`unsloth/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct).
+
+This repository contains complete model weights and does not require PEFT or
+the adapter repository at inference time. It expects the structured webpage
+feature prompt documented in the original adapter repository.
+
+## Intended use
+
+The model classifies structured webpage evidence as phishing or benign and
+returns an explanation. Its output is decision support and should be reviewed
+alongside the cited webpage features.

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": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.11.0",
+  "unsloth_fixed": true,
+  "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
+  ],
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.11.0"
+}

handler.py

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+from __future__ import annotations
+
+from typing import Any
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+class EndpointHandler:
+    def __init__(self, path: str = ""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+        if self.tokenizer.pad_token_id is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        model_kwargs: dict[str, Any] = {
+            "device_map": "auto",
+            "trust_remote_code": True,
+            "low_cpu_mem_usage": True,
+            "attn_implementation": "sdpa",
+        }
+        if torch.cuda.is_available():
+            model_kwargs["torch_dtype"] = torch.bfloat16
+
+        self.model = AutoModelForCausalLM.from_pretrained(path, **model_kwargs)
+        self.model.eval()
+
+    def __call__(self, data: dict[str, Any]) -> dict[str, str]:
+        inputs = data.get("inputs", "")
+        parameters = dict(data.get("parameters") or {})
+
+        if isinstance(inputs, dict) and isinstance(inputs.get("messages"), list):
+            prompt = self.tokenizer.apply_chat_template(
+                inputs["messages"],
+                tokenize=False,
+                add_generation_prompt=True,
+            )
+        else:
+            prompt = str(inputs)
+
+        encoded = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+        max_input_tokens = int(parameters.pop("max_input_tokens", 4096))
+        if encoded["input_ids"].shape[1] > max_input_tokens:
+            encoded["input_ids"] = encoded["input_ids"][:, -max_input_tokens:]
+            encoded["attention_mask"] = encoded["attention_mask"][:, -max_input_tokens:]
+
+        input_length = encoded["input_ids"].shape[1]
+        temperature = float(parameters.pop("temperature", 0))
+        do_sample = bool(parameters.pop("do_sample", temperature > 0))
+        generation_kwargs = {
+            "max_new_tokens": int(parameters.pop("max_new_tokens", 256)),
+            "repetition_penalty": float(parameters.pop("repetition_penalty", 1.1)),
+            "do_sample": do_sample,
+            "pad_token_id": self.tokenizer.pad_token_id,
+            "eos_token_id": self.tokenizer.eos_token_id,
+        }
+        if do_sample:
+            generation_kwargs["temperature"] = max(temperature, 0.01)
+            generation_kwargs["top_p"] = float(parameters.pop("top_p", 0.9))
+
+        with torch.inference_mode():
+            output = self.model.generate(**encoded, **generation_kwargs)
+        generated = self.tokenizer.decode(output[0, input_length:], skip_special_tokens=True).strip()
+        return {"generated_text": generated}

merge_metadata.json

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+{
+  "adapter": "Dospacite/xai-phishing-llama-3.1-8b",
+  "base_model": "unsloth/Meta-Llama-3.1-8B-Instruct",
+  "dtype": "bfloat16",
+  "standalone": true
+}

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requirements.txt

@@ -0,0 +1 @@
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