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Model: loaiabdalslam/Alexander-Cyber-Qwen Source: Original Platform
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183
README.md
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README.md
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---
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library_name: transformers
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tags:
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- cyber
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- red-team
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- cybersecuirty
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license: apache-2.0
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datasets:
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- loaiabdalslam/Alexander-Cyber-Dataset-v2
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name: alexander-cyber-qlora
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channels:
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- conda-forge
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- defaults
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dependencies:
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- python=3.12
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- pip
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- numpy
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- sentencepiece
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- pip:
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- "torch"
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- "transformers>=4.56"
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- "datasets>=3.0"
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- "accelerate>=1.0"
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- "peft>=0.17"
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- "trl>=0.27"
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- "bitsandbytes>=0.46.1"
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- "huggingface_hub>=0.34"
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- "safetensors"
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---
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# Alexander Cyber Qwen
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Fine-tuning workflow for **Alexander Cyber**, an authorized red-team and cybersecurity copilot, using **QLoRA**, Hugging Face Transformers, PEFT, TRL, and bitsandbytes.
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The training notebook loads a chat-formatted JSONL dataset, validates the message structure, quantizes the base model to 4-bit NF4, trains a LoRA adapter, saves/pushes the adapter, and optionally merges the adapter back into the base model.
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## Project Overview
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The current notebook is configured to use:
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* Base model: `Qwen/Qwen2.5-0.5B-Instruct`
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* Training method: QLoRA
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* Quantization: 4-bit NF4 with double quantization
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* Trainer: `trl.SFTTrainer`
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* LoRA rank: `32`
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* LoRA alpha: `64`
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* LoRA dropout: `0.05`
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* Optimizer: `paged_adamw_8bit`
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* Maximum training steps: `100`
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* Training sequence length: `1536`
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* Seed: `279`
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> Note: the notebook variable is named `USE_QWEN3_4B`, but when it is `True` the selected model is currently `Qwen/Qwen2.5-0.5B-Instruct`. The README preserves the behavior of the notebook as written.
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## Requirements
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A CUDA-capable NVIDIA GPU is recommended because the notebook uses 4-bit quantization through `bitsandbytes`.
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Main dependencies:
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* Python 3.12
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* PyTorch
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* Transformers >= 4.56
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* Datasets >= 3.0
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* Accelerate >= 1.0
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* PEFT >= 0.17
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* TRL >= 0.27
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* bitsandbytes >= 0.46.1
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* huggingface_hub >= 0.34
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* sentencepiece
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* safetensors
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## Environment Setup
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Create the Conda environment:
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```bash
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conda env create -f environment.yml
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conda activate alexander-cyber-qlora
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```
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Verify GPU support:
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```python
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import torch
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print("CUDA available:", torch.cuda.is_available())
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if torch.cuda.is_available():
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print("GPU:", torch.cuda.get_device_name(0))
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print("BF16 supported:", torch.cuda.is_bf16_supported())
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```
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## Hugging Face Login
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```python
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from huggingface_hub import notebook_login, whoami
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notebook_login()
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print(whoami())
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```
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## Dataset
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The notebook expects JSONL files for training and validation.
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Current Kaggle paths:
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```text
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/kaggle/input/datasets/loaiabdalslam/alexander-cyber/alexander_cyber_v2_train.jsonl
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/kaggle/input/datasets/loaiabdalslam/alexander-cyber/alexander_cyber_v2_validation.jsonl
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/kaggle/input/datasets/loaiabdalslam/alexander-cyber/alexander_cyber_v2_benchmark.jsonl
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```
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Each example contains a `messages` list:
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```json
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{
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"messages": [
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{
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"role": "system",
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"content": "You are Alexander Cyber, an authorized red-team and cybersecurity copilot."
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},
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{
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"role": "user",
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"content": "Analyze this security finding."
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},
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{
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"role": "assistant",
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"content": "Start by validating the evidence and confirming the affected service."
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}
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]
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}
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```
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## 4-bit QLoRA
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The model is loaded using bitsandbytes NF4 quantization:
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```python
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from transformers import BitsAndBytesConfig
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import torch
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compute_dtype = (
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torch.bfloat16
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if torch.cuda.is_bf16_supported()
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else torch.float16
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)
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bnb = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True,
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bnb_4bit_compute_dtype=compute_dtype,
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)
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```
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## Training
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Current training configuration:
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```text
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max_steps = 100
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learning_rate = 2e-4
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per_device_train_batch_size = 1
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per_device_eval_batch_size = 1
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gradient_accumulation_steps = 1
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warmup_steps = 20
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lr_scheduler_type = cosine
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eval_steps = 50
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save_steps = 100
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max_length = 1536
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packing = True
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optimizer = paged_adamw_8bit
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max_grad_norm = 0.3
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weight_decay = 0.01
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seed = 279
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```
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54
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") %}
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{{- '<|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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57
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": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"full_attention",
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"full_attention",
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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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_type": "default"
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.15.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.15.0"
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}
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1271ad484a50ae5f37693c22acf7e196456cdafe3cc04f07fb83c0927078c0d1
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size 988097536
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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30
tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
|
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"<|video_pad|>"
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],
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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