Update README.md

README.md

@@ -1,238 +1,238 @@
----
-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/dd101bb/latent-tts-colar)
-
-</div>
-
-## Overview
-
-**CoLaR** (Continuous 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 dd101bb/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**: [dd101bb/latent-tts-colar](https://huggingface.co/dd101bb/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}, 
-}
-```
+---
+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** (Continuous 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}, 
+}
+```