Initial upload of CGGR-specialized Math model

.gitattributes

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+benchmark_dashboard.png filter=lfs diff=lfs merge=lfs -text
+loss_curve.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM-135M
+datasets:
+- openai/gsm8k
+- meta-math/MetaMathQA
+- AI-MO/NuminaMath-1.5
+tags:
+- math
+- reasoning
+- efficient-training
+- cggr
+- sparse-gradients
+model_name: SmolLM-135M-CGGR-Math
+---
+
+# SmolLM-135M-CGGR-Math
+
+This model is a specialized version of **HuggingFaceTB/SmolLM-135M**, fine-tuned for mathematical reasoning using **Confidence-Gated Gradient Routing (CGGR)**.
+
+## 🚀 The CGGR Breakthrough
+
+This model was trained using a novel training strategy that selects only the "hardest" tokens for gradient updates, allowing for:
+- **4.08x Higher Throughput:** Processing 4x more data in the same wall-clock time compared to standard training.
+- **66% VRAM Savings:** Fitting large-batch training on consumer hardware (RTX 3060).
+- **Superior Convergence:** Achieving a **+19% relative accuracy improvement** on math reasoning tasks (AIME 2024) compared to standard fine-tuning.
+
+### Benchmark Results (6-Hour Training Race)
+
+| Metric                      | Standard (Baseline) | CGGR (Ours)        | Relative Gain     |
+| :-------------------------- | :------------------ | :----------------- | :---------------- |
+| **Solving Accuracy (AIME)** | 8.00%               | **9.50%**          | **+18.75%**       |
+| **Training Throughput**     | 14,368 samples      | **58,716 samples** | **+308%**         |
+| **Final Loss**              | 0.3610              | **0.0980**         | **-73% Error**    |
+| **Max Batch Size (12GB)**   | 18                  | **69**             | **3.8x Capacity** |
+
+## 📈 Performance Visuals
+
+![Benchmark Dashboard](https://huggingface.co/MinimaML/SmolLM-135M-CGGR-Math/resolve/main/benchmark_dashboard.png)
+
+## Model Details
+
+- **Architecture:** Transformer Decoder (SmolLM-135M)
+- **Training Method:** CGGR (Confidence-Gated Gradient Routing)
+- **Selection Strategy:** Fixed Quota (Top 25% hardest tokens)
+- **Compute:** Trained on a single NVIDIA RTX 3060 (12GB)
+- **Duration:** 6 Total Hours
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "MinimaML/SmolLM-135M-CGGR-Math"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "Question: If x + y = 10 and x - y = 2, what is the value of x?\n\nAnswer:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Citation
+
+If you use this model or the CGGR technique in your research, please cite:
+
+```bibtex
+@software{cggr2026,
+  title={CGGR: Confidence-Gated Gradient Routing},
+  author={MinimaML},
+  year={2026},
+  url={https://github.com/MinimaML/CGGR}
+}
+```

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 3,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0rc1",
+  "use_cache": true,
+  "vocab_size": 49152
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "5.0.0rc1"
+}

model.safetensors

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+size 538090408

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "additional_special_tokens": null,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<jupyter_script>",
+    "<empty_output>"
+  ],
+  "is_local": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}