初始化项目，由ModelHub XC社区提供模型
Model: Dospacite/xai-phishing-llama-3.1-8b-merged Source: Original Platform
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36
.gitattributes
vendored
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*.7z filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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27
README.md
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README.md
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---
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base_model:
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- unsloth/Meta-Llama-3.1-8B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- merged
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- phishing-detection
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- explainability
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- cybersecurity
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---
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# Explainable Phishing Detector - Llama 3.1 8B Merged
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Standalone model produced by merging the LoRA adapter
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[`Dospacite/xai-phishing-llama-3.1-8b`](https://huggingface.co/Dospacite/xai-phishing-llama-3.1-8b) into
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[`unsloth/Meta-Llama-3.1-8B-Instruct`](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct).
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This repository contains complete model weights and does not require PEFT or
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the adapter repository at inference time. It expects the structured webpage
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feature prompt documented in the original adapter repository.
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## Intended use
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The model classifies structured webpage evidence as phishing or benign and
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returns an explanation. Its output is decision support and should be reviewed
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alongside the cited webpage features.
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109
chat_template.jinja
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109
chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
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{{- "<|eom_id|>" }}
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{%- else %}
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{{- "<|eot_id|>" }}
|
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{%- endif %}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
|
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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37
config.json
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37
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
|
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"bos_token_id": 128000,
|
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"dtype": "bfloat16",
|
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"eos_token_id": 128009,
|
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 14336,
|
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"max_position_embeddings": 131072,
|
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
|
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
|
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"rope_parameters": {
|
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"factor": 8.0,
|
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"high_freq_factor": 4.0,
|
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"low_freq_factor": 1.0,
|
||||
"original_max_position_embeddings": 8192,
|
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"rope_theta": 500000.0,
|
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"rope_type": "llama3"
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},
|
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"tie_word_embeddings": false,
|
||||
"transformers_version": "5.11.0",
|
||||
"unsloth_fixed": true,
|
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"use_cache": true,
|
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"vocab_size": 128256
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}
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14
generation_config.json
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generation_config.json
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{
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"bos_token_id": 128000,
|
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"do_sample": true,
|
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"eos_token_id": [
|
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128001,
|
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128008,
|
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128009
|
||||
],
|
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"max_length": 131072,
|
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"pad_token_id": 128004,
|
||||
"temperature": 0.6,
|
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"top_p": 0.9,
|
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"transformers_version": "5.11.0"
|
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}
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63
handler.py
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handler.py
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from __future__ import annotations
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|
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from typing import Any
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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|
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class EndpointHandler:
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def __init__(self, path: str = ""):
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self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
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if self.tokenizer.pad_token_id is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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|
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model_kwargs: dict[str, Any] = {
|
||||
"device_map": "auto",
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||||
"trust_remote_code": True,
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"low_cpu_mem_usage": True,
|
||||
"attn_implementation": "sdpa",
|
||||
}
|
||||
if torch.cuda.is_available():
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model_kwargs["torch_dtype"] = torch.bfloat16
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self.model = AutoModelForCausalLM.from_pretrained(path, **model_kwargs)
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self.model.eval()
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|
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def __call__(self, data: dict[str, Any]) -> dict[str, str]:
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inputs = data.get("inputs", "")
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parameters = dict(data.get("parameters") or {})
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|
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if isinstance(inputs, dict) and isinstance(inputs.get("messages"), list):
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prompt = self.tokenizer.apply_chat_template(
|
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inputs["messages"],
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tokenize=False,
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add_generation_prompt=True,
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)
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else:
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prompt = str(inputs)
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encoded = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
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max_input_tokens = int(parameters.pop("max_input_tokens", 4096))
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if encoded["input_ids"].shape[1] > max_input_tokens:
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encoded["input_ids"] = encoded["input_ids"][:, -max_input_tokens:]
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encoded["attention_mask"] = encoded["attention_mask"][:, -max_input_tokens:]
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|
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input_length = encoded["input_ids"].shape[1]
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temperature = float(parameters.pop("temperature", 0))
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do_sample = bool(parameters.pop("do_sample", temperature > 0))
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generation_kwargs = {
|
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"max_new_tokens": int(parameters.pop("max_new_tokens", 256)),
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"repetition_penalty": float(parameters.pop("repetition_penalty", 1.1)),
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"do_sample": do_sample,
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"pad_token_id": self.tokenizer.pad_token_id,
|
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"eos_token_id": self.tokenizer.eos_token_id,
|
||||
}
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if do_sample:
|
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generation_kwargs["temperature"] = max(temperature, 0.01)
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generation_kwargs["top_p"] = float(parameters.pop("top_p", 0.9))
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|
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with torch.inference_mode():
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output = self.model.generate(**encoded, **generation_kwargs)
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generated = self.tokenizer.decode(output[0, input_length:], skip_special_tokens=True).strip()
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return {"generated_text": generated}
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6
merge_metadata.json
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merge_metadata.json
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{
|
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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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}
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}
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||||
1
requirements.txt
Normal file
1
requirements.txt
Normal file
@@ -0,0 +1 @@
|
||||
accelerate>=1.2.0
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user