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Model: Dospacite/xai-phishing-deepseek-r1-qwen-7b-merged Source: Original Platform
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ModelHub XC
36
.gitattributes
vendored
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.gitattributes
vendored
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 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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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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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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# CyberGuard DeepSeek R1 Qwen 7B
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Standalone model produced by merging the LoRA adapter
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[`Dospacite/xai-phishing-deepseek-r1-qwen-7b`](https://huggingface.co/Dospacite/xai-phishing-deepseek-r1-qwen-7b) into
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[`deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B).
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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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53
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} {{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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62
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 131072,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 10000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.11.0",
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"use_cache": true,
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"use_mrope": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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9
generation_config.json
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 151646,
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"do_sample": true,
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"eos_token_id": 151643,
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"temperature": 0.6,
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"top_p": 0.95,
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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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from typing import Any
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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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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model_kwargs: dict[str, Any] = {
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"device_map": "auto",
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"trust_remote_code": True,
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"low_cpu_mem_usage": True,
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}
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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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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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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(
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prompt,
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return_tensors="pt",
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truncation=True,
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max_length=int(parameters.pop("max_input_tokens", 4096)),
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).to(self.model.device)
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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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}
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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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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-deepseek-r1-qwen-7b",
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"base_model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
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"dtype": "bfloat16",
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"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>=0.27
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e72d364a37413ca00a9000b2d2cff0be9c4511c6d1565263dcca665c5911c4ca
|
||||
size 11422968
|
||||
35
tokenizer_config.json
Normal file
35
tokenizer_config.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"bos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<｜begin▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<｜end▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"legacy": true,
|
||||
"model_max_length": 16384,
|
||||
"pad_token": {
|
||||
"__type": "AddedToken",
|
||||
"content": "<｜end▁of▁sentence｜>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"sp_model_kwargs": {},
|
||||
"unk_token": null,
|
||||
"tokenizer_class": "LlamaTokenizerFast",
|
||||
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜><think>\\n'}}{% endif %}"
|
||||
}
|
||||
Reference in New Issue
Block a user