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README.md

@@ -1,3 +1,230 @@
 ---
+library_name: transformers
 license: mit
+base_model:
+- openai-community/gpt2
 ---
+# CODI 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-codi)
+
+</div>
+
+## Overview
+
+**CODI** (Continuous Output with Discrete Input) is a latent reasoning model based on GPT-2 that extends the base architecture with an optional projector module for enhanced hidden state 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**: GPT-2 Language Model
+- **Model Class**: `CODIGPT2` (extends `GPT2LMHeadModel`)
+- **Special Features**: Optional projector module for extended hidden states
+- **Latent Tokens**: Uses special tokens `<|latent|>`, `<|start-latent|>`, `<|end-latent|>` for latent reasoning
+- **Input Format**: Direct input without newline before `<|start-latent|>` token
+
+## Related Models
+
+This repository includes other latent reasoning models that you might find useful:
+
+- **[COCONUT Model](../coconut/README.md)** - GPT-2 based model for continuous thought generation
+- **[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-codi --local-dir checkpoints/codi
+```
+
+## 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/codi"
+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 LatentCODI(MODELS["codi"]["class"], LatentGenerationMixin):
+    def __init__(self, config):
+        super().__init__(config)
+
+# Load model
+model = LatentCODI.from_pretrained(
+    model_id,
+    latent_id=latent_id,
+    latent_start_id=start_id,
+    latent_end_id=end_id,
+    device_map="auto",
+)
+
+# Prepare input (note: no newline before <|start-latent|>)
+question = "What is 2 + 2?<|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 with Transformers:
+
+```python
+# Prepare batch inputs
+questions = [
+    "What is 2 + 2?<|start-latent|>",
+    "What is 5 * 3?<|start-latent|>",
+    "What is 10 - 4?<|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)
+```
+
+## Model Architecture
+
+### Projector Module
+
+CODI includes an optional projector module that extends hidden states:
+
+```python
+# Projector configuration (if enabled in model)
+projector = nn.Sequential(
+    nn.Dropout(projector_dropout),
+    nn.Linear(hidden_size, projector_hidden_size),
+    nn.GELU(),
+    nn.Linear(projector_hidden_size, hidden_size),
+    nn.LayerNorm(hidden_size),
+)
+```
+
+The projector is used when `output_hidden_states=True` and `config.projector=True`.
+
+## 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
+
+CODI uses standard number extraction from the generated text:
+
+```python
+from src.paths import extract_answer_number
+
+# Extract answer from generated text
+answer = extract_answer_number(result)
+print(f"Answer: {answer}")
+```
+
+## Evaluation
+
+Run evaluation using the provided scripts:
+
+```bash
+# For CODI (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-codi](https://huggingface.co/dd101bb/latent-tts-codi)
+- **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{shen2025codicompressingchainofthoughtcontinuous,
+      title={CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation}, 
+      author={Zhenyi Shen and Hanqi Yan and Linhai Zhang and Zhanghao Hu and Yali Du and Yulan He},
+      year={2025},
+      eprint={2502.21074},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.21074}, 
+}
+```

added_tokens.json

@@ -0,0 +1,6 @@
+{
+  "<|end-latent|>": 50259,
+  "<|latent|>": 50260,
+  "<|start-latent|>": 50258,
+  "[PAD]": 50257
+}

config.json

@@ -0,0 +1,44 @@
+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "CODIGPT2"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "latent_end_id": -100,
+  "latent_id": -100,
+  "latent_start_id": -100,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "projector": true,
+  "projector_dropout": 0.0,
+  "projector_hidden_size": 768,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "vocab_size": 50260
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.52.4"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:20c3730ebd562f317c56d7e1ab0eaa9fb41ab5228fa2bba32b7ac4189e7c1554
+size 502514824

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,54 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50257": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50258": {
+      "content": "<|start-latent|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
+      "content": "<|end-latent|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50260": {
+      "content": "<|latent|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "padding_side": "left",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}