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README.md

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+---
+license: apache-2.0
+base_model: answerdotai/ModernBERT-base
+tags:
+  - modernbert
+  - long-context
+  - mlm
+  - rope-scaling
+  - yarn
+  - fill-mask
+datasets:
+  - Leooyii/Slimpajama_downsample_32k_1B
+language:
+  - en
+pipeline_tag: fill-mask
+library_name: transformers
+---
+
+# ModernBERT-base-32K
+
+A long-context extension of [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) fine-tuned for 32K token context length using YaRN (Yet another RoPE extensioN) scaling.
+
+## Model Description
+
+This model extends the original ModernBERT-base from 8,192 tokens to **32,768 tokens** context length while preserving the base model's capabilities. It was fine-tuned using specialized techniques to maintain long-range retrieval performance:
+
+- **YaRN RoPE Scaling**: Factor of 4.0x to extend positional embeddings
+- **Retrieval-Aware Masking**: Custom MLM objective that encourages long-range token dependencies  
+- **Elastic Weight Consolidation (EWC)**: Regularization to prevent catastrophic forgetting
+- **Conservative Learning Rate**: 1e-5 with constant warmup schedule
+
+### Model Details
+
+| Property | Value |
+|----------|-------|
+| Parameters | 149M |
+| Context Length | 32,768 tokens |
+| Hidden Size | 768 |
+| Attention Heads | 12 |
+| Layers | 22 |
+| Vocabulary | 50,368 |
+| Architecture | ModernBERT (RoPE + Sliding Window + Global Attention) |
+
+## Training
+
+### Dataset
+- **Source**: Leooyii/Slimpajama_downsample_32k_1B (subset)
+- **Samples**: ~1B tokens of long-form text (≥32K tokens per document)
+- **Preprocessing**: Concatenated documents with proper sequence boundaries
+
+### Hyperparameters
+
+```yaml
+learning_rate: 1e-5
+lr_scheduler: constant_with_warmup
+warmup_ratio: 0.1
+epochs: 1
+batch_size: 6
+gradient_accumulation: 1
+precision: bf16
+max_length: 32768
+
+# RoPE Scaling
+rope_scaling_type: yarn
+rope_scaling_factor: 4.0
+
+# Retrieval Masking
+mlm_probability: 0.30
+retrieval_probability: 0.10
+min_distance_for_retrieval: 512
+
+# EWC Regularization
+ewc_lambda: 1000.0
+ewc_samples: 200
+```
+
+### Training Infrastructure
+- **Hardware**: AMD MI300X GPU (192GB HBM3)
+- **Framework**: PyTorch + Transformers
+- **Training Time**: ~4 hours
+
+## Evaluation Results
+
+### Comparison with Base Model (at 32K context)
+
+| Metric | This Model | Base ModernBERT | 
+|--------|-----------|-----------------|
+| Basic MLM Accuracy | 100% | 100% |
+| Perplexity @ 32K | 1.00 | 1.00 |
+| Passkey Retrieval @ 512 | **60%** | 50% |
+| Passkey Retrieval @ 1K | 50% | 50% |
+| Long-Range Coreference | 100% | 100% |
+| Position Accuracy (early) | **90%** | 85% |
+| Position Accuracy (late) | **71%** | 64% |
+| Repeated Info Consistency | 100% | 100% |
+
+### Distance-Based Retrieval Accuracy
+
+| Distance | Accuracy |
+|----------|----------|
+| 64 tokens | 80% |
+| 128 tokens | 60% |
+| 256 tokens | 73% |
+| 512 tokens | 27% |
+| 1024 tokens | 40% |
+| 2048 tokens | 47% |
+| 4096 tokens | 53% |
+| 8192 tokens | 20% |
+
+### Perplexity by Context Length
+
+| Context | Perplexity |
+|---------|------------|
+| 512 | 1.84 |
+| 1024 | 1.29 |
+| 2048 | 1.11 |
+| 4096 | 1.02 |
+| 8192 | 1.00 |
+| 16384 | 1.00 |
+| 24576 | 1.00 |
+| 32768 | 1.00 |
+
+## Usage
+
+### Basic Usage
+
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+model = AutoModelForMaskedLM.from_pretrained("llm-semantic-router/modernbert-base-32k")
+tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/modernbert-base-32k")
+
+text = "The capital of France is [MASK]."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+# Get predictions
+mask_idx = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
+logits = outputs.logits[0, mask_idx, :]
+top_tokens = logits.topk(5).indices[0]
+print([tokenizer.decode(t) for t in top_tokens])
+# ['Paris', 'Lyon', 'Nancy', ...]
+```
+
+### Long Context Usage
+
+```python
+# For sequences longer than 8192 tokens, YaRN scaling is automatically applied
+long_text = "..." * 10000  # Very long document
+inputs = tokenizer(
+    long_text, 
+    return_tensors="pt", 
+    max_length=32768, 
+    truncation=True
+)
+outputs = model(**inputs)
+```
+
+### Feature Extraction
+
+```python
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("llm-semantic-router/modernbert-base-32k")
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+embeddings = outputs.last_hidden_state  # [batch, seq_len, 768]
+
+# Mean pooling for sentence embedding
+attention_mask = inputs["attention_mask"]
+masked_embeddings = embeddings * attention_mask.unsqueeze(-1)
+sentence_embedding = masked_embeddings.sum(1) / attention_mask.sum(1, keepdim=True)
+```
+
+## Limitations
+
+1. **Passkey Retrieval at Long Distances**: Like the base model, this model struggles with needle-in-haystack retrieval beyond ~1K tokens. This is a fundamental limitation of MLM architectures, not the fine-tuning.
+
+2. **Context Utilization**: At very long contexts (16K+), the model may not always benefit from additional context for MLM predictions. This is expected behavior for encoder-only models.
+
+3. **Memory Requirements**: Processing 32K tokens requires significant GPU memory (~16GB+ for inference).
+
+4. **Domain**: Trained primarily on web text; may not generalize well to specialized domains without additional fine-tuning.
+
+## Intended Use
+
+- **Long-document understanding**: Processing documents that exceed typical BERT context limits
+- **Information retrieval**: Embedding long documents for semantic search
+- **Long-form text classification**: Sentiment analysis, topic classification on long texts
+- **Coreference resolution**: Resolving references across long documents
+- **Feature extraction**: Getting embeddings for downstream tasks
+
+## Training Methodology
+
+This model uses several techniques to preserve long-range capabilities during fine-tuning:
+
+### 1. YaRN RoPE Scaling
+Extends positional embeddings from 8K to 32K using Yet another RoPE extensioN with:
+- Scaling factor: 4.0
+- Original base frequency: 10000
+- Attention scaling for numerical stability
+
+### 2. Retrieval-Aware Masking
+Custom MLM objective that creates explicit long-range dependencies:
+- Standard MLM masking (30%)
+- Additional masking of tokens that appear in distant context (10% probability)
+- Minimum distance threshold: 512 tokens
+
+### 3. Elastic Weight Consolidation
+Prevents catastrophic forgetting by:
+- Computing Fisher information matrix on base model
+- Penalizing changes to important weights during fine-tuning
+- Lambda: 1000.0
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{modernbert-32k,
+  title={ModernBERT-base-32K: Long-Context Extension of ModernBERT},
+  author={LLM Semantic Router},
+  year={2026},
+  url={https://huggingface.co/llm-semantic-router/modernbert-base-32k}
+}
+```
+
+Also cite the original ModernBERT paper:
+
+```bibtex
+@article{modernbert2024,
+  title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference},
+  author={Warner, Benjamin and Chaffin, Antoine and Geiger, Benjamin and Werra, Leandro von and Tunstall, Lewis and Bartolo, Max and Thrush, Tristan},
+  journal={arXiv preprint},
+  year={2024}
+}
+```
+
+## License
+
+Apache 2.0 (same as base model)
+
+## Acknowledgments
+
+- [Answer.AI](https://www.answer.ai/) for the original ModernBERT model
+- [Leooyii](https://github.com/Leooyii) for the SlimPajama dataset
+- [AMD](https://www.amd.com/) for the MI300X GPU
+

config.json

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+  "layer_norm_eps": 1e-05,
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+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
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+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.56.1",
+  "vocab_size": 50368
+}

model.safetensors

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special_tokens_map.json

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+}

training_config.json

@@ -0,0 +1,37 @@
+{
+  "model_name_or_path": "answerdotai/ModernBERT-base",
+  "rope_scaling_type": "yarn",
+  "rope_scaling_factor": 4.0,
+  "rope_original_max_position_embeddings": 8192,
+  "yarn_beta_fast": 32.0,
+  "yarn_beta_slow": 1.0,
+  "yarn_extrapolation_factor": 1.0,
+  "yarn_attn_factor": 1.0,
+  "dataset_name": "/data/datasets/slimpajama_32k_1B",
+  "model_max_length": 32768,
+  "mlm_probability": 0.3,
+  "max_train_samples": null,
+  "preprocessing_num_workers": 4,
+  "dataloader_num_workers": 4,
+  "output_dir": "/data/outputs/modernbert-32k-retrieval",
+  "per_device_train_batch_size": 6,
+  "torch_compile": false,
+  "gradient_accumulation_steps": 1,
+  "learning_rate": 1e-05,
+  "weight_decay": 0.01,
+  "warmup_steps": 100,
+  "warmup_ratio": 0.1,
+  "num_train_epochs": 1,
+  "max_grad_norm": 1.0,
+  "lr_scheduler_type": "constant_with_warmup",
+  "use_retrieval_masking": true,
+  "retrieval_probability": 0.1,
+  "min_distance_for_retrieval": 512,
+  "use_ewc": true,
+  "ewc_lambda": 1000.0,
+  "ewc_samples": 100,
+  "bf16": true,
+  "logging_steps": 10,
+  "save_steps": 500,
+  "seed": 42
+}