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Model: npanium/deepseek-r1-qwen7b-smartcontract-grpo Source: Original Platform
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README.md
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---
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library_name: transformers
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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tags:
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- smart-contracts
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- solidity
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- security
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- vulnerability-detection
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- grpo
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- lora
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- reinforcement-learning
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language:
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- en
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license: mit
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---
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# DeepSeek-R1-Distill-Qwen-7B — Smart Contract Vulnerability Detection
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RL fine-tuned version of DeepSeek-R1-Distill-Qwen-7B for detecting vulnerabilities in Solidity smart contracts. Fine-tuned using GRPO (Group Relative Policy Optimization) with LoRA on the CGT (Consolidated Ground Truth) dataset.
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---
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## Model Description
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- **Base model:** deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
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- **Fine-tuning method:** GRPO + LoRA
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- **Task:** Smart contract vulnerability detection and classification
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- **Developer:** Nishant Pandav ([@npanium](https://github.com/npanium))
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- **Repository:** [https://github.com/npanium/smartcontracts-vulnerability-r1](https://github.com/npanium/smartcontracts-vulnerability-r1)
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- **License:** MIT
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---
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## What it does
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Given a Solidity smart contract, the model:
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1. Reasons through the code using chain-of-thought inside `<think>` tags
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2. Determines whether the contract is vulnerable
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3. Classifies the vulnerability by DASP category (1–9) and SWC ID (100–136)
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Output format:
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```
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<think>
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... reasoning about the contract ...
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</think>
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VULNERABLE: yes/no
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DASP_CATEGORY: N
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SWC_ID: NNN
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EXPLANATION: ...
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```
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---
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## Results
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Evaluated on 1,478 held-out contracts from the CGT dataset:
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| Metric | Before (base) | After (fine-tuned) | Delta |
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|--------|--------------|-------------------|-------|
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| Detection accuracy (Tier 1) | 23.0% | 74.3% | **+51.3%** |
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| DASP category (Tier 2) | 8.8% | 11.3% | +2.5% |
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| SWC ID (Tier 3) | 3.0% | 0.0% | -3.0% |
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| Overall | 11.6% | 28.5% | **+16.9%** |
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| Parse failure rate | 40.2% | 0.0% | **-40.2%** |
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---
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "npanium/deepseek-r1-qwen7b-smartcontract-grpo"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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contract = """
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pragma solidity ^0.4.18;
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contract Vulnerable {
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mapping(address => uint) public balances;
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function withdraw(uint _amount) public {
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if (balances[msg.sender] >= _amount) {
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msg.sender.call.value(_amount)();
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balances[msg.sender] -= _amount;
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}
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}
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}
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"""
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prompt = f"""Analyze this Solidity smart contract for security vulnerabilities.
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Think step by step inside <think> tags, then provide your assessment.
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``solidity
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{contract}``
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Use this exact format:
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VULNERABLE: yes/no
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DASP_CATEGORY: [1-9]
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SWC_ID: [100-136]
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EXPLANATION: [one sentence]"""
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messages = [{"role": "user", "content": prompt}]
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inputs = tokenizer.apply_chat_template(
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messages,
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return_tensors="pt",
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add_generation_prompt=True,
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)
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if hasattr(inputs, "input_ids"):
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inputs = inputs.input_ids
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inputs = inputs.to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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inputs,
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max_new_tokens=1024,
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temperature=0.1,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(
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outputs[0][inputs.shape[1]:],
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skip_special_tokens=True,
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)
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print(response)
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```
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---
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## Training Details
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### Dataset
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**CGT (Consolidated Ground Truth)** — [github.com/gsalzer/cgt](https://github.com/gsalzer/cgt)
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Consolidates 13 prior smart contract vulnerability datasets. Labels cross-validated across source datasets.
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| Split | Examples |
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|-------|----------|
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| Train | 5,910 |
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| Test (locked) | 1,478 |
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### Training Hyperparameters
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| Parameter | Value |
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|-----------|-------|
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| Training regime | bf16 mixed precision |
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| Learning rate | 5e-6 |
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| LoRA rank | 16 |
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| LoRA alpha | 32 |
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| LoRA target modules | q_proj, v_proj, k_proj, o_proj |
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| Generations per prompt | 8 |
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| Max completion length | 1024 |
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| Gradient accumulation steps | 8 |
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| Epochs | 1 |
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### Hardware
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- **GPU:** NVIDIA A100 SXM4 80GB
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- **Cloud provider:** Fluence Network
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- **Training duration:** ~48 hours
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---
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## Limitations
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**Outcome reward only.** The reward function validates whether the final label is correct, not whether the reasoning is valid. The model may produce plausible-sounding analysis that doesn't actually justify the conclusion.
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**SWC ID regression.** Post-training SWC ID accuracy dropped to zero. The model prioritised the higher-weighted binary detection reward at the expense of fine-grained weakness classification.
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**Context window.** Contracts exceeding ~4,000 characters were excluded from training. Performance on very large contracts is untested.
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---
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## Citation
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```bibtex
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@misc{pandav2026scvulnrl,
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author = {Pandav, Nishant},
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title = {Smart Contract Vulnerability Detection via RL Fine-Tuning},
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year = {2026},
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url = {https://github.com/npanium/smartcontracts-vulnerability-r1}
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}
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```
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chat_template.jinja
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{% 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 %}
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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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"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": "5.3.0",
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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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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": "5.3.0"
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}
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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:f870ae6b1169e03774feb6ec0d28fa34980f35f573e34ce18905b7f473a1cfb9
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size 15231272152
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:322664cdc3082b6eba003af5228a77ca1d7936d402e584ecde8f15d3d98bdb72
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size 11421911
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tokenizer_config.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<｜begin▁of▁sentence｜>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<｜end▁of▁sentence｜>",
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"is_local": false,
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"legacy": true,
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"model_max_length": 16384,
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"pad_token": "<｜end▁of▁sentence｜>",
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"sp_model_kwargs": {},
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"tokenizer_class": "TokenizersBackend",
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"unk_token": null
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}
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