README.md

---
library_name: transformers
license: mit
base_model:
- meta-llama/Llama-3.2-1B-Instruct
---
# CoLaR 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-colar)

</div>

## Overview

**CoLaR** (Compressed Latent Reasoning) is a latent reasoning model based on LLaMA that uses a specialized LatentHead module for generating continuous latent representations. 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**: LLaMA Language Model
- **Model Class**: `ColarLlama` (extends `LlamaForCausalLM`)
- **Special Features**: LatentHead module for latent space generation
- **Latent Tokens**: Uses special token `<|latent|>` for latent reasoning
- **End Token**: Uses `###` as the end-of-latent marker
- **Input Format**: Direct input format with latent tokens

## 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-colar --local-dir checkpoints/colar
```

## Quick Start

### Basic Usage

```python
import torch
from transformers import AutoTokenizer
from src.generation_mixin import LatentGenerationMixin, LatentGenerationConfig
from src.paths import MODELS

# Load tokenizer
model_id = "checkpoints/colar"
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|>")
end_id = tokenizer.convert_tokens_to_ids("###")

# Create model class with generation mixin
class LatentCoLaR(MODELS["colar"]["class"], LatentGenerationMixin):
    pass

# Load model
model = LatentCoLaR.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,  # Recommended for LLaMA models
)

# Prepare input
question = "What is 2 + 2?<|latent|>"
inputs = tokenizer(question, return_tensors="pt").to(model.device)

# Configure generation
generation_config = LatentGenerationConfig(
    max_new_tokens=128,
    max_latent_length=64,  # CoLaR uses max_latent_length instead of latent_length
    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 with Transformers:

```python
import torch

# Prepare batch inputs
questions = [
    "What is 2 + 2?<|latent|>",
    "What is 5 * 3?<|latent|>",
    "What is 10 - 4?<|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)
```

## Model Architecture

### LatentHead Module

CoLaR uses a specialized LatentHead for generating latent representations:

```python
class LatentHead(nn.Module):
    def __init__(self, feature_size, intermediate_size=512):
        super().__init__()
        self.fc = nn.Sequential(
            nn.Linear(feature_size, intermediate_size),
            nn.GELU(),
            nn.Linear(intermediate_size, intermediate_size),
            nn.LayerNorm(intermediate_size),
        )
        self.mean = nn.Linear(intermediate_size, feature_size)
```

The latent embeddings are scaled by `latent_embedding_std` (default: 0.018 for LLaMA-3.2 models).

## Generation Parameters

### LatentGenerationConfig

- `max_new_tokens` (int): Maximum number of tokens to generate
- `max_latent_length` (int): Maximum number of latent tokens (default: 64)
- `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

CoLaR uses a special answer format with "Answer:" prefix:

```python
from src.paths import colar_extract_answer_number

# Extract answer from generated text
answer = colar_extract_answer_number(result)
print(f"Answer: {answer}")
```

## Evaluation

Run evaluation using the provided scripts:

```bash
# For CoLaR (LLaMA based models)
./run_tests_llama.sh
```

## Model Card

- **Paper**: [Parallel Test-Time Scaling for Latent Reasoning Models](https://arxiv.org/abs/2510.07745)
- **HuggingFace**: [ModalityDance/latent-tts-colar](https://huggingface.co/ModalityDance/latent-tts-colar)
- **Benchmarks**: GSM8K Test, GSM8K Hard, MultiArith

## Notes

- **Data Type**: Recommended to use `torch.bfloat16` or `torch.float16` for LLaMA models
- **Memory**: LLaMA models typically require more GPU memory than GPT-2 models
- **Latent Length**: CoLaR uses `max_latent_length` instead of fixed `latent_length`

## 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{tan2025thinksilentlythinkfast,
      title={Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains}, 
      author={Wenhui Tan and Jiaze Li and Jianzhong Ju and Zhenbo Luo and Jian Luan and Ruihua Song},
      year={2025},
      eprint={2505.16552},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.16552}, 
}
```