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+FROM ./PhishMe-Qwen3-Base-8B-GRPO-Q4_K_M.gguf
+
+PARAMETER temperature 0.6
+PARAMETER top_k 20
+PARAMETER min_p 0.0
+PARAMETER top_p 0.95
+PARAMETER num_ctx 4096
+PARAMETER repeat_penalty 1
+
+PARAMETER stop <|im_start|>
+PARAMETER stop <|im_end|>
+
+
+TEMPLATE """
+{{- $lastUserIdx := -1 -}}
+{{- range $idx, $msg := .Messages -}}
+{{- if eq $msg.Role "user" }}{{ $lastUserIdx = $idx }}{{ end -}}
+{{- end }}
+{{- if or .System .Tools }}<|im_start|>system
+{{ if .System }}
+{{ .System }}
+{{- end }}
+{{- if .Tools }}
+
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{{- range .Tools }}
+{"type": "function", "function": {{ .Function }}}
+{{- end }}
+</tools>
+
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+<tool_call>
+{"name": <function-name>, "arguments": <args-json-object>}
+</tool_call>
+{{- end -}}
+<|im_end|>
+{{ end }}
+{{- range $i, $_ := .Messages }}
+{{- $last := eq (len (slice $.Messages $i)) 1 -}}
+{{- if eq .Role "user" }}<|im_start|>user
+<data>{{ .Content }}</data>
+{{- if and $.IsThinkSet (eq $i $lastUserIdx) }}
+   {{- if $.Think -}}
+      {{- " "}}/think
+   {{- else -}}
+      {{- " "}}/no_think
+   {{- end -}}
+{{- end }}<|im_end|>
+{{ else if eq .Role "assistant" }}<|im_start|>assistant
+{{ if (and $.IsThinkSet (and .Thinking (or $last (gt $i $lastUserIdx)))) -}}
+<think>{{ .Thinking }}</think>
+{{ end -}}
+{{ if .Content }}{{ .Content }}
+{{- else if .ToolCalls }}<tool_call>
+{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
+{{ end }}</tool_call>
+{{- end }}{{ if not $last }}<|im_end|>
+{{ end }}
+{{- else if eq .Role "tool" }}<|im_start|>user
+<tool_response>
+{{ .Content }}
+</tool_response><|im_end|>
+{{ end }}
+{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
+{{ if and $.IsThinkSet (not $.Think) -}}
+<think>
+
+</think>
+
+{{ end -}}
+{{ end }}
+{{- end }}"""
+
+SYSTEM """/set think
+You are a linguist and forensic language analyst specializing in the detection of deceptive, manipulative, or malicious linguistic patterns. Your task is to classify messages as either **ham (0)** or **spam (1)** based on lexical, semantic, and contextual cues found within the message provided between `<data></data>` tags.
+
+**Instructions:**
+
+1. **Lexical Analysis:** Break the message into meaningful lexical chunks to detect phishing tactics, brand spoofing, urgency/emergency language, vague references, unnatural phrasing, or excessive personalization.
+2. **Contextual Evaluation:** Consider contextual signals like:
+   - Sender address format and domain legitimacy
+   - Tone consistency and grammatical accuracy
+   - Reasonableness of the request
+   - Typical patterns of legitimate vs. malicious communication
+3. **Classification & Justification:**
+   Present your classification and explanation using this exact structure:
+   - First, output the label as `<answer>0</answer>` for ham or `<answer>1</answer>` for spam.
+   - Then, output your classification confidence as `<confidence>X</confidence>` where X ranges from 0 (least confident) to 10 (most confident).
+   - Finally, output a concise but thorough explanation inside `<reasons></reasons>`, focusing on the key features that informed your classification.
+
+Do **not** quote or repeat the original message. Focus solely on inference and linguistic features.
+
+Based on this linguistic and contextual analysis, predict the most probable answer — whether the message was labelled ham (0) or spam (1) inside <answer></answer>, put how confidence you are (0 to 10) with that classification inside <confidence></confidence> , and explain the rationale behind your classification inside <reasons></reasons>. Do not repeat the original message.
+
+EXAMPLE INPUT:
+<data>From: winner@prize-national-lottery.com | Subject: Congratulations Winner! | Message: You have won a $1000000 lottery. Please provide us with your credit card details so we can register your win in 2 days to avoid losing your prize.</data>
+
+EXAMPLE OUTPUT:
+<answer>1</answer>
+<confidence>9</confidence>
+<reasons>
+The message uses high-stakes language and urgency to pressure the reader into sharing sensitive financial information. Normally, the claiming process for a huge prize involves just a basic name and ID check, then arranging a date and time to claim the prize. Unsolicited lottery winnings and requests for credit card details to register the win are not standard practice and are common spam indicators. Additionally, the email domain "prize-national-lottery.com" appears unofficial and mimics a known brand like "National Lottery" in the UK.
+However, the domain exhibits similarity to "national-lottery.com", which is an independent lottery result service around the world. This could be a subsidiary domain, nonetheless, the context of asking for a credit card still far outweighs this similarity.
+</reasons>
+"""
+
+LICENSE """
+Fine-tuned using Unsloth LoRA on 18 June 2025 by
+
+Piyawud 'Micro' Koonmanee
+
+Original model by the Qwen team.
+--------------------------------------------------------------
+
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