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Model: wvnvwn/qwen2.5-7b-instruct-gsm8k-sn-tuned-lr5e-5 Source: Original Platform
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62
README.md
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README.md
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---
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license: apache-2.0
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tags:
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- safety
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- fine-tuning
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- llama
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- safety-neurons
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---
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# qwen2.5-7b-instruct-gsm8k-sn-tuned-lr5e-5
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This is a Safety Neuron-Tuned (SN-Tune) version of Llama-3.2-3B-Instruct.
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## Model Description
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- **Base Model**: meta-llama/Llama-3.2-3B-Instruct
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- **Fine-tuning Method**: SN-Tune (Safety Neuron Tuning)
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- **Training Data**: Circuit Breakers dataset (safety alignment data)
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- **Upload Date**: 2026-05-02 20:40:48
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## What is SN-Tune?
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SN-Tune is a selective fine-tuning approach that:
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1. Detects safety neurons - a small set of neurons critical for safety
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2. Freezes all non-safety parameters
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3. Fine-tunes only safety neurons on safety data
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This approach allows for:
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- Enhanced safety alignment
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- Minimal impact on general capabilities
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- Parameter-efficient fine-tuning
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "wvnvwn/qwen2.5-7b-instruct-gsm8k-sn-tuned-lr5e-5"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Generate text
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prompt = "How can I help you today?"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=100)
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print(tokenizer.decode(outputs[0]))
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```
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## Safety Note
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This model has been fine-tuned specifically for safety using the SN-Tune method.
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It should provide improved safety alignment compared to the base model.
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## License
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This model is licensed under the Apache 2.0 License.
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See the base model (meta-llama/Llama-3.2-3B-Instruct) for more details.
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## References
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- Base model: [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
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- Safety neurons detection methodology
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added_tokens.json
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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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{%- 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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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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"dtype": "bfloat16",
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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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],
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"model_type": "qwen2",
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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finetune_config.json
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{
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"base_model": "wvnvwn/qwen2.5-7b-instruct-only-sn-tuned-lr5e-5",
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"fine_tuning_type": "GSM8K Fine-tuning with Safety Neuron Freezing",
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"safety_neurons_file": "/NHNHOME/WORKSPACE/26msit001_A/edge_ai_lab/jongbokwon/minseong_results/safety_qwen2.5-7b-instruct/safety_neuron.txt",
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"dataset": "GSM8K",
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||||
"num_train_samples": 7473,
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"batch_size": 4,
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"grad_accum": 4,
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"learning_rate": 5e-05,
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"weight_decay": 0.01,
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"warmup_ratio": 0.1,
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"epochs": 3,
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"max_length": 1024,
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|
||||
"optimizer": "adamw_torch",
|
||||
"gradient_checkpointing": false,
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||||
"dtype": "bf16",
|
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"trainer_type": "Trainer",
|
||||
"strategy": "Freeze safety neurons, train others"
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}
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generation_config.json
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generation_config.json
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"top_p": 0.8,
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"transformers_version": "4.57.3"
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}
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}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user