Instructions to use piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M
Use Docker
docker model run hf.co/piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF with Ollama:
ollama run hf.co/piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF with Docker Model Runner:
docker model run hf.co/piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M
- Lemonade
How to use piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull piyawudk/PhishMe-Qwen3-Base-8B-GRPO-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.PhishMe-Qwen3-Base-8B-GRPO-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload Modelfile
Browse files
Modelfile
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|
| 1 |
+
FROM ./PhishMe-Qwen3-Base-8B-GRPO-Q4_K_M.gguf
|
| 2 |
+
|
| 3 |
+
PARAMETER temperature 0.6
|
| 4 |
+
PARAMETER top_k 20
|
| 5 |
+
PARAMETER min_p 0.0
|
| 6 |
+
PARAMETER top_p 0.95
|
| 7 |
+
PARAMETER num_ctx 4096
|
| 8 |
+
PARAMETER num_predict 4096
|
| 9 |
+
PARAMETER repeat_penalty 1
|
| 10 |
+
|
| 11 |
+
PARAMETER stop <|im_start|>
|
| 12 |
+
PARAMETER stop <|im_end|>
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
TEMPLATE """
|
| 16 |
+
{{- $lastUserIdx := -1 -}}
|
| 17 |
+
{{- range $idx, $msg := .Messages -}}
|
| 18 |
+
{{- if eq $msg.Role "user" }}{{ $lastUserIdx = $idx }}{{ end -}}
|
| 19 |
+
{{- end }}
|
| 20 |
+
{{- if or .System .Tools }}<|im_start|>system
|
| 21 |
+
{{ if .System }}
|
| 22 |
+
{{ .System }}
|
| 23 |
+
{{- end }}
|
| 24 |
+
{{- if .Tools }}
|
| 25 |
+
|
| 26 |
+
# Tools
|
| 27 |
+
|
| 28 |
+
You may call one or more functions to assist with the user query.
|
| 29 |
+
|
| 30 |
+
You are provided with function signatures within <tools></tools> XML tags:
|
| 31 |
+
<tools>
|
| 32 |
+
{{- range .Tools }}
|
| 33 |
+
{"type": "function", "function": {{ .Function }}}
|
| 34 |
+
{{- end }}
|
| 35 |
+
</tools>
|
| 36 |
+
|
| 37 |
+
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
|
| 38 |
+
<tool_call>
|
| 39 |
+
{"name": <function-name>, "arguments": <args-json-object>}
|
| 40 |
+
</tool_call>
|
| 41 |
+
{{- end -}}
|
| 42 |
+
<|im_end|>
|
| 43 |
+
{{ end }}
|
| 44 |
+
{{- range $i, $_ := .Messages }}
|
| 45 |
+
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
|
| 46 |
+
{{- if eq .Role "user" }}<|im_start|>user
|
| 47 |
+
<data>{{ .Content }}</data>
|
| 48 |
+
{{- if and $.IsThinkSet (eq $i $lastUserIdx) }}
|
| 49 |
+
{{- if $.Think -}}
|
| 50 |
+
{{- " "}}/think
|
| 51 |
+
{{- else -}}
|
| 52 |
+
{{- " "}}/no_think
|
| 53 |
+
{{- end -}}
|
| 54 |
+
{{- end }}<|im_end|>
|
| 55 |
+
{{ else if eq .Role "assistant" }}<|im_start|>assistant
|
| 56 |
+
{{ if (and $.IsThinkSet (and .Thinking (or $last (gt $i $lastUserIdx)))) -}}
|
| 57 |
+
<think>{{ .Thinking }}</think>
|
| 58 |
+
{{ end -}}
|
| 59 |
+
{{ if .Content }}{{ .Content }}
|
| 60 |
+
{{- else if .ToolCalls }}<tool_call>
|
| 61 |
+
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
|
| 62 |
+
{{ end }}</tool_call>
|
| 63 |
+
{{- end }}{{ if not $last }}<|im_end|>
|
| 64 |
+
{{ end }}
|
| 65 |
+
{{- else if eq .Role "tool" }}<|im_start|>user
|
| 66 |
+
<tool_response>
|
| 67 |
+
{{ .Content }}
|
| 68 |
+
</tool_response><|im_end|>
|
| 69 |
+
{{ end }}
|
| 70 |
+
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
|
| 71 |
+
{{ if and $.IsThinkSet (not $.Think) -}}
|
| 72 |
+
<think>
|
| 73 |
+
|
| 74 |
+
</think>
|
| 75 |
+
|
| 76 |
+
{{ end -}}
|
| 77 |
+
{{ end }}
|
| 78 |
+
{{- end }}"""
|
| 79 |
+
|
| 80 |
+
SYSTEM """
|
| 81 |
+
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.
|
| 82 |
+
|
| 83 |
+
**Instructions:**
|
| 84 |
+
|
| 85 |
+
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.
|
| 86 |
+
2. **Contextual Evaluation:** Consider contextual signals like:
|
| 87 |
+
- Sender address format and domain legitimacy
|
| 88 |
+
- Tone consistency and grammatical accuracy
|
| 89 |
+
- Reasonableness of the request
|
| 90 |
+
- Typical patterns of legitimate vs. malicious communication
|
| 91 |
+
3. **Classification & Justification:**
|
| 92 |
+
Present your classification and explanation using this exact structure:
|
| 93 |
+
- First, output the label as `<answer>0</answer>` for ham or `<answer>1</answer>` for spam.
|
| 94 |
+
- Then, output your classification confidence as `<confidence>X</confidence>` where X ranges from 0 (least confident) to 10 (most confident).
|
| 95 |
+
- Finally, output a concise but thorough explanation inside `<reasons></reasons>`, focusing on the key features that informed your classification.
|
| 96 |
+
|
| 97 |
+
Do **not** quote or repeat the original message. Focus solely on inference and linguistic features.
|
| 98 |
+
|
| 99 |
+
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.
|
| 100 |
+
|
| 101 |
+
EXAMPLE INPUT:
|
| 102 |
+
<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>
|
| 103 |
+
|
| 104 |
+
EXAMPLE OUTPUT:
|
| 105 |
+
<answer>1</answer>
|
| 106 |
+
<confidence>9</confidence>
|
| 107 |
+
<reasons>
|
| 108 |
+
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.
|
| 109 |
+
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.
|
| 110 |
+
</reasons>
|
| 111 |
+
"""
|
| 112 |
+
|
| 113 |
+
LICENSE """
|
| 114 |
+
Fine-tuned using Unsloth LoRA on 18 June 2025 by
|
| 115 |
+
|
| 116 |
+
Piyawud 'Micro' Koonmanee
|
| 117 |
+
|
| 118 |
+
Original model by the Qwen team.
|
| 119 |
+
--------------------------------------------------------------
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
Apache License
|
| 123 |
+
Version 2.0, January 2004
|
| 124 |
+
http://www.apache.org/licenses/
|
| 125 |
+
|
| 126 |
+
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
| 127 |
+
|
| 128 |
+
1. Definitions.
|
| 129 |
+
|
| 130 |
+
"License" shall mean the terms and conditions for use, reproduction,
|
| 131 |
+
and distribution as defined by Sections 1 through 9 of this document.
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| 132 |
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| 133 |
+
"Licensor" shall mean the copyright owner or entity authorized by
|
| 134 |
+
the copyright owner that is granting the License.
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| 135 |
+
|
| 136 |
+
"Legal Entity" shall mean the union of the acting entity and all
|
| 137 |
+
other entities that control, are controlled by, or are under common
|
| 138 |
+
control with that entity. For the purposes of this definition,
|
| 139 |
+
"control" means (i) the power, direct or indirect, to cause the
|
| 140 |
+
direction or management of such entity, whether by contract or
|
| 141 |
+
otherwise, or (ii) ownership of fifty percent (50%) or more of the
|
| 142 |
+
outstanding shares, or (iii) beneficial ownership of such entity.
|
| 143 |
+
|
| 144 |
+
"You" (or "Your") shall mean an individual or Legal Entity
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| 145 |
+
exercising permissions granted by this License.
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+
"Source" form shall mean the preferred form for making modifications,
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+
including but not limited to software source code, documentation
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source, and configuration files.
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"Object" form shall mean any form resulting from mechanical
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transformation or translation of a Source form, including but
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not limited to compiled object code, generated documentation,
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and conversions to other media types.
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"Work" shall mean the work of authorship, whether in Source or
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Object form, made available under the License, as indicated by a
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copyright notice that is included in or attached to the work
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(an example is provided in the Appendix below).
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"Derivative Works" shall mean any work, whether in Source or Object
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form, that is based on (or derived from) the Work and for which the
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editorial revisions, annotations, elaborations, or other modifications
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represent, as a whole, an original work of authorship. For the purposes
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of this License, Derivative Works shall not include works that remain
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separable from, or merely link (or bind by name) to the interfaces of,
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"Contribution" shall mean any work of authorship, including
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"""
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