From bd7228d2fc77a776c4adafeb202df712c8000618 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Fri, 10 Jul 2026 16:59:18 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: nareshmeena12/fluxion-370m-instruct Source: Original Platform --- .gitattributes | 35 + README.md | 364 + chat_template.jinja | 15 + config.json | 17 + model.safetensors | 3 + tokenizer.json | 249126 +++++++++++++++++++++++++++++++++++++++ tokenizer_config.json | 11 + 7 files changed, 249571 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 model.safetensors create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..c059304 --- /dev/null +++ b/README.md @@ -0,0 +1,364 @@ +--- +library_name: transformers +tags: +- code +- math +- text-generation-inference +- slm +- instructmodel +license: apache-2.0 +language: +- en +--- + +# ⚡ Fluxion-370M-Instruct + +### *Trained on 0.227T tokens. Competing with models trained on 28T.* + +
+ +[![arXiv](https://img.shields.io/badge/arXiv-coming%20soon-b31b1b?style=flat-square&logo=arxiv)]() +[![HuggingFace](https://img.shields.io/badge/🤗%20Model-nareshmeena12%2Ffluxion--370m--instruct-yellow?style=flat-square)](https://huggingface.co/nareshmeena12/fluxion-370m-instruct) +[![GitHub](https://img.shields.io/badge/GitHub-Code-black?style=flat-square&logo=github)]() +[![License](https://img.shields.io/badge/License-Apache%202.0-blue?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0) + +
+ +![Math and Code Benchmarks](https://github.com/nareshmeena12/fluxion-370m-instruct/blob/main/assests/math%20and%20code%20benchmarks.png) + +
+ +--- + +## What is Fluxion? + +**Fluxion-370M-Instruct** is a 370M-parameter instruction-tuned language model built from scratch, focused on **mathematical reasoning and code generation**. Its core design philosophy is simple: *do more with less*. + +Trained on just **227B tokens** — a fraction of what comparable models consume — Fluxion achieves competitive performance on math and coding benchmarks against models that have seen **18–28× more data**. On token efficiency (MATH-500 score per trillion training tokens), Fluxion ranks **1st** across all models in its class by a dominant margin. + +The model went through three training stages: large-scale pretraining, supervised fine-tuning for instruction following, and GRPO reinforcement learning specifically targeting mathematical reasoning with rule-based rewards. + +--- + +## ⚡ Token Efficiency — The Core Story + +| Model | Training Tokens | MATH-500 | **MATH-500 / Trillion Tokens** | +|:---|:---:|:---:|:---:| +| **Fluxion-370M** | **0.227T** | **17.40%** | **🥇 76.7×** | +| MobileLLM-R1-360M | 4.2T | 25.60% | 6.1× | +| Gemma3-270M | 6T | 8.20% | 1.4× | +| Granite-350M | 11T | 19.20% | 1.7× | +| Qwen2.5-0.5B | 18T | 29.80% | 1.7× | +| LFM2.5-350M | 28T | 13.40% | 0.5× | +| SmolLM2-360M | 4T | 3.40% | 0.9× | + +**Fluxion delivers 76.7× higher MATH-500 score per trillion training tokens than any other model in this comparison.** This is a structural advantage from architecture choices, custom tokenization, and a high-signal data pipeline — not a measurement artifact. + +--- + +## 🏗️ Architecture + +Fluxion is a clean, modern decoder-only transformer. No hybrid layers, no custom operators — just well-chosen standard components assembled deliberately. + +``` +FluxionForCausalLM +├── Embedding (vocab_size=50,000 · hidden_size=1024) +├── 26 × DecoderBlock +│ ├── RMSNorm (ε = 1e-6) +│ ├── GroupedQueryAttention +│ │ ├── 16 Query heads · 8 KV heads (GQA 2:1) +│ │ └── RoPE (θ = 500,000) + NTK scaling +│ ├── RMSNorm (ε = 1e-6) +│ └── SwiGLU FFN (hidden → 3072 → hidden) +└── RMSNorm → LM Head (weight-tied to embedding) +``` + +### Hyperparameters + +| Parameter | Value | +|:---|:---:| +| **Total Parameters** | ~370M | +| **Layers** | 26 | +| **Hidden Size** | 1024 | +| **FFN Intermediate Size** | 3072 | +| **Attention Heads (Q)** | 16 | +| **KV Heads (GQA)** | 8 | +| **GQA Group Ratio** | 2 : 1 | +| **Head Dimension** | 64 | +| **Context Length** | 4096 | +| **Vocabulary Size** | 50,000 | +| **RoPE Base θ** | 500,000 | +| **RoPE Scaling** | NTK | +| **Normalization** | RMSNorm (ε = 1e-6) | +| **Activation** | SwiGLU | +| **Attention** | Flash Attention | +| **Weight Tying** | Yes (embed ↔ lm\_head) | +| **Attention Bias** | No | +| **FFN Bias** | No | +| **dtype** | bfloat16 | + +### Design Choices & Rationale + +**Grouped Query Attention (16Q / 8KV):** Halves KV cache memory vs standard MHA with no meaningful quality loss at this scale. Larger effective batch sizes at both training and inference time. + +**RoPE with θ = 500,000 + NTK scaling:** High base frequency improves length generalisation beyond the training window. NTK scaling further stabilises attention patterns at longer sequences without fine-tuning. + +**SwiGLU activation:** Consistently stronger than GeLU/ReLU on reasoning tasks. The gated structure gives the FFN richer expressiveness per parameter. + +**Custom BPE tokenizer (50k vocab):** Trained from scratch on 100GB of domain-focused text — a curated subset of the pretraining corpus weighted toward mathematics, code, and English prose. A smaller, domain-tuned vocabulary means fewer tokens per math expression and code snippet, directly improving effective context utilisation and reducing sequence lengths vs generic 128k tokenizers. + +**Weight tying (embed ↔ lm\_head):** Saves ~50M parameters, keeps the model compact, and forces the input and output representation spaces to align. + +**Flash Attention:** Full Flash Attention used throughout training for memory efficiency and throughput. TF32 enabled for matrix multiplications. + +--- + +## 📊 Benchmarks + +All results are **0-shot** unless noted. Evaluation is strict. + +### Math & Code + +| Model | Tokens | GSM8K | MATH-500 | HumanEval | MBPP | **Avg** | +|:---|:---:|:---:|:---:|:---:|:---:|:---:| +| Qwen2.5-0.5B | 18T | 43.44% | 29.80% | 30.49% | 38.91% | 35.66% | +| Granite-350M | 11T | 31.69% | 19.20% | 29.88% | 42.02% | 30.70% | +| LFM2.5-350M | 28T | 32.68% | 13.40% | 10.37% | 5.45% | 15.48% | +| MobileLLM-R1-360M | 4.2T | 23.81% | 25.60% | 19.51% | 17.51% | 21.61% | +| **Fluxion-370M** | **0.227T** | **29.57%** | **17.40%** | **16.46%** | **20.23%** | **20.92%** | +| SmolLM2-360M | 4T | 8.87% | 3.40% | 17.68% | 35.02% | 16.24% | +| Gemma3-270M | 6T | 7.58% | 8.20% | 11.59% | 19.07% | 11.61% | + +### General Reasoning + +| Model | ARC | PIQA | OBQA | CSQA | MMLU | Wino | IFEval | **Avg** | +|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| +| Qwen2.5-0.5B | 50.9% | 70.1% | 36.2% | 42.3% | 45.9% | 51.1% | 74.1% | 52.9% | +| Granite-350M | 51.8% | 68.9% | 37.8% | 40.5% | 33.8% | 53.8% | 83.2% | 52.8% | +| LFM2.5-350M | 49.4% | 67.0% | 31.6% | 33.4% | 42.2% | 52.0% | 88.3% | 52.0% | +| SmolLM2-360M | 45.9% | 71.5% | 35.2% | 29.0% | 24.9% | 54.8% | 77.5% | 48.4% | +| Gemma3-270M | 43.7% | 66.6% | 30.6% | 36.9% | 24.7% | 49.7% | 71.5% | 46.2% | +| MobileLLM-R1-360M | 39.1% | 58.6% | 26.2% | 27.6% | 26.7% | 50.8% | 71.2% | 42.9% | +| **Fluxion-370M** | **36.3%** | **61.6%** | **29.4%** | **27.3%** | **25.3%** | **51.9%** | **66.9%** | **42.7%** | + +### Rankings Summary + +| Category | Score | Rank | +|:---|:---:|:---:| +| Math Average (GSM8K + MATH-500) | 23.49% | **4th** | +| Code Average (HumanEval + MBPP) | 18.35% | **5th** | +| Math + Code Combined | 20.92% | **4th** | +| General Average | 39.19% | 7th | +| **Token Efficiency (MATH-500 / T tokens)** | **76.7×** | **🥇 1st** | + +> Fluxion's design target is math and code under tight data constraints. On general benchmarks like IFEval and MMLU it trails models trained on significantly more diverse data — this is expected and intentional. On token efficiency it has no close competitor. + +--- + +## 💬 Chat Template & Tokenizer + +Fluxion-Instruct uses the **ChatML** format: + +``` +<|im_start|>system +You are a helpful assistant.<|im_end|> +<|im_start|>user +{your prompt here}<|im_end|> +<|im_start|>assistant +``` + +### Special Tokens + +| Token | ID | Role | +|:---|:---:|:---| +| `<\|im_start\|>` | 100278 | Turn start marker | +| `<\|im_end\|>` | 100279 | Turn end marker | +| `<\|pad\|>` | — | Padding token | + +### Stopping Token IDs + +Always pass `eos_token_id=[1, 4]` during generation. Without these, the model may continue generating beyond the intended response boundary: + +```python +output = model.generate( + **inputs, + eos_token_id=[1, 4], + ... +) +``` + +Token ID `1` is the primary EOS token and token ID `4` is the `<|im_end|>` marker. Including both ensures clean turn termination in all inference scenarios. + +### Tokenizer + +Fluxion uses a **custom BPE tokenizer** trained from scratch on ~100GB of domain-focused text — a curated subset of the pretraining corpus weighted toward mathematics, code, and English prose. With a **50,000 token vocabulary**, it is significantly more compact than general-purpose tokenizers (32k–128k). This means: + +- Fewer tokens per math expression and code snippet +- Better effective context utilisation within the 4096 token window +- Faster embedding lookups and a smaller lm\_head projection + +--- + +## 🚀 Quick Start + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + +model = AutoModelForCausalLM.from_pretrained( + "nareshmeena12/fluxion-370m-instruct", + torch_dtype=torch.bfloat16, + device_map="auto", +) +tokenizer = AutoTokenizer.from_pretrained( + "nareshmeena12/fluxion-370m-instruct" +) + +messages = [ + {"role": "system", "content": "You are a helpful assistant that solves math problems step by step."}, + {"role": "user", "content": "What is the sum of the first 100 natural numbers?"} +] + +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) +inputs = tokenizer(text, return_tensors="pt").to(model.device) + +with torch.no_grad(): + output = model.generate( + **inputs, + max_new_tokens=512, + eos_token_id=[1, 4], + do_sample=True, + top_p=0.9, + ) + +response = tokenizer.decode( + output[0, inputs["input_ids"].shape[1]:], + skip_special_tokens=True +) +print(response) +``` + + + +> **Note:** Always include `eos_token_id=[1, 4]` in all `generate()` calls regardless of quantization mode. + +--- + +## 🗂️ Training + +Fluxion was trained in three stages. + +### Stage 1 — Pretraining + +Large-scale unsupervised pretraining on a curated mix of math, code, and English text. + +| Parameter | Value | +|:---|:---:| +| **Total Tokens** | ~227B (0.227T) | +| **Steps** | 264,000 | +| **Batch Size** | 10 sequences | +| **Gradient Accumulation** | 21 steps | +| **Effective Batch** | ~860K tokens/step | +| **Learning Rate** | 3e-4 | +| **Min Learning Rate** | 9e-5 | +| **LR Schedule** | Cosine decay | +| **Warmup Steps** | 2,000 | +| **Sequence Length** | 4,096 | +| **Optimizer** | AdamW | +| **Precision** | bfloat16 + TF32 | +| **Attention** | Flash Attention | +| **Positional Encoding** | RoPE (θ=500k) + NTK scaling | + +**Pretraining data:** *(to be added)* + +### Stage 2 — Supervised Fine-Tuning (SFT) + +Instruction tuning using ChatML format to teach the model to follow user instructions across math, code, and general tasks. + +| Parameter | Value | +|:---|:---:| +| **Format** | ChatML | +| **Data** | *(to be added)* | + +### Stage 3 — GRPO (Math Reasoning) + +Group Relative Policy Optimisation applied specifically to strengthen mathematical reasoning. + +| Parameter | Value | +|:---|:---:| +| **Algorithm** | GRPO | +| **Reward Signal** | Rule-based answer correctness | +| **Target Domain** | Mathematics | +| **Data** | *(to be added)* | + +Rule-based rewards verify final answer correctness against ground truth — no LLM judge, no model-in-the-loop. This keeps the reward signal clean, fast, and free from reward hacking through verbosity or style. + +--- + +## 🎯 Intended Use + +**Fluxion-370M-Instruct is designed for:** +- Mathematical problem solving and step-by-step reasoning +- Code generation and explanation (Python, general purpose) +- Instruction-following tasks in math and code domains +- Fine-tuning base for domain-specific math/code applications +- Efficient deployment — capable performance on modest hardware +- Research into token-efficient pretraining and RL for reasoning + +**Fluxion-370M-Instruct is NOT designed for:** +- Knowledge-intensive QA requiring broad world knowledge +- Long-document tasks beyond 4096 tokens +- Multilingual use cases +- Safety-critical production deployments without additional alignment + +--- + +## ⚠️ Limitations + +- **Narrow training data scope.** 0.227T tokens is intentionally small and domain-focused. The model has limited world knowledge outside math, code, and general English. +- **Context length is 4096 tokens.** Not suitable for long-document tasks without chunking strategies. +- **General benchmarks lag.** IFEval (66.9%) and MMLU (25.3%) reflect that the model was not optimised for instruction following diversity or broad knowledge recall — this is a deliberate trade-off. +- **Custom tokenizer.** The 50k BPE vocabulary is domain-tuned. Rare tokens, non-English text, and highly specialised symbols outside the training distribution may tokenise suboptimally. +- **Small scale.** At 370M parameters, the model is outperformed by larger models on complex multi-step reasoning chains requiring deep world knowledge. +- **Always set `eos_token_id=[1, 4]`.** Without explicit stopping token IDs the model may continue generating past the intended response boundary. + +--- + +## 📰 News + +- `` — Fluxion-370M-Instruct released on Hugging Face +- `` — Training code released on GitHub +- `` — Technical report available on arXiv + +--- + +## 📄 Citation + +```bibtex +@misc{fluxion2025, + title = {Fluxion-370M: Token-Efficient Pretraining and Reinforcement Learning for Mathematical and Code Reasoning}, + author = {Meena, Naresh}, + year = {2025}, + note = {} +} +``` + +--- + +## 📬 Contact + + + +--- + +
+ +*Less data. Less compute. More signal.* + +**[🤗 Model](https://huggingface.co/nareshmeena12/fluxion-370m-instruct) · [💻 Code]() · [📄 Paper]()** + +
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"dele": 17382, + "Ġfellow": 17383, + "ĠRussian": 17384, + "ĠIntelligence": 17385, + "Ġtomat": 17386, + "Ġchapters": 17387, + "ĠBul": 17388, + "ĠPE": 17389, + "Ġmult": 17390, + "Ġpreprocessed": 17391, + "Ġpressing": 17392, + "pired": 17393, + "Ġcrusher": 17394, + "Ġtroublesh": 17395, + "contains": 17396, + "recipe": 17397, + "Router": 17398, + "storm": 17399, + "losses": 17400, + "Ġwage": 17401, + "Ġbowl": 17402, + "Ġfulfilling": 17403, + "Ġtutorials": 17404, + "Ġoverlooked": 17405, + "........": 17406, + "Ġgenerators": 17407, + "previous": 17408, + "unctions": 17409, + "elery": 17410, + "USD": 17411, + "BI": 17412, + "Carol": 17413, + "ĠIntegral": 17414, + "Ġintuition": 17415, + "Ġlifting": 17416, + "Ġrestricted": 17417, + "|>": 17418, + "races": 17419, + "rance": 17420, + "ĠTherapy": 17421, + "Ġborrowing": 17422, + "Interest": 17423, + "Ġnest": 17424, + "Ġintellectual": 17425, + "pio": 17426, + "probability": 17427, + "ĠEfficiency": 17428, + "NU": 17429, + "Ġreserved": 17430, + 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17887, + "Ġquartile": 17888, + "Ġgrab": 17889, + "Ġscar": 17890, + "Ġliber": 17891, + "ĠWashington": 17892, + "Ġattrs": 17893, + "Ġrug": 17894, + "/(": 17895, + "recurs": 17896, + "196": 17897, + "ĠLE": 17898, + "ĠContinue": 17899, + "Round": 17900, + "Ġpennies": 17901, + "Ġlegi": 17902, + "Ġcalibration": 17903, + "itud": 17904, + "002": 17905, + "Ġcomo": 17906, + "Ġundergo": 17907, + "setText": 17908, + "Ġtran": 17909, + "Ġsuffering": 17910, + "scene": 17911, + "Ġê²½ìļ°": 17912, + "Ġdownloading": 17913, + "对象": 17914, + "Ðļ": 17915, + "Ġsparse": 17916, + "Ġautism": 17917, + "setLevel": 17918, + "ĠRegarding": 17919, + "umes": 17920, + "tc": 17921, + "Sequence": 17922, + "asant": 17923, + "Ġenriching": 17924, + "genre": 17925, + "Ġclicked": 17926, + "Ġ196": 17927, + "Ġgeneralized": 17928, + "ensing": 17929, + "Light": 17930, + "assets": 17931, + "capture": 17932, + "Ġspecifying": 17933, + "ĠInstall": 17934, + "Ġpsychology": 17935, + "Ġancest": 17936, + "ĠLLC": 17937, + "IZ": 17938, + "Ġvulnerabilities": 17939, + "Ġcouples": 17940, + "Ġglucose": 17941, + "LENGTH": 17942, + "ĠSteve": 17943, + "Ġmystery": 17944, + "Ġpx": 17945, + "Ġshouldn": 17946, + "Ġinterviews": 17947, + "Screen": 17948, + "ograp": 17949, + "Invest": 17950, + "ĠPercentage": 17951, + "åŁ": 17952, + "ĠAzure": 17953, + "Ġfats": 17954, + "}-{": 17955, + "Ġobstacle": 17956, + "ĠBur": 17957, + "ĠLower": 17958, + "é»": 17959, + "arium": 17960, + "Ġpalette": 17961, + "ĠRequired": 17962, + "ĠPubl": 17963, + "Ġattachment": 17964, + "Ġkilometer": 17965, + "Ġformulation": 17966, + "Ġchest": 17967, + "orce": 17968, + "è¨": 17969, + "Ġteles": 17970, + "Have": 17971, + "Ġequip": 17972, + "hydr": 17973, + "Ġspeakers": 17974, + "ÛĮ": 17975, + "252": 17976, + "олиÑĩ": 17977, + "spark": 17978, + "åľ°": 17979, + "Ġempowering": 17980, + "|}": 17981, + "Ġrib": 17982, + "Ġdischarge": 17983, + "wm": 17984, + "focus": 17985, + "Square": 17986, + "ĠSunday": 17987, + "CB": 17988, + "Ġconvergent": 17989, + "Ġspac": 17990, + "олиÑĩеÑģÑĤв": 17991, + "ĠMu": 17992, + "Ġscalars": 17993, + "zeta": 17994, + "Ñĥм": 17995, + "Ġwebpage": 17996, + "foo": 17997, + "Ġlingu": 17998, + "beat": 17999, + "Means": 18000, + "distances": 18001, + "sha": 18002, + "Ġdoctors": 18003, + "ĠOffice": 18004, + "\"<": 18005, + "gun": 18006, + "Ġworse": 18007, + "ĠRew": 18008, + "school": 18009, + "ĠFurther": 18010, + "Ġsuccessive": 18011, + "Ġpink": 18012, + "Ġmas": 18013, + "Ġvalidates": 18014, + "ĠReverse": 18015, + "miss": 18016, + "iters": 18017, + "Character": 18018, + "ĠSpring": 18019, + "elist": 18020, + "human": 18021, + "ĠRoom": 18022, + "Ġclf": 18023, + "umbers": 18024, + "sex": 18025, + "Ġaltering": 18026, + "itives": 18027, + "bye": 18028, + "Ġquad": 18029, + "Ġenvironmentally": 18030, + "ITED": 18031, + "Ġecological": 18032, + "ĠCommit": 18033, + "Ġregulate": 18034, + "Ġunauthorized": 18035, + "Design": 18036, + "adjust": 18037, + "Ġachievement": 18038, + "Ġcloth": 18039, + "ĠConcat": 18040, + "ĠCredit": 18041, + "çĶŁæĪIJ": 18042, + "ĠTI": 18043, + "APP": 18044, + "Ġcultivate": 18045, + "145": 18046, + "Ġlifetime": 18047, + "ĠBlueprint": 18048, + "hop": 18049, + "urday": 18050, + "Ġallure": 18051, + "Ġriddle": 18052, + "Ġinflection": 18053, + "ĠAssistant": 18054, + "Report": 18055, + "Ġobs": 18056, + "è¯į": 18057, + "ĠKen": 18058, + "problems": 18059, + "ĠPractices": 18060, + "Role": 18061, + "osed": 18062, + "ĠGP": 18063, + "DELETE": 18064, + "ĠSAS": 18065, + "ValidationError": 18066, + "echo": 18067, + "2016": 18068, + "å®ŀ": 18069, + "splitext": 18070, + "Ġcelebration": 18071, + "Ġburning": 18072, + "Ġfname": 18073, + "Ġhesitate": 18074, + "amination": 18075, + "Ġelevator": 18076, + "ĠCF": 18077, + "Ġsecuring": 18078, + "itan": 18079, + "ĠÑĩиÑģло": 18080, + "Ġcoal": 18081, + "strar": 18082, + "Ġcoun": 18083, + "longitude": 18084, + "Ġstdin": 18085, + "Ġmodifications": 18086, + "Ġenergies": 18087, + "Ġrecommendation": 18088, + "ĠUUID": 18089, + "rename": 18090, + "discord": 18091, + "ĠÐĺ": 18092, + "CASCADE": 18093, + "Float": 18094, + "ucing": 18095, + "âĪĴ": 18096, + "ë¥ĺ": 18097, + "andas": 18098, + "Ġstomach": 18099, + "ĠRussia": 18100, + "ĠStreamlit": 18101, + "Ġmotivated": 18102, + "counters": 18103, + "Ġnuts": 18104, + "Ġcoordination": 18105, + "capital": 18106, + "Ġenforcement": 18107, + "ĠRab": 18108, + "pins": 18109, + "Ġapproximating": 18110, + "ĠRisk": 18111, + "Ġphones": 18112, + "crop": 18113, + "ĠReady": 18114, + "README": 18115, + "aways": 18116, + "Ġsink": 18117, + "BB": 18118, + "itations": 18119, + "Ġupstream": 18120, + "Ġpathways": 18121, + "GD": 18122, + "Ġimaging": 18123, + "!(": 18124, + "Ġrearranged": 18125, + "apple": 18126, + "Ġadapting": 18127, + "Ġcoord": 18128, + "åıĤæķ°": 18129, + "cb": 18130, + "ĠTurtle": 18131, + "__.": 18132, + "Track": 18133, + "Ġbtn": 18134, + "Ġclassifiers": 18135, + "?\",": 18136, + "Ġsurvive": 18137, + "æį¢": 18138, + "Ġentrepreneur": 18139, + "Ġ1600": 18140, + "Ġcanceling": 18141, + "Ġreproducibility": 18142, + "qa": 18143, + "itize": 18144, + "obby": 18145, + "ĠFre": 18146, + "uer": 18147, + "Ġswapping": 18148, + "Ġconcentrations": 18149, + "BACK": 18150, + "ÃŃa": 18151, + "Ġ...,": 18152, + "ĠTravel": 18153, + "Ġtract": 18154, + "315": 18155, + "Ġderiving": 18156, + "ĠMiss": 18157, + "Ġcaps": 18158, + "Ġleaks": 18159, + "Mass": 18160, + "ĠMaps": 18161, + "ĠLou": 18162, + "Bad": 18163, + "Ġdisable": 18164, + "Ġpt": 18165, + "Sur": 18166, + "Ġhonor": 18167, + "ĠSand": 18168, + "Äĥ": 18169, + "Ġmembership": 18170, + "ĠThreshold": 18171, + "Ġseasons": 18172, + "sync": 18173, + "ĠSkipping": 18174, + "vised": 18175, + "Ġniche": 18176, + "handlers": 18177, + "ĠIntegrating": 18178, + "istan": 18179, + "Ġзап": 18180, + "Ġshortcut": 18181, + "Ġcountless": 18182, + "Ġbricks": 18183, + "culpt": 18184, + "ĠPeriod": 18185, + "Ġrespiratory": 18186, + "ati": 18187, + "Ġå¼": 18188, + "xl": 18189, + "Ġfundamentals": 18190, + "Ġrecurring": 18191, + "à¥Ī": 18192, + "ĠHot": 18193, + "ĠSingle": 18194, + "modified": 18195, + "decorators": 18196, + "Ġdrawings": 18197, + "Ġpints": 18198, + "poon": 18199, + "Ġapproximates": 18200, + "ividuals": 18201, + "Cell": 18202, + "ailure": 18203, + "combined": 18204, + "Copy": 18205, + "Ġthresholds": 18206, + "jpeg": 18207, + "Ġinsects": 18208, + "Sam": 18209, + "ĠпÑĢ": 18210, + "Ġmur": 18211, + "CONT": 18212, + "ilation": 18213, + "Operator": 18214, + "middleware": 18215, + "rinsic": 18216, + "irl": 18217, + "Ġincenti": 18218, + "ĠDifferentiating": 18219, + "CU": 18220, + "Callback": 18221, + "Ġadaptive": 18222, + "nings": 18223, + "ĠCourt": 18224, + "Ġabbre": 18225, + "orporate": 18226, + "Ġconsulting": 18227, + "Ġresidential": 18228, + "uros": 18229, + "Ġbust": 18230, + "kins": 18231, + "Future": 18232, + "experiment": 18233, + "anship": 18234, + "Ġslides": 18235, + "ĮĢ": 18236, + "('<": 18237, + "ë²": 18238, + "divis": 18239, + "Ġdeposits": 18240, + "CalledProcessError": 18241, + "/')": 18242, + "ĠTrack": 18243, + "odings": 18244, + "ographics": 18245, + "Ġplanets": 18246, + "ĠConsiderations": 18247, + "when": 18248, + "ĠNE": 18249, + "KS": 18250, + "ĠSA": 18251, + "Constra": 18252, + "forgettable": 18253, + "ĠStorage": 18254, + "Ġprojections": 18255, + "backward": 18256, + "Ġpist": 18257, + "Ġvillage": 18258, + "Ġstrengthens": 18259, + "人": 18260, + "sound": 18261, + "ĠTraditional": 18262, + "Pal": 18263, + "Ġweapon": 18264, + "ĠCool": 18265, + "Ġexplos": 18266, + "have": 18267, + "greSQL": 18268, + "outine": 18269, + "Ġencrypt": 18270, + "Ġware": 18271, + "ĠPsych": 18272, + "CN": 18273, + "library": 18274, + "vote": 18275, + "jack": 18276, + "į°ìĿ´": 18277, + "Audio": 18278, + "ĠSystemExit": 18279, + "Ġelectromagnetic": 18280, + "ĠDirection": 18281, + "FV": 18282, + "LICENSE": 18283, + "Ġtokenized": 18284, + "raulic": 18285, + "hal": 18286, + "ι": 18287, + "acher": 18288, + "ĠDEF": 18289, + "fly": 18290, + "Sing": 18291, + "conditions": 18292, + "ĠAudio": 18293, + "Ġbehaviour": 18294, + "ĠJewish": 18295, + "Stack": 18296, + "rotation": 18297, + "DEX": 18298, + "frequ": 18299, + "utation": 18300, + "episode": 18301, + "Ġglo": 18302, + "Ġpandemic": 18303, + "NAL": 18304, + "ĠOly": 18305, + "Ġdatabases": 18306, + "Ġbuff": 18307, + "patterns": 18308, + "osome": 18309, + "Ġpencils": 18310, + "POS": 18311, + "calc": 18312, + "Ġparti": 18313, + "pler": 18314, + "Body": 18315, + "omorphism": 18316, + "hole": 18317, + "Ġpursuit": 18318, + "encia": 18319, + "ĠNumer": 18320, + "gif": 18321, + "Ġmetals": 18322, + "ë°": 18323, + "bridge": 18324, + "ĠSpec": 18325, + "tere": 18326, + "Ġinten": 18327, + "Ġtries": 18328, + "Ġmaximizes": 18329, + "Ġdispersion": 18330, + "ĠCL": 18331, + "elestial": 18332, + "ivalent": 18333, + "Mus": 18334, + "dam": 18335, + "rosion": 18336, + "LOCK": 18337, + "ãģ§": 18338, + "complex": 18339, + "dia": 18340, + "Ġjudgment": 18341, + "Thanks": 18342, + "ĠRound": 18343, + "ĠPlant": 18344, + "HA": 18345, + "Ġë²": 18346, + "ĠFoot": 18347, + "Ġimagination": 18348, + "iffs": 18349, + "ĠHeron": 18350, + "Ġskewed": 18351, + "Ġgraphic": 18352, + "Ġorbital": 18353, + "Course": 18354, + "ìłķ": 18355, + "Ġ114": 18356, + "lengths": 18357, + "ĠABCD": 18358, + "Ġbones": 18359, + "ĠSAT": 18360, + "Ġinjection": 18361, + "Ġherbs": 18362, + "ĠScal": 18363, + "ĠSpirit": 18364, + "Ġ169": 18365, + "rize": 18366, + "Adam": 18367, + "Ġzoom": 18368, + "sen": 18369, + "Ġcylinders": 18370, + "Ġhormone": 18371, + "Ġprevalent": 18372, + "Ġimported": 18373, + "Host": 18374, + "inet": 18375, + "dade": 18376, + "ĠMexico": 18377, + "motor": 18378, + "éĺ": 18379, + "ĠEND": 18380, + "Iteration": 18381, + "Ġreminds": 18382, + "lecting": 18383, + "ο": 18384, + "Ġtrivial": 18385, + "ت": 18386, + "ĠпеÑĢ": 18387, + "Ġlets": 18388, + "tidad": 18389, + "Ġhazards": 18390, + "ordinary": 18391, + "Ġshaft": 18392, + "activation": 18393, + "015": 18394, + "Ins": 18395, + "Ġnerve": 18396, + "Sal": 18397, + "cx": 18398, + "Ġbiodiversity": 18399, + "RAME": 18400, + "Ġjewel": 18401, + "entralized": 18402, + "ĠScreen": 18403, + "Ġpractition": 18404, + "560": 18405, + "Ġprone": 18406, + "Prompt": 18407, + "Ġ${": 18408, + "scheduler": 18409, + "ĠIgn": 18410, + "Ġstraw": 18411, + "ĠDoc": 18412, + "Ġeliminates": 18413, + "hostname": 18414, + "Ġexceeding": 18415, + "Rob": 18416, + "jar": 18417, + "Ġfract": 18418, + "Ġdedu": 18419, + "]^": 18420, + "ĠChallenge": 18421, + "loading": 18422, + "Ġbug": 18423, + "_='": 18424, + "ican": 18425, + "Sep": 18426, + "Ġnicely": 18427, + "ĠValues": 18428, + "ĠCoun": 18429, + "bility": 18430, + "Ġsuited": 18431, + "ĠMoney": 18432, + "dashboard": 18433, + "Ġspanning": 18434, + "ĠCLI": 18435, + "share": 18436, + "ê³¼": 18437, + "ĠMNIST": 18438, + "CHA": 18439, + "ĠCreating": 18440, + "ĠWood": 18441, + "Ġsublist": 18442, + "ĠAuthentication": 18443, + "ASA": 18444, + "ĠUP": 18445, + "ĠBeing": 18446, + "Ġrevision": 18447, + "Ġphysi": 18448, + "Ġvarieties": 18449, + "factors": 18450, + "Ġrobots": 18451, + "Ġgrad": 18452, + "Ġcoords": 18453, + "ouds": 18454, + "ĠOrig": 18455, + "}}{(": 18456, + "Ġinhib": 18457, + "ĠChoosing": 18458, + "Ġresilient": 18459, + "Ġstudio": 18460, + "callable": 18461, + "Ġremained": 18462, + "Ġcrack": 18463, + "ĠDocker": 18464, + "Ġhinges": 18465, + "æŶéĹ´": 18466, + "changer": 18467, + "selector": 18468, + "Ġgraphically": 18469, + "ĠPor": 18470, + "Ġvessel": 18471, + "urities": 18472, + "å·²": 18473, + "ĠMah": 18474, + "Ġthro": 18475, + "ĠChi": 18476, + "]`": 18477, + "iser": 18478, + "ĠSame": 18479, + "Ġ103": 18480, + "Ġ625": 18481, + "ĠTrends": 18482, + "Ġpredictable": 18483, + "ĠEF": 18484, + "Ġtablets": 18485, + "vertical": 18486, + "缮å½ķ": 18487, + "åı£": 18488, + "Ġcredits": 18489, + "Ġsignifies": 18490, + "kal": 18491, + "Ġpromotions": 18492, + "abus": 18493, + "Ġweaknesses": 18494, + "overs": 18495, + "edu": 18496, + "\"]/": 18497, + "Ġcoat": 18498, + "Ġquadrants": 18499, + "ĠCoin": 18500, + "Disc": 18501, + "unknown": 18502, + "fft": 18503, + "Ġanalogy": 18504, + "Ġfootage": 18505, + "Ġbeloved": 18506, + "wear": 18507, + "elastic": 18508, + "ĠElectric": 18509, + "feat": 18510, + "Ġpreservation": 18511, + "provider": 18512, + "ĠRobert": 18513, + ">{": 18514, + "bbox": 18515, + "ĠExecutes": 18516, + "otos": 18517, + "Market": 18518, + "ÑĢам": 18519, + "èĢ": 18520, + "Enum": 18521, + "Ġimpression": 18522, + "Ġcompanions": 18523, + "Ġposes": 18524, + "occur": 18525, + "Ġsearches": 18526, + "ĠObser": 18527, + "enable": 18528, + "Ġtourism": 18529, + "Ġaddr": 18530, + "mx": 18531, + "apache": 18532, + "103": 18533, + "Ġnervous": 18534, + "Ġmicrow": 18535, + "ubic": 18536, + "Finding": 18537, + "Beyond": 18538, + "ighbors": 18539, + "解": 18540, + "liest": 18541, + "aus": 18542, + "horizontal": 18543, + "Ġoverhead": 18544, + "wart": 18545, + "Ġdivisions": 18546, + "ĠBelow": 18547, + "ÐĶ": 18548, + "Ġthrill": 18549, + "ç´¢": 18550, + "density": 18551, + "ÅĮ": 18552, + "COM": 18553, + "çīĩ": 18554, + "次": 18555, + "Ġsmoke": 18556, + "scroll": 18557, + "§è¡Į": 18558, + "}+\\": 18559, + "Ġgrant": 18560, + "Ġlinearity": 18561, + "Ġcorn": 18562, + "Hi": 18563, + "Ġpenalties": 18564, + "Ġpublication": 18565, + "reator": 18566, + "Render": 18567, + "Ġconsuming": 18568, + "Ġbab": 18569, + "generation": 18570, + "Ġacknowledge": 18571, + "sig": 18572, + "]\")": 18573, + "utch": 18574, + "ë¶": 18575, + "Ġdisad": 18576, + "tization": 18577, + "Ġacquisition": 18578, + "ĠWall": 18579, + "ĠLake": 18580, + "ë©": 18581, + "ricket": 18582, + "Ġ273": 18583, + "Ġìĺ¤": 18584, + "slice": 18585, + "åħ³": 18586, + "Ġvine": 18587, + "cookie": 18588, + "Ġfoss": 18589, + "ubs": 18590, + "arms": 18591, + "Ġdoubled": 18592, + "Ġfishing": 18593, + "ĠUr": 18594, + "Ġsingly": 18595, + "Ġcoating": 18596, + "asset": 18597, + "redirect": 18598, + "aman": 18599, + "ĠFranc": 18600, + ":')": 18601, + "åĪ¶": 18602, + "Ġinvestigate": 18603, + "ĠPin": 18604, + "Ġactionable": 18605, + "Ġvectorizer": 18606, + "Ġmetabolism": 18607, + "Ġadaptable": 18608, + "ecko": 18609, + "ĠComputer": 18610, + "ĠPS": 18611, 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37156, + "oshop": 37157, + "Ġbasement": 37158, + "Ġseparators": 37159, + "KEYDOWN": 37160, + "ĠíĬ": 37161, + "ĠStructField": 37162, + "Ġtodas": 37163, + "matriz": 37164, + "Ġparametri": 37165, + "Ġenigmatic": 37166, + "ĠнÑĥ": 37167, + "Ġplanner": 37168, + "755": 37169, + "otyping": 37170, + "Ġsquee": 37171, + "Ġdeg": 37172, + "ĠFinite": 37173, + "Ġurge": 37174, + "ĠOak": 37175, + "ĠоÑĪиб": 37176, + "æ": 37177, + "Ġlistas": 37178, + "srv": 37179, + "ĠзапÑĢоÑģ": 37180, + "Opening": 37181, + "ĠFlat": 37182, + "Ġlaunches": 37183, + "Ġcommittees": 37184, + "Ġcsrf": 37185, + "Ġdisciples": 37186, + "Eye": 37187, + "tico": 37188, + "ĠDecoder": 37189, + "ĠBeginners": 37190, + "Ġthirst": 37191, + "Ġincentive": 37192, + "essori": 37193, + "ĠLookup": 37194, + "ĠBelize": 37195, + "ede": 37196, + "Ġinformational": 37197, + "Schwarz": 37198, + "Ġinicio": 37199, + "Ġasynchronously": 37200, + "deserial": 37201, + "Fr": 37202, + "ĠMHz": 37203, + "ĠZen": 37204, + "ĠKS": 37205, + "\\))**:": 37206, + 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"arehouse": 42926, + "ĠHermitian": 42927, + "Ġdiscoloration": 42928, + "!}\\)": 42929, + "ĠTranslator": 42930, + "Ġprudent": 42931, + "ĠWronskian": 42932, + "Ġdelic": 42933, + "Ġaffectionate": 42934, + "SENSOR": 42935, + "Ġhype": 42936, + "ĠGap": 42937, + "ĠTransactions": 42938, + "Ġसम": 42939, + "ijkl": 42940, + "Ġrefrigeration": 42941, + "Ġkeyboards": 42942, + "ĠAttend": 42943, + "Ġtrivia": 42944, + "Ġarche": 42945, + "sele": 42946, + "ĠJulian": 42947, + "Ġtwentieth": 42948, + "printed": 42949, + "Cle": 42950, + "QUARE": 42951, + "belief": 42952, + "Ġterrains": 42953, + "433": 42954, + "mentation": 42955, + "outliers": 42956, + "Ġuncle": 42957, + "Ġtransmitting": 42958, + "Ġminers": 42959, + "ĠMLE": 42960, + "Ġfounders": 42961, + "dto": 42962, + "ĠArrow": 42963, + "451": 42964, + "Ġresumes": 42965, + "Ġparallelepip": 42966, + "Associativity": 42967, + "Ġgrapple": 42968, + "Console": 42969, + "Ġdancers": 42970, + "Ġchave": 42971, + "Ġcodigo": 42972, + "ĠNeighbors": 42973, + "Ġesper": 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