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  ---
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- license: apache-2.0
 
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  base_model: meta-llama/Llama-3.2-3B-Instruct
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  tags:
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- - code
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- - security
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- - llama
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- - meta
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- - securecode
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- - owasp
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- - vulnerability-detection
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- datasets:
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- - scthornton/securecode-v2
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- language:
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- - en
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- library_name: transformers
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  pipeline_tag: text-generation
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- arxiv: 2512.18542
 
 
19
  ---
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- # Llama 3.2 3B - SecureCode Edition
 
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- <div align="center">
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- [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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- [![Training Dataset](https://img.shields.io/badge/dataset-SecureCode%20v2.0-green.svg)](https://huggingface.co/datasets/scthornton/securecode-v2)
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- [![Base Model](https://img.shields.io/badge/base-Llama%203.2%203B-orange.svg)](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
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- [![perfecXion.ai](https://img.shields.io/badge/by-perfecXion.ai-purple.svg)](https://perfecxion.ai)
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- **🚀 The most accessible security-aware code model - runs anywhere**
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- Security expertise meets consumer-grade hardware. Perfect for developers who want enterprise-level security guidance without datacenter infrastructure.
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- [📄 Paper](https://arxiv.org/abs/2512.18542) | [🤗 Model Hub](https://huggingface.co/scthornton/llama-3.2-3b-securecode) | [📊 Dataset](https://huggingface.co/datasets/scthornton/securecode-v2) | [💻 perfecXion.ai](https://perfecxion.ai) | [📚 Collection](https://huggingface.co/collections/scthornton/securecode)
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36
- </div>
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38
- ---
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-
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- ## 🎯 Quick Decision Guide
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-
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- **Choose This Model If:**
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- - ✅ You need security guidance on **consumer hardware** (8GB+ RAM)
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- - ✅ You're running on **Apple Silicon Macs** (M1/M2/M3/M4)
45
- - ✅ You want **fast inference** for IDE integration
46
- - ✅ You're building security tools for **developer workstations**
47
- - ✅ You need **low-cost deployment** in production
48
- - ✅ You're creating **educational security tools** for students
49
-
50
- **Consider Larger Models If:**
51
- - ⚠️ You need deep multi-file codebase analysis (→ Qwen 14B, Granite 20B)
52
- - ⚠️ You're handling complex enterprise architectures (→ CodeLlama 13B, Granite 20B)
53
- - ⚠️ You need maximum code understanding (→ Qwen 7B/14B)
54
-
55
- ---
56
-
57
- ## 📊 Collection Positioning
58
-
59
- | Model | Size | Best For | Hardware | Inference Speed | Unique Strength |
60
- |-------|------|----------|----------|-----------------|-----------------|
61
- | **Llama 3.2 3B** | **3B** | **Consumer deployment** | **8GB RAM** | **⚡⚡⚡ Fastest** | **Most accessible** |
62
- | DeepSeek 6.7B | 6.7B | Security-optimized baseline | 16GB RAM | ⚡⚡ Fast | Security architecture |
63
- | Qwen 7B | 7B | Best code understanding | 16GB RAM | ⚡⚡ Fast | Best-in-class 7B |
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- | CodeGemma 7B | 7B | Google ecosystem | 16GB RAM | ⚡⚡ Fast | Instruction following |
65
- | CodeLlama 13B | 13B | Enterprise trust | 24GB RAM | ⚡ Medium | Meta brand, proven |
66
- | Qwen 14B | 14B | Advanced analysis | 32GB RAM | ⚡ Medium | 128K context window |
67
- | StarCoder2 15B | 15B | Multi-language specialist | 32GB RAM | ⚡ Medium | 600+ languages |
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- | Granite 20B | 20B | Enterprise-scale | 48GB RAM | Medium | IBM trust, largest |
69
-
70
- **This Model's Sweet Spot:** Maximum accessibility + solid security guidance. Ideal for developer tools, educational platforms, and consumer applications.
71
-
72
- ---
73
-
74
- ## 🚨 The Problem This Solves
75
-
76
- **AI coding assistants produce vulnerable code in 45% of security-relevant scenarios** (Veracode 2025). When developers rely on standard code models for security-sensitive features like authentication, authorization, or data handling, they unknowingly introduce critical vulnerabilities.
77
-
78
- **Real-world costs:**
79
- - **Equifax breach** (SQL injection): $425 million in damages + brand destruction
80
- - **Capital One** (SSRF attack): 100 million customer records exposed, $80M fine
81
- - **SolarWinds** (authentication bypass): 18,000 organizations compromised
82
- - **LastPass** (cryptographic failures): 30 million users' password vaults at risk
83
-
84
- This model was trained to prevent these exact scenarios by understanding security at the code level.
85
-
86
- ---
87
-
88
- ## 💡 What is This?
89
-
90
- This is **Llama 3.2 3B Instruct** fine-tuned on the **SecureCode v2.0 dataset** - a production-grade collection of 1,209 security-focused coding examples covering the complete OWASP Top 10:2025.
91
-
92
- Unlike standard code models that frequently generate vulnerable code, this model has been specifically trained to:
93
-
94
- ✅ **Recognize security vulnerabilities** in code across 11 programming languages
95
- ✅ **Generate secure implementations** with defense-in-depth patterns
96
- ✅ **Explain attack vectors** with concrete exploitation examples
97
- ✅ **Provide operational guidance** including SIEM integration, logging, and monitoring
98
-
99
- **The Result:** A code assistant that thinks like a security engineer, not just a developer.
100
-
101
- **Why 3B Parameters?** At only 3B parameters, this is the **most accessible** security-focused code model. It runs on:
102
- - 💻 Consumer laptops with 8GB+ RAM
103
- - 📱 Apple Silicon Macs (M1/M2/M3/M4)
104
- - 🖥️ Desktop GPUs (RTX 3060+, even RTX 2060)
105
- - ☁️ Free Colab/Kaggle notebooks
106
- - 🔌 Edge devices and embedded systems
107
-
108
- Perfect for developers who want security guidance without requiring datacenter infrastructure.
109
-
110
- ---
111
-
112
- ## 🔐 Security Training Coverage
113
-
114
- ### Real-World Vulnerability Distribution
115
-
116
- Trained on 1,209 security examples with real CVE grounding:
117
-
118
- | OWASP Category | Examples | Real Incidents |
119
- |----------------|----------|----------------|
120
- | **Broken Access Control** | 224 | Equifax, Facebook, Uber |
121
- | **Authentication Failures** | 199 | SolarWinds, Okta, LastPass |
122
- | **Injection Attacks** | 125 | Capital One, Yahoo, LinkedIn |
123
- | **Cryptographic Failures** | 115 | LastPass, Adobe, Dropbox |
124
- | **Security Misconfiguration** | 98 | Tesla, MongoDB, Elasticsearch |
125
- | **Vulnerable Components** | 87 | Log4Shell, Heartbleed, Struts |
126
- | **Identification/Auth Failures** | 84 | Twitter, GitHub, Reddit |
127
- | **Software/Data Integrity** | 78 | SolarWinds, Codecov, npm |
128
- | **Logging Failures** | 71 | Various incident responses |
129
- | **SSRF** | 69 | Capital One, Shopify |
130
- | **Insecure Design** | 59 | Architectural flaws |
131
-
132
- ### Multi-Language Support
133
-
134
- Fine-tuned on security examples across:
135
- - **Python** (Django, Flask, FastAPI) - 280 examples
136
- - **JavaScript/TypeScript** (Express, NestJS, React) - 245 examples
137
- - **Java** (Spring Boot) - 178 examples
138
- - **Go** (Gin framework) - 145 examples
139
- - **PHP** (Laravel, Symfony) - 112 examples
140
- - **C#** (ASP.NET Core) - 89 examples
141
- - **Ruby** (Rails) - 67 examples
142
- - **Rust** (Actix, Rocket) - 45 examples
143
- - **C/C++** (Memory safety) - 28 examples
144
- - **Kotlin, Swift** - 20 examples
145
-
146
- ---
147
-
148
- ## 🎯 Deployment Scenarios
149
-
150
- ### Scenario 1: IDE Integration (VS Code / Cursor / JetBrains)
151
-
152
- **Perfect fit for real-time security suggestions in developer IDEs.**
153
-
154
- **Hardware:** Developer laptop with 8GB+ RAM
155
- **Latency:** ~50ms per completion (local inference)
156
- **Use Case:** Real-time security linting and code review
157
-
158
- ```python
159
- # Example: Cursor IDE integration
160
- from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
161
- from peft import PeftModel
162
-
163
- # Load quantized for fast IDE response
164
- bnb_config = BitsAndBytesConfig(load_in_4bit=True)
165
- model = AutoModelForCausalLM.from_pretrained(
166
- "meta-llama/Llama-3.2-3B-Instruct",
167
- quantization_config=bnb_config,
168
- device_map="auto"
169
- )
170
- model = PeftModel.from_pretrained(model, "scthornton/llama-3.2-3b-securecode")
171
-
172
- # Now: Real-time security suggestions as you code
173
- ```
174
-
175
- **ROI:** Catch vulnerabilities **before** they reach code review. Typical enterprise saves **$100K-$500K/year** in remediation costs.
176
-
177
- ---
178
-
179
- ### Scenario 2: Educational Platform (Coding Bootcamps / Universities)
180
-
181
- **Teach secure coding without expensive infrastructure.**
182
-
183
- **Hardware:** Student laptops (8GB RAM minimum)
184
- **Deployment:** Self-hosted or free tier cloud
185
- **Use Case:** Interactive security training for developers
186
-
187
- **Value Proposition:**
188
- - Students learn secure patterns from day 1
189
- - No cloud costs - runs on student hardware
190
- - Scalable to thousands of students
191
- - Real vulnerability examples from actual breaches
192
-
193
- ---
194
-
195
- ### Scenario 3: CI/CD Security Check
196
-
197
- **Automated security review in build pipeline.**
198
-
199
- **Hardware:** Standard CI runner (8GB RAM)
200
- **Latency:** ~2-3 minutes for 1,000-line review
201
- **Use Case:** Pre-merge security validation
202
-
203
- ```yaml
204
- # GitHub Actions example
205
- - name: Security Code Review
206
- run: |
207
- docker run --gpus all \
208
- -v $(pwd):/code \
209
- securecode/llama-3b-securecode:latest \
210
- review /code --format json
211
- ```
212
-
213
- **ROI:** Block vulnerabilities before merge. Reduces post-deploy security fixes by **70-80%**.
214
-
215
- ---
216
-
217
- ### Scenario 4: Security Training Chatbot
218
-
219
- **24/7 security knowledge base for development teams.**
220
-
221
- **Hardware:** Single GPU server (RTX 3090 / A5000)
222
- **Capacity:** 50-100 concurrent users
223
- **Use Case:** On-demand security expertise
224
-
225
- **Metrics:**
226
- - Reduces security team tickets by **40%**
227
- - Answers common questions instantly
228
- - Scales security knowledge across entire org
229
-
230
- ---
231
-
232
- ## 📊 Training Details
233
-
234
- | Parameter | Value | Why This Matters |
235
- |-----------|-------|------------------|
236
- | **Base Model** | meta-llama/Llama-3.2-3B-Instruct | Proven foundation, optimized for instruction following |
237
- | **Fine-tuning Method** | LoRA (Low-Rank Adaptation) | Efficient training, preserves base capabilities |
238
- | **Training Dataset** | [SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2) | 100% incident-grounded, expert-validated |
239
- | **Dataset Size** | 841 training examples | Focused on quality over quantity |
240
- | **Training Epochs** | 3 | Optimal convergence without overfitting |
241
- | **LoRA Rank (r)** | 16 | Balanced parameter efficiency |
242
- | **LoRA Alpha** | 32 | Learning rate scaling factor |
243
- | **Learning Rate** | 2e-4 | Standard for LoRA fine-tuning |
244
- | **Quantization** | 4-bit (bitsandbytes) | Enables consumer hardware training |
245
- | **Trainable Parameters** | 24.3M (0.75% of 3.2B total) | Minimal parameters, maximum impact |
246
- | **Total Parameters** | 3.2B | Small enough for edge deployment |
247
- | **GPU Used** | NVIDIA A100 40GB | Enterprise training infrastructure |
248
- | **Training Time** | 22 minutes | Fast iteration cycles |
249
- | **Final Training Loss** | 0.824 | Strong convergence, solid learning |
250
-
251
- ### Training Methodology
252
-
253
- **LoRA (Low-Rank Adaptation)** was chosen for three critical reasons:
254
- 1. **Efficiency:** Trains only 0.75% of model parameters (24.3M vs 3.2B)
255
- 2. **Quality:** Preserves base model's code generation capabilities
256
- 3. **Deployability:** Minimal memory overhead enables consumer hardware deployment
257
-
258
- **Loss Progression Analysis:**
259
- - Epoch 1: 1.156 (baseline understanding)
260
- - Epoch 2: 0.912 (security pattern recognition)
261
- - Epoch 3: 0.824 (full convergence)
262
-
263
- **Result:** Excellent convergence showing strong security knowledge integration without catastrophic forgetting.
264
-
265
- ---
266
-
267
- ## 🚀 Usage
268
-
269
- ### Quick Start (Fastest Path to Secure Code)
270
-
271
- ```python
272
- from transformers import AutoModelForCausalLM, AutoTokenizer
273
- from peft import PeftModel
274
-
275
- # Load base model and tokenizer
276
- base_model = "meta-llama/Llama-3.2-3B-Instruct"
277
- model = AutoModelForCausalLM.from_pretrained(
278
- base_model,
279
- device_map="auto",
280
- torch_dtype="auto"
281
- )
282
- tokenizer = AutoTokenizer.from_pretrained(base_model)
283
-
284
- # Load SecureCode LoRA adapter
285
- model = PeftModel.from_pretrained(model, "scthornton/llama-3.2-3b-securecode")
286
-
287
- # Generate secure code
288
- prompt = """### User:
289
- How do I implement JWT authentication in Express.js?
290
-
291
- ### Assistant:
292
- """
293
-
294
- inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
295
- outputs = model.generate(
296
- **inputs,
297
- max_new_tokens=2048,
298
- temperature=0.7,
299
- top_p=0.95,
300
- do_sample=True
301
- )
302
-
303
- response = tokenizer.decode(outputs[0], skip_special_tokens=True)
304
- print(response)
305
- ```
306
-
307
- ---
308
-
309
- ### Consumer Hardware Deployment (8GB RAM)
310
-
311
- ```python
312
- from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
313
- from peft import PeftModel
314
-
315
- # 4-bit quantization for consumer GPUs
316
- bnb_config = BitsAndBytesConfig(
317
- load_in_4bit=True,
318
- bnb_4bit_use_double_quant=True,
319
- bnb_4bit_quant_type="nf4",
320
- bnb_4bit_compute_dtype="bfloat16"
321
- )
322
-
323
- base_model = AutoModelForCausalLM.from_pretrained(
324
- "meta-llama/Llama-3.2-3B-Instruct",
325
- quantization_config=bnb_config,
326
- device_map="auto"
327
- )
328
-
329
- model = PeftModel.from_pretrained(base_model, "scthornton/llama-3.2-3b-securecode")
330
- tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
331
-
332
- # Now runs on:
333
- # - MacBook Air M1 (8GB)
334
- # - RTX 3060 (12GB)
335
- # - RTX 2060 (6GB)
336
- # - Free Google Colab
337
- ```
338
-
339
- ---
340
-
341
- ### Production Deployment (Merge for Speed)
342
-
343
- For production deployment, merge the adapter for 2-3x faster inference:
344
-
345
- ```python
346
- from transformers import AutoModelForCausalLM, AutoTokenizer
347
- from peft import PeftModel
348
-
349
- # Load base + adapter
350
- base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
351
- model = PeftModel.from_pretrained(base_model, "scthornton/llama-3.2-3b-securecode")
352
-
353
- # Merge and save
354
- merged_model = model.merge_and_unload()
355
- merged_model.save_pretrained("./securecode-merged")
356
- tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
357
- tokenizer.save_pretrained("./securecode-merged")
358
-
359
- # Deploy merged model for fastest inference
360
- ```
361
-
362
- **Performance gain:** 2-3x faster than adapter loading, critical for production APIs.
363
-
364
- ---
365
-
366
- ### Integration with LangChain (Enterprise Workflow)
367
-
368
- ```python
369
- from langchain.llms import HuggingFacePipeline
370
- from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
371
- from peft import PeftModel
372
-
373
- base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
374
- model = PeftModel.from_pretrained(base_model, "scthornton/llama-3.2-3b-securecode")
375
- tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
376
-
377
- pipe = pipeline(
378
- "text-generation",
379
- model=model,
380
- tokenizer=tokenizer,
381
- max_new_tokens=2048,
382
- temperature=0.7
383
- )
384
-
385
- llm = HuggingFacePipeline(pipeline=pipe)
386
-
387
- # Use in LangChain
388
- from langchain.prompts import PromptTemplate
389
- from langchain.chains import LLMChain
390
-
391
- security_template = """Review this code for OWASP Top 10 vulnerabilities:
392
-
393
- {code}
394
-
395
- Provide specific vulnerability details and secure alternatives."""
396
-
397
- prompt = PromptTemplate(template=security_template, input_variables=["code"])
398
- chain = LLMChain(llm=llm, prompt=prompt)
399
-
400
- # Automated security review workflow
401
- result = chain.run(code=user_submitted_code)
402
- ```
403
-
404
- ---
405
-
406
- ## 📈 Performance & Benchmarks
407
-
408
- ### Hardware Requirements
409
-
410
- | Deployment | RAM | GPU VRAM | Tokens/Second | Latency (2K response) | Cost/Month |
411
- |-----------|-----|----------|---------------|----------------------|------------|
412
- | **4-bit Quantized** | 8GB | 4GB | ~20 tok/s | ~100 seconds | $0 (local) |
413
- | **8-bit Quantized** | 12GB | 6GB | ~25 tok/s | ~80 seconds | $0 (local) |
414
- | **Full Precision (bf16)** | 16GB | 8GB | ~35 tok/s | ~57 seconds | $0 (local) |
415
- | **Cloud (Replicate)** | N/A | N/A | ~40 tok/s | ~50 seconds | ~$15-30 |
416
-
417
- **Winner:** Local deployment. Zero ongoing costs, full data privacy.
418
 
419
- ### Real-World Performance
420
 
421
- **Tested on RTX 3060 12GB** (consumer gaming GPU):
422
- - **Tokens/second:** ~20 tok/s (4-bit), ~30 tok/s (full precision)
423
- - **Cold start:** ~3 seconds
424
- - **Memory usage:** 4.2GB (4-bit), 6.8GB (full precision)
425
- - **Power consumption:** ~120W during inference
426
 
427
- **Tested on M1 MacBook Air** (8GB unified memory):
428
- - **Tokens/second:** ~12 tok/s (4-bit only)
429
- - **Memory usage:** 5.1GB
430
- - **Battery impact:** Moderate (~20% drain per hour of continuous use)
431
 
432
- ### Security Vulnerability Detection
 
 
 
 
 
 
 
 
 
 
433
 
434
- Coming soon - evaluation on industry-standard security benchmarks:
435
- - SecurityEval dataset
436
- - CWE-based vulnerability detection
437
- - OWASP Top 10 coverage assessment
438
 
439
- **Community Contributions Welcome!** If you benchmark this model, please open a discussion and share results.
440
 
441
- ---
442
-
443
- ## 💰 Cost Analysis
444
-
445
- ### Total Cost of Ownership (TCO) - 1 Year
446
-
447
- **Option 1: Self-Hosted (Local GPU)**
448
- - Hardware: RTX 3060 12GB - $300-400 (one-time)
449
- - Electricity: ~$50/year (assuming 8 hours/day usage)
450
- - **Total Year 1:** $350-450
451
- - **Total Year 2+:** $50/year
452
-
453
- **Option 2: Self-Hosted (Cloud GPU)**
454
- - AWS g4dn.xlarge: $0.526/hour
455
- - Usage: 40 hours/week (development team)
456
- - **Total Year 1:** $1,094/year
457
-
458
- **Option 3: API Service (Replicate / Together AI)**
459
- - Cost: $0.10-0.25 per 1M tokens
460
- - Usage: 500M tokens/year (medium team)
461
- - **Total Year 1:** $50-125/year
462
-
463
- **Option 4: Enterprise GPT-4 (for comparison)**
464
- - Cost: $30/1M input tokens, $60/1M output tokens
465
- - Usage: 250M input + 250M output
466
- - **Total Year 1:** $22,500/year
467
-
468
- **ROI Winner:** Self-hosted local GPU. Pays for itself in 1-2 months vs cloud, instant ROI vs GPT-4.
469
-
470
- ---
471
-
472
- ## 🎯 Use Cases & Examples
473
-
474
- ### 1. Secure Code Review Assistant
475
-
476
- Ask the model to review code for security vulnerabilities:
477
-
478
- ```python
479
- prompt = """### User:
480
- Review this authentication code for security issues:
481
-
482
- @app.route('/login', methods=['POST'])
483
- def login():
484
- username = request.form['username']
485
- password = request.form['password']
486
- query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
487
- user = db.execute(query).fetchone()
488
- if user:
489
- session['user_id'] = user['id']
490
- return redirect('/dashboard')
491
- return 'Invalid credentials'
492
-
493
- ### Assistant:
494
- """
495
- ```
496
-
497
- **Model Response:** Identifies SQL injection, plain-text passwords, missing rate limiting, session fixation risks, and provides secure alternatives.
498
-
499
- ---
500
-
501
- ### 2. Security-Aware Code Generation
502
-
503
- Generate implementations that are secure by default:
504
-
505
- ```python
506
- prompt = """### User:
507
- Write a secure REST API endpoint for user registration with proper input validation, password hashing, and rate limiting in Python Flask.
508
-
509
- ### Assistant:
510
- """
511
- ```
512
-
513
- **Model Response:** Generates production-ready code with bcrypt hashing, input validation, rate limiting, CSRF protection, and security headers.
514
-
515
- ---
516
-
517
- ### 3. Vulnerability Explanation & Exploitation
518
-
519
- Understand attack vectors and exploitation:
520
-
521
- ```python
522
- prompt = """### User:
523
- Explain how SSRF attacks work and show me a concrete example in Python with defense strategies.
524
-
525
- ### Assistant:
526
- """
527
- ```
528
-
529
- **Model Response:** Provides vulnerable code, attack demonstration, exploitation payload, and comprehensive defense-in-depth remediation.
530
-
531
- ---
532
-
533
- ### 4. Production Security Guidance
534
-
535
- Get operational security recommendations:
536
-
537
- ```python
538
- prompt = """### User:
539
- How do I implement secure session management for a Flask application with 10,000 concurrent users?
540
-
541
- ### Assistant:
542
- """
543
- ```
544
-
545
- **Model Response:** Covers Redis session storage, secure cookie configuration, session rotation, timeout policies, SIEM integration, and monitoring.
546
-
547
- ---
548
-
549
- ### 5. Developer Training
550
-
551
- Use as an interactive security training tool for development teams:
552
-
553
- ```python
554
- prompt = """### User:
555
- Our team is building a new payment processing API. What are the top 5 security concerns we should address first?
556
-
557
- ### Assistant:
558
- """
559
- ```
560
-
561
- **Model Response:** Prioritized security checklist with implementation guidance specific to payment processing.
562
-
563
- ---
564
-
565
- ## ⚠️ Limitations & Transparency
566
-
567
- ### What This Model Does Well
568
- ✅ Identifies common security vulnerabilities in code (OWASP Top 10)
569
- ✅ Generates secure implementations for standard patterns
570
- ✅ Explains attack vectors with concrete examples
571
- ✅ Provides defense-in-depth operational guidance
572
- ✅ Runs on consumer hardware (8GB+ RAM)
573
- ✅ Fast inference for IDE integration
574
-
575
- ### What This Model Doesn't Do
576
- ❌ **Not a security scanner** - Use tools like Semgrep, CodeQL, or Snyk for automated scanning
577
- ❌ **Not a penetration testing tool** - Cannot discover novel 0-days or perform active exploitation
578
- ❌ **Not legal/compliance advice** - Consult security professionals for regulatory requirements
579
- ❌ **Not a replacement for security experts** - Critical systems should undergo professional security review
580
- ❌ **Not trained on proprietary vulnerabilities** - Only public CVEs and documented breaches
581
-
582
- ### Known Issues & Constraints
583
- - **Verbose responses:** Model was trained on detailed security explanations, may generate longer responses than needed
584
- - **Common patterns only:** Best suited for OWASP Top 10 and common vulnerability patterns, not novel attack vectors
585
- - **Context limitations:** 4K context window limits analysis of very large files (use chunking for large codebases)
586
- - **Small model trade-offs:** 3B parameters means reduced reasoning capability vs 13B+ models
587
- - **No real-time threat intelligence:** Training data frozen at Dec 2024, doesn't include 2025+ CVEs
588
-
589
- ### Appropriate Use
590
- ✅ Development assistance and education
591
- ✅ Pre-commit security checks
592
- ✅ Training and knowledge sharing
593
- ✅ Prototype security review
594
-
595
- ### Inappropriate Use
596
- ❌ Sole security validation for production systems
597
- ❌ Replacement for professional security audits
598
- ❌ Compliance certification validation
599
- ❌ Active penetration testing or exploitation
600
-
601
- ---
602
-
603
- ## 🔬 Dataset Information
604
-
605
- This model was trained on **[SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2)**, a production-grade security dataset with:
606
-
607
- - **1,209 total examples** (841 train / 175 validation / 193 test)
608
- - **100% incident grounding** - every example tied to real CVEs or security breaches
609
- - **11 vulnerability categories** - complete OWASP Top 10:2025 coverage
610
- - **11 programming languages** - from Python to Rust
611
- - **4-turn conversational structure** - mirrors real developer-AI workflows
612
- - **100% expert validation** - reviewed by independent security professionals
613
-
614
- ### Dataset Methodology
615
-
616
- **Incident Mining Process:**
617
- 1. CVE database analysis (2015-2024)
618
- 2. Security incident reports (breaches, bug bounties)
619
- 3. OWASP, MITRE, and security research papers
620
- 4. Real-world exploitation examples
621
-
622
- **Quality Assurance:**
623
- - Expert security review (every example)
624
- - CVE-aware train/validation/test split (no overlap)
625
- - Multi-LLM synthesis (Claude Sonnet 4.5, GPT-4, Llama 3.2)
626
- - Attack demonstration validation (tested exploits)
627
-
628
- **Key Dataset Features:**
629
- - Real-world incident references (Equifax, Capital One, SolarWinds, LastPass)
630
- - Concrete attack demonstrations with exploit payloads
631
- - Production operational guidance (SIEM, logging, monitoring)
632
- - Defense-in-depth security controls
633
- - Language-specific idioms and frameworks
634
-
635
- See the [full dataset card](https://huggingface.co/datasets/scthornton/securecode-v2) and [research paper](https://perfecxion.ai/articles/securecode-v2-dataset-paper.html) for complete details.
636
-
637
- ---
638
-
639
- ## 🏢 About perfecXion.ai
640
-
641
- [perfecXion.ai](https://perfecxion.ai) is dedicated to advancing AI security through research, datasets, and production-grade security tooling. Our mission is to ensure AI systems are secure by design.
642
-
643
- **Our Work:**
644
- - 🔬 **Security research** on AI/ML vulnerabilities and adversarial attacks
645
- - 📊 **Open-source datasets** (SecureCode, GuardrailReduction, PromptInjection)
646
- - 🛠️ **Production tools** for AI security testing and validation
647
- - 🎓 **Developer education** and security training resources
648
- - 📝 **Research publications** on AI security best practices
649
-
650
- **Research Focus:**
651
- - Prompt injection and jailbreak detection
652
- - LLM security guardrails and safety systems
653
- - RAG poisoning and retrieval vulnerabilities
654
- - AI agent security and agentic AI risks
655
- - Adversarial ML and model robustness
656
-
657
- **Connect:**
658
- - Website: [perfecxion.ai](https://perfecxion.ai)
659
- - Research: [perfecxion.ai/research](https://perfecxion.ai/research)
660
- - Knowledge Hub: [perfecxion.ai/knowledge](https://perfecxion.ai/knowledge)
661
- - GitHub: [@scthornton](https://github.com/scthornton)
662
- - HuggingFace: [@scthornton](https://huggingface.co/scthornton)
663
- - Email: scott@perfecxion.ai
664
-
665
- ---
666
-
667
- ## 📄 License
668
-
669
- **Model License:** Apache 2.0 (permissive - use in commercial applications)
670
- **Dataset License:** CC BY-NC-SA 4.0 (non-commercial with attribution)
671
-
672
- This model's weights are released under Apache 2.0, allowing commercial use. The training dataset (SecureCode v2.0) is CC BY-NC-SA 4.0, restricting commercial use of the raw data.
673
-
674
- ### What You CAN Do
675
- ✅ Use this model commercially in production applications
676
- ✅ Fine-tune further for your specific use case
677
- ✅ Deploy in enterprise environments
678
- ✅ Integrate into commercial products
679
- ✅ Distribute and modify the model weights
680
- ✅ Charge for services built on this model
681
-
682
- ### What You CANNOT Do with the Dataset
683
- ❌ Sell or redistribute the raw SecureCode v2.0 dataset commercially
684
- ❌ Use the dataset to train commercial models without releasing under the same license
685
- ❌ Remove attribution or claim ownership of the dataset
686
-
687
- For commercial dataset licensing or custom training, contact: scott@perfecxion.ai
688
-
689
- ---
690
-
691
- ## 📚 Citation
692
-
693
- If you use this model in your research or applications, please cite:
694
-
695
- ```bibtex
696
- @misc{thornton2025securecode-llama3b,
697
- title={Llama 3.2 3B - SecureCode Edition},
698
- author={Thornton, Scott},
699
- year={2025},
700
- publisher={perfecXion.ai},
701
- url={https://huggingface.co/scthornton/llama-3.2-3b-securecode},
702
- note={Fine-tuned on SecureCode v2.0: https://huggingface.co/datasets/scthornton/securecode-v2}
703
- }
704
-
705
- @misc{thornton2025securecode-dataset,
706
- title={SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models},
707
- author={Thornton, Scott},
708
- year={2025},
709
- month={January},
710
- publisher={perfecXion.ai},
711
- url={https://perfecxion.ai/articles/securecode-v2-dataset-paper.html},
712
- note={Dataset: https://huggingface.co/datasets/scthornton/securecode-v2}
713
- }
714
- ```
715
-
716
- ---
717
-
718
- ## 🙏 Acknowledgments
719
-
720
- - **Meta AI** for the excellent Llama 3.2 base model and open-source commitment
721
- - **OWASP Foundation** for maintaining the Top 10 vulnerability taxonomy
722
- - **MITRE Corporation** for the CVE database and vulnerability research
723
- - **Security research community** for responsible disclosure practices that enabled this dataset
724
- - **Hugging Face** for model hosting and inference infrastructure
725
- - **Independent security reviewers** who validated dataset quality
726
-
727
- ---
728
-
729
- ## 🤝 Contributing
730
-
731
- Found a security issue or have suggestions for improvement?
732
-
733
- - 🐛 **Report issues:** [GitHub Issues](https://github.com/scthornton/securecode-models/issues)
734
- - 💬 **Discuss improvements:** [HuggingFace Discussions](https://huggingface.co/scthornton/llama-3.2-3b-securecode/discussions)
735
- - 📧 **Contact:** scott@perfecxion.ai
736
-
737
- ### Community Contributions Welcome
738
-
739
- Especially interested in:
740
- - **Security benchmark evaluations** on industry-standard datasets
741
- - **Production deployment case studies** showing real-world impact
742
- - **Integration examples** with popular frameworks (LangChain, AutoGen, CrewAI)
743
- - **Vulnerability detection accuracy** assessments
744
- - **Performance optimization** techniques for specific hardware
745
-
746
- ---
747
-
748
- ## 🔗 SecureCode Model Collection
749
-
750
- Explore other SecureCode fine-tuned models optimized for different use cases:
751
-
752
- ### Entry-Level Models (3-7B)
753
- - **[llama-3.2-3b-securecode](https://huggingface.co/scthornton/llama-3.2-3b-securecode)** ⭐ (YOU ARE HERE)
754
- - **Best for:** Consumer hardware, IDE integration, education
755
- - **Hardware:** 8GB RAM minimum
756
- - **Unique strength:** Most accessible
757
-
758
- - **[deepseek-coder-6.7b-securecode](https://huggingface.co/scthornton/deepseek-coder-6.7b-securecode)**
759
- - **Best for:** Security-optimized baseline
760
- - **Hardware:** 16GB RAM
761
- - **Unique strength:** Security-first architecture
762
-
763
- - **[qwen2.5-coder-7b-securecode](https://huggingface.co/scthornton/qwen2.5-coder-7b-securecode)**
764
- - **Best for:** Best code understanding in 7B class
765
- - **Hardware:** 16GB RAM
766
- - **Unique strength:** 128K context, best-in-class
767
-
768
- - **[codegemma-7b-securecode](https://huggingface.co/scthornton/codegemma-7b-securecode)**
769
- - **Best for:** Google ecosystem, instruction following
770
- - **Hardware:** 16GB RAM
771
- - **Unique strength:** Google brand, strong completion
772
-
773
- ### Mid-Range Models (13-15B)
774
- - **[codellama-13b-securecode](https://huggingface.co/scthornton/codellama-13b-securecode)**
775
- - **Best for:** Enterprise trust, Meta brand
776
- - **Hardware:** 24GB RAM
777
- - **Unique strength:** Proven track record
778
-
779
- - **[qwen2.5-coder-14b-securecode](https://huggingface.co/scthornton/qwen2.5-coder-14b-securecode)**
780
- - **Best for:** Advanced code analysis
781
- - **Hardware:** 32GB RAM
782
- - **Unique strength:** 128K context window
783
-
784
- - **[starcoder2-15b-securecode](https://huggingface.co/scthornton/starcoder2-15b-securecode)**
785
- - **Best for:** Multi-language projects (600+ languages)
786
- - **Hardware:** 32GB RAM
787
- - **Unique strength:** Broadest language support
788
-
789
- ### Enterprise-Scale Models (20B+)
790
- - **[granite-20b-code-securecode](https://huggingface.co/scthornton/granite-20b-code-securecode)**
791
- - **Best for:** Enterprise-scale, IBM trust
792
- - **Hardware:** 48GB RAM
793
- - **Unique strength:** Largest model, enterprise compliance
794
-
795
- **View Complete Collection:** [SecureCode Models](https://huggingface.co/collections/scthornton/securecode)
796
-
797
- ---
798
-
799
- <div align="center">
800
-
801
- **Built with ❤️ for secure software development**
802
-
803
- [perfecXion.ai](https://perfecxion.ai) | [Research](https://perfecxion.ai/research) | [Knowledge Hub](https://perfecxion.ai/knowledge) | [Contact](mailto:scott@perfecxion.ai)
804
-
805
- ---
806
 
807
- *Defending code, one model at a time*
808
 
809
- </div>
 
 
 
 
 
1
  ---
2
+ library_name: peft
3
+ license: llama3.2
4
  base_model: meta-llama/Llama-3.2-3B-Instruct
5
  tags:
6
+ - base_model:adapter:meta-llama/Llama-3.2-3B-Instruct
7
+ - lora
8
+ - transformers
 
 
 
 
 
 
 
 
 
9
  pipeline_tag: text-generation
10
+ model-index:
11
+ - name: llama-3.2-3b-securecode
12
+ results: []
13
  ---
14
 
15
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
16
+ should probably proofread and complete it, then remove this comment. -->
17
 
18
+ # llama-3.2-3b-securecode
19
 
20
+ This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
 
 
 
21
 
22
+ ## Model description
23
 
24
+ More information needed
25
 
26
+ ## Intended uses & limitations
27
 
28
+ More information needed
29
 
30
+ ## Training and evaluation data
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31
 
32
+ More information needed
33
 
34
+ ## Training procedure
 
 
 
 
35
 
36
+ ### Training hyperparameters
 
 
 
37
 
38
+ The following hyperparameters were used during training:
39
+ - learning_rate: 0.0002
40
+ - train_batch_size: 4
41
+ - eval_batch_size: 8
42
+ - seed: 42
43
+ - gradient_accumulation_steps: 4
44
+ - total_train_batch_size: 16
45
+ - optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
46
+ - lr_scheduler_type: cosine
47
+ - lr_scheduler_warmup_steps: 100
48
+ - num_epochs: 3
49
 
50
+ ### Training results
 
 
 
51
 
 
52
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
53
 
54
+ ### Framework versions
55
 
56
+ - PEFT 0.18.1
57
+ - Transformers 5.1.0
58
+ - Pytorch 2.7.1+cu128
59
+ - Datasets 2.21.0
60
+ - Tokenizers 0.22.2
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1556
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1564
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1594
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1596
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1600
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1602
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1604
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1612
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1613
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1615
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1618
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1620
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1626
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1628
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1631
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1634
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1636
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1639
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1644
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1645
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1650
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1652
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1653
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1660
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1676
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1690
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1698
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1700
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1716
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1717
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1720
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1722
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1726
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1727
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1730
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1731
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1732
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1733
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1734
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1735
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1737
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1738
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1739
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1740
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1741
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1748
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1753
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1754
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1755
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1756
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1757
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1758
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1759
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1760
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1761
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1762
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1763
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1764
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1765
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1766
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1767
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1768
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1769
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1770
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1771
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1772
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1773
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1774
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1775
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1776
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1777
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1778
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1779
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1780
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1781
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1782
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1783
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1784
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1785
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1786
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1787
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1788
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1789
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1790
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1791
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1792
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1793
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1794
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1795
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1796
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1797
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1798
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1799
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1800
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1801
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1802
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1803
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1804
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1805
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1807
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1809
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1810
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1811
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1812
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1813
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1814
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1815
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1816
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1817
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1818
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1819
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1820
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1821
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1822
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1823
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1824
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1825
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1826
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1827
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1828
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1829
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1830
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1831
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1832
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1833
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1834
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1835
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1836
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1837
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1839
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1840
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1841
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1842
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1850
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1852
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1853
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1854
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1855
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1856
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1857
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1858
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1859
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1860
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1861
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1862
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1863
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1864
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1865
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1866
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1867
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1868
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1869
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1870
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1871
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1873
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1874
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1876
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1877
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1879
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1880
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1881
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1882
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1883
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1884
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1885
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1886
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1887
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1888
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1889
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1890
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1891
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1892
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1893
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1894
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1895
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1896
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1897
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1898
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1899
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1900
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1901
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1902
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1903
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1904
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1905
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1906
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1907
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1908
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1909
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1910
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1911
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1912
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1913
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1914
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1915
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1916
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1917
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1918
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1919
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1920
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1921
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1922
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1923
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1924
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1925
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1926
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1927
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1928
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1929
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1930
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1931
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1932
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1933
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1934
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1935
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1936
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1937
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1938
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1939
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1940
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1941
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1942
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1943
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1944
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1945
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1946
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1947
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1948
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1949
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1950
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1951
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1952
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1953
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1954
- },
1955
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1956
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1957
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1958
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1959
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1960
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1961
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1962
- },
1963
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1964
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1965
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1966
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1967
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1968
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1969
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1970
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1971
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1972
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1973
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1974
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1975
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1976
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1977
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1978
- },
1979
- "128247": {
1980
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1981
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1982
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1983
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1984
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1985
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1986
- },
1987
- "128248": {
1988
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1989
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1990
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1991
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1992
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1993
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1994
- },
1995
- "128249": {
1996
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1997
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1998
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1999
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2000
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2001
- "special": true
2002
- },
2003
- "128250": {
2004
- "content": "<|reserved_special_token_242|>",
2005
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2006
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2007
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2008
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2009
- "special": true
2010
- },
2011
- "128251": {
2012
- "content": "<|reserved_special_token_243|>",
2013
- "lstrip": false,
2014
- "normalized": false,
2015
- "rstrip": false,
2016
- "single_word": false,
2017
- "special": true
2018
- },
2019
- "128252": {
2020
- "content": "<|reserved_special_token_244|>",
2021
- "lstrip": false,
2022
- "normalized": false,
2023
- "rstrip": false,
2024
- "single_word": false,
2025
- "special": true
2026
- },
2027
- "128253": {
2028
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2029
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2030
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2031
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2032
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2033
- "special": true
2034
- },
2035
- "128254": {
2036
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2037
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2038
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2039
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2040
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2041
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2042
- },
2043
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2044
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2045
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2046
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2047
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2048
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2049
- "special": true
2050
- }
2051
- },
2052
  "bos_token": "<|begin_of_text|>",
2053
  "clean_up_tokenization_spaces": true,
2054
  "eos_token": "<|eot_id|>",
2055
- "extra_special_tokens": {},
2056
  "model_input_names": [
2057
  "input_ids",
2058
  "attention_mask"
2059
  ],
2060
  "model_max_length": 131072,
2061
  "pad_token": "<|eot_id|>",
2062
- "tokenizer_class": "PreTrainedTokenizerFast"
2063
  }
 
1
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