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README.md

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-# COCONUT Model
-
-<div align="center">
-
-[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-Model-fcc21b?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/dd101bb/latent-tts-coconut)
-
-</div>
-
-## Overview
-
-**COCONUT** (Continuous COnceptual Neural Thought) is a latent reasoning model based on GPT-2 that enables continuous thought generation in latent space. This model is part of the [Parallel Test-Time Scaling for Latent Reasoning Models](https://arxiv.org/abs/2510.07745) framework.
-
-## Model Details
-
-- **Base Architecture**: GPT-2 Language Model
-- **Model Class**: `COCONUTGPT2` (extends `GPT2LMHeadModel`)
-- **Latent Tokens**: Uses special tokens `<|latent|>`, `<|start-latent|>`, `<|end-latent|>` for latent reasoning
-- **Input Format**: Requires newline after input question before `<|start-latent|>` token
-
-## Related Models
-
-This repository includes other latent reasoning models that you might find useful:
-
-- **[CODI Model](../codi/README.md)** - GPT-2 based model with optional projector module for enhanced hidden states
-- **[CoLaR Model](../colar/README.md)** - LLaMA based model with specialized LatentHead module
-
-## Installation
-
-Download the model from HuggingFace:
-
-```bash
-huggingface-cli download dd101bb/latent-tts-coconut --local-dir checkpoints/coconut
-```
-
-## Quick Start
-
-### Basic Usage
-
-```python
-from transformers import AutoTokenizer
-from src.generation_mixin import LatentGenerationMixin, LatentGenerationConfig
-from src.paths import MODELS
-
-# Load tokenizer
-model_id = "checkpoints/coconut"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-
-# Get latent token IDs
-latent_id = tokenizer.convert_tokens_to_ids("<|latent|>")
-start_id = tokenizer.convert_tokens_to_ids("<|start-latent|>")
-end_id = tokenizer.convert_tokens_to_ids("<|end-latent|>")
-
-# Create model class with generation mixin
-class LatentCOCONUT(MODELS["coconut"]["class"], LatentGenerationMixin):
-    def __init__(self, config):
-        super().__init__(config)
-
-# Load model
-model = LatentCOCONUT.from_pretrained(
-    model_id,
-    latent_id=latent_id,
-    latent_start_id=start_id,
-    latent_end_id=end_id,
-    device_map="auto",
-)
-
-# Prepare input (note: newline before <|start-latent|>)
-question = "What is 2 + 2?\n<|start-latent|>"
-inputs = tokenizer(question, return_tensors="pt").to(model.device)
-
-# Configure generation
-generation_config = LatentGenerationConfig(
-    max_new_tokens=512,
-    latent_length=6,
-    latent_do_sample=True,
-    latent_do_sample_by="dropout",  # or "noise"
-    dropout_p=0.1,
-    pad_token_id=tokenizer.pad_token_id,
-    eos_token_id=tokenizer.eos_token_id,
-)
-
-# Generate
-output = model.generate(
-    **inputs,
-    generation_config=generation_config,
-    num_return_sequences=1,
-)
-
-# Decode result
-result = tokenizer.decode(output[0], skip_special_tokens=True)
-print(result)
-```
-
-### Batch Processing
-
-The model fully supports batch processing:
-
-```python
-# Prepare batch inputs
-questions = [
-    "What is 2 + 2?\n<|start-latent|>",
-    "What is 5 * 3?\n<|start-latent|>",
-    "What is 10 - 4?\n<|start-latent|>",
-]
-inputs = tokenizer(questions, return_tensors="pt", padding=True).to(model.device)
-
-# Generate for batch
-outputs = model.generate(
-    **inputs,
-    generation_config=generation_config,
-    num_return_sequences=1,
-)
-
-# Decode batch results
-results = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-for result in results:
-    print(result)
-```
-
-## Generation Parameters
-
-### LatentGenerationConfig
-
-- `max_new_tokens` (int): Maximum number of tokens to generate
-- `latent_length` (int): Number of latent tokens (default: 6)
-- `latent_do_sample` (bool): Whether to use stochastic sampling
-- `latent_do_sample_by` (str): Sampling method - `"dropout"` or `"noise"`
-- `dropout_p` (float): Dropout probability for Monte Carlo Dropout (e.g., 0.1)
-- `noise_std` (float): Standard deviation for Additive Gaussian Noise
-
-### Sampling Methods
-
-1. **Monte Carlo Dropout**: Randomly drops activations during forward passes
-
-   ```python
-   generation_config = LatentGenerationConfig(
-       latent_do_sample_by="dropout",
-       dropout_p=0.1,
-       # ...
-   )
-   ```
-2. **Additive Gaussian Noise**: Injects noise into latent embeddings
-
-   ```python
-   generation_config = LatentGenerationConfig(
-       latent_do_sample_by="noise",
-       noise_std=0.1,
-       # ...
-   )
-   ```
-
-## Answer Extraction
-
-COCONUT uses a special answer format with `#` separator:
-
-```python
-from src.paths import coconut_extract_answer_number
-
-# Extract answer from generated text
-answer = coconut_extract_answer_number(result)
-print(f"Answer: {answer}")
-```
-
-## Evaluation
-
-Run evaluation using the provided scripts:
-
-```bash
-# For COCONUT (GPT-2 based models)
-./run_tests.sh
-```
-
-## Model Card
-
-- **Paper**: [Parallel Test-Time Scaling for Latent Reasoning Models](https://arxiv.org/abs/2510.07745)
-- **HuggingFace**: [dd101bb/latent-tts-coconut](https://huggingface.co/dd101bb/latent-tts-coconut)
-- **Benchmarks**: GSM8K Test, GSM8K Hard, MultiArith
-
-## Citation
-
-If you use this model, please cite:
-
-```bibtex
-@misc{you2025paralleltesttimescalinglatent,
-      title={Parallel Test-Time Scaling for Latent Reasoning Models}, 
-      author={Runyang You and Yongqi Li and Meng Liu and Wenjie Wang and Liqiang Nie and Wenjie Li},
-      year={2025},
-      eprint={2510.07745},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2510.07745}, 
-}
-
-@misc{hao2025traininglargelanguagemodels,
-      title={Training Large Language Models to Reason in a Continuous Latent Space}, 
-      author={Shibo Hao and Sainbayar Sukhbaatar and DiJia Su and Xian Li and Zhiting Hu and Jason Weston and Yuandong Tian},
-      year={2025},
-      eprint={2412.06769},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2412.06769}, 
-}
-```
+---
+license: mit
+base_model:
+- openai-community/gpt2
+---
+# COCONUT Model
+
+<div align="center">
+
+[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-Model-fcc21b?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/ModalityDance/latent-tts-coconut)
+
+</div>
+
+## Overview
+
+**COCONUT** (Continuous COnceptual Neural Thought) is a latent reasoning model based on GPT-2 that enables continuous thought generation in latent space. This model is part of the [Parallel Test-Time Scaling for Latent Reasoning Models](https://arxiv.org/abs/2510.07745) framework.
+
+## Model Details
+
+- **Base Architecture**: GPT-2 Language Model
+- **Model Class**: `COCONUTGPT2` (extends `GPT2LMHeadModel`)
+- **Latent Tokens**: Uses special tokens `<|latent|>`, `<|start-latent|>`, `<|end-latent|>` for latent reasoning
+- **Input Format**: Requires newline after input question before `<|start-latent|>` token
+
+## Related Models
+
+This repository includes other latent reasoning models that you might find useful:
+
+[ModalityDance/latent-tts](https://huggingface.co/collections/ModalityDance/latent-tts)
+
+## Installation
+
+Download the model from HuggingFace:
+
+```bash
+huggingface-cli download ModalityDance/latent-tts-coconut --local-dir checkpoints/coconut
+```
+
+## Quick Start
+
+### Basic Usage
+
+```python
+from transformers import AutoTokenizer
+from src.generation_mixin import LatentGenerationMixin, LatentGenerationConfig
+from src.paths import MODELS
+
+# Load tokenizer
+model_id = "checkpoints/coconut"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# Get latent token IDs
+latent_id = tokenizer.convert_tokens_to_ids("<|latent|>")
+start_id = tokenizer.convert_tokens_to_ids("<|start-latent|>")
+end_id = tokenizer.convert_tokens_to_ids("<|end-latent|>")
+
+# Create model class with generation mixin
+class LatentCOCONUT(MODELS["coconut"]["class"], LatentGenerationMixin):
+    def __init__(self, config):
+        super().__init__(config)
+
+# Load model
+model = LatentCOCONUT.from_pretrained(
+    model_id,
+    latent_id=latent_id,
+    latent_start_id=start_id,
+    latent_end_id=end_id,
+    device_map="auto",
+)
+
+# Prepare input (note: newline before <|start-latent|>)
+question = "What is 2 + 2?\n<|start-latent|>"
+inputs = tokenizer(question, return_tensors="pt").to(model.device)
+
+# Configure generation
+generation_config = LatentGenerationConfig(
+    max_new_tokens=512,
+    latent_length=6,
+    latent_do_sample=True,
+    latent_do_sample_by="dropout",  # or "noise"
+    dropout_p=0.1,
+    pad_token_id=tokenizer.pad_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+# Generate
+output = model.generate(
+    **inputs,
+    generation_config=generation_config,
+    num_return_sequences=1,
+)
+
+# Decode result
+result = tokenizer.decode(output[0], skip_special_tokens=True)
+print(result)
+```
+
+### Batch Processing
+
+The model fully supports batch processing:
+
+```python
+# Prepare batch inputs
+questions = [
+    "What is 2 + 2?\n<|start-latent|>",
+    "What is 5 * 3?\n<|start-latent|>",
+    "What is 10 - 4?\n<|start-latent|>",
+]
+inputs = tokenizer(questions, return_tensors="pt", padding=True).to(model.device)
+
+# Generate for batch
+outputs = model.generate(
+    **inputs,
+    generation_config=generation_config,
+    num_return_sequences=1,
+)
+
+# Decode batch results
+results = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+for result in results:
+    print(result)
+```
+
+## Generation Parameters
+
+### LatentGenerationConfig
+
+- `max_new_tokens` (int): Maximum number of tokens to generate
+- `latent_length` (int): Number of latent tokens (default: 6)
+- `latent_do_sample` (bool): Whether to use stochastic sampling
+- `latent_do_sample_by` (str): Sampling method - `"dropout"` or `"noise"`
+- `dropout_p` (float): Dropout probability for Monte Carlo Dropout (e.g., 0.1)
+- `noise_std` (float): Standard deviation for Additive Gaussian Noise
+
+### Sampling Methods
+
+1. **Monte Carlo Dropout**: Randomly drops activations during forward passes
+
+   ```python
+   generation_config = LatentGenerationConfig(
+       latent_do_sample_by="dropout",
+       dropout_p=0.1,
+       # ...
+   )
+   ```
+2. **Additive Gaussian Noise**: Injects noise into latent embeddings
+
+   ```python
+   generation_config = LatentGenerationConfig(
+       latent_do_sample_by="noise",
+       noise_std=0.1,
+       # ...
+   )
+   ```
+
+## Answer Extraction
+
+COCONUT uses a special answer format with `#` separator:
+
+```python
+from src.paths import coconut_extract_answer_number
+
+# Extract answer from generated text
+answer = coconut_extract_answer_number(result)
+print(f"Answer: {answer}")
+```
+
+## Evaluation
+
+Run evaluation using the provided scripts:
+
+```bash
+# For COCONUT (GPT-2 based models)
+./run_tests.sh
+```
+
+## Model Card
+
+- **Paper**: [Parallel Test-Time Scaling for Latent Reasoning Models](https://arxiv.org/abs/2510.07745)
+- **HuggingFace**: [ModalityDance/latent-tts-coconut](https://huggingface.co/ModalityDance/latent-tts-coconut)
+- **Benchmarks**: GSM8K Test, GSM8K Hard, MultiArith
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{you2025paralleltesttimescalinglatent,
+      title={Parallel Test-Time Scaling for Latent Reasoning Models}, 
+      author={Runyang You and Yongqi Li and Meng Liu and Wenjie Wang and Liqiang Nie and Wenjie Li},
+      year={2025},
+      eprint={2510.07745},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2510.07745}, 
+}
+
+@misc{hao2025traininglargelanguagemodels,
+      title={Training Large Language Models to Reason in a Continuous Latent Space}, 
+      author={Shibo Hao and Sainbayar Sukhbaatar and DiJia Su and Xian Li and Zhiting Hu and Jason Weston and Yuandong Tian},
+      year={2025},
+      eprint={2412.06769},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2412.06769}, 
+}
+```