feat: update the package for main model, create dcd evaluate, update …

…readme

README.md

@@ -6,69 +6,89 @@ by [Phuc Phan](https://www.linkedin.com/in/pphuc/), [Hieu Tran](https://www.link
 
 
 ## Introduction
+In our paper, we introduce Distillation Contrastive Decoding (DCD), a novel approach designed to enhance the reasoning capabilities of large language models (LLMs) during inference. DCD leverages the power of contrastive chain-of-thought prompts (CP) and distillation to improve LLMs' task performance by minimizing reasoning errors.
+
+DCD addresses the limitations of existing Contrastive Decoding techniques, such as the need for smaller parallel models and high memory usage, offering a more efficient and scalable solution. Our extensive evaluations across various reasoning benchmarks, including arithmetic and commonsense reasoning tasks, demonstrate the superior performance of DCD, marking a significant advancement in the field of natural language processing and LLM reasoning enhancement.
 
 <img align="center" src="assets/figure1-method.jpg" width="750">
 
 <!-- <img align="center" src="assets/compare_methods.jpg" width="750"> -->
 
 
 ## Installation
+
+To install the package, clone the repository and install the required dependencies:
+
 ```bash
 git clone https://github.com/pphuc25/distillation-contrastive-decoding.git
 cd distillation-contrastive-decoding
 pip install -e .
 ```
 
 ## Quickstart
-Our Distillation Contrastive Decoding apply for task generation in huggingface with beam size is 1. Currently support for 🤗 Hugging Face.
+
+Our DCD approach is applicable for task generation in 🤗 Hugging Face with a beam size of 1. Here's a quick example of how to use it:
 
 ```python
+# Import necessary libraries and functions
 from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from dcd import dcd_pipeline_registry, set_stop_words, create_prompt, create_prompt_student
 
-from dcd import dcd_pipeline_registry, set_stop_words
-from dcd import create_prompt, create_prompt_student
-
-dcd_pipeline_registry()  # register 'DCD' into greedy search of Hugging Face environment.
+# Register 'DCD' into the greedy search of Hugging Face environment
+dcd_pipeline_registry()
 
+# Load the model and tokenizer
 model = AutoModelForCausalLM.from_pretrained("model_name", device_map="auto")
 tokenizer = AutoTokenizer.from_pretrained("model_name", device_map="auto")
 
-# Set the beam size to 1, add cot prompt for both expert and amateur model
+# Set the generation configuration
 generation_config = GenerationConfig(
     num_beams=1,
     pad_token_id=0,
     eos_token_id=0,
 )
 
-# Set the alpha, beta and dropout rate
+# Set the alpha, beta, and dropout rate
 alpha_coef = 0.1
 beta_coef = 0.8
 dropout_rate = 0.2
 
+# Define the question and format it
 question = "Toulouse has twice as many sheep as Charleston. Charleston has 4 times as many sheep as Seattle. How many sheep do Toulouse, Charleston, and Seattle have together if Seattle has 20 sheep?"
 question_formated = "Q: " + question + "\n" + "A:"
 inputs = tokenizer(create_prompt(args_prompt, data_name=args_prompt.data_name) + question_formated, return_tensors="pt")
 input_ids = inputs["input_ids"].to(device)
 
-# Create input ids student
+# Create input ids for the student model
 inputs_student = tokenizer(create_prompt_student(args_prompt, type=type_prompt, data_name=args_prompt.data_name) + question_formated, return_tensors="pt")
 input_ids_student = inputs_student["input_ids"].to(device)
 
-# Generate
+# Generate the output sequences
 output_sequences = model.generate(
     generation_config=generation_config,
     input_ids=input_ids,
 
-    # Set the args for DCD
+    # Args of DCD
     input_ids_student=input_ids_student,
     teacher_student=True,
     dropout_rate=dropout_rate,
     alpha_coef=alpha_coef,
     beta_coef=beta_coef,
 )
-# This is done! Do the rest for infer model 
+
+# Continue with your inference process...
+
 ```
 
+## Examples
+
+Check out [examples](./examples) of Distillation Contrastive Decoding (DCD), containing both applied dropout and dropout with quantize implementation. We welcome community contributions as well!
+
+
+## DCD Evaluation
+
+To facilitate easier evaluation or reproduction of DCD results, we have released a package framework, [DCD_eval](./dcd_eval), designed for few-shot evaluation of both arithmetic and commonsense reasoning on standard benchmarks. For more detailed information, please refer to our [paper]().
+
 
 ## Citation
 If you find this useful in your research, please consider citing:

dcd_eval/README.md

@@ -0,0 +1,87 @@
+# Distillation Contrastive Decoding (DCD) Evaluation
+
+## Overview
+
+This package provides a method for evaluating the performance of Language Learning Models (LLMs) on various standard benchmarks. For more information about the evaluation process, please refer to [our DCD paper]().
+
+
+## Installation
+
+```bash
+# If you have already done this, you can skip these steps
+git clone https://github.com/pphuc25/distillation-contrastive-decoding.git
+cd distillation-contrastive-decoding
+pip install -e .
+
+# Setting up the evaluation environment
+cd dcd_eval
+bash install_packages.sh
+```
+
+## Basic Usage
+
+To evaluate the generative performance of a language model on a specific dataset (GSM8K or StrategyQA), use the following command:
+
+```bash
+python3 src/run_generation.py \
+    --model_name_or_path $model_name_or_path \
+    --task $task \
+    --ntrain $ntrain \
+    --seed $seed
+
+# Alternatively, you can use the existing bash file
+
+bash configs/combined/deepseak/quantize-strategy-deepseek-7b-base-beta08.sh
+```
+
+## Experiments
+
+### Main Arguments
+
+| Argument                        | Example                    | Description                                                                                                                                    |
+| ------------------------------- | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
+| `--model_name_or_path`          | `meta-llama/Llama-2-7b-hf` | Specifies the model to be used.                                                                                                           |
+| `--student_name_or_path`        | `TheBloke/Llama-2-7B-AWQ`  | Specifies the student model to be used. In our context, it's the quantized model.|
+| `--prompt_file`                 | `gsm8k`                    | The name of the dataset to be evaluated on the test set.|
+| `--constractive_prompt_student` | `4`                        | The types of contrastive CoT prompting for the amateur model. The number corresponds with the prompting detail in the paper (See appendix for more detail). |
+| `--outfile`                     | `output_path.json`         | The location to store the output results. |
+| `--alpha_coef`                  | `1`                        | The threshold for plausibility.                                                                                                                |
+| `--beta_coef`                   | `27`                       | The strength of the amateur model compared to the expert model or the adjustment factor for the amateur penalty.                                          |
+| `--dropout_num`                 | `0.1`                      | The dropout rate of the amateur model.                                                                                                             |
+
+### Other Arguments
+
+| Argument           | Example    | Description                                                                                           |
+| ------------------ | ---------- | ----------------------------------------------------------------------------------------------------- |
+| `--cot_flag`       | `*enable*` | Add the flag text to extract the results. By default, the flag is "The answer is ". |
+| `--fp16`           | `*enable*` | The model will run in float 16 (with quantization on the amateur model, this setting only loads on the expert model). |
+| `--bf16`           | `*enable*` | The model will run in bfloat 16 (with quantization on the amateur model, this setting only loads on the expert model). |
+| `--max_new_tokens` | `256`      | The maximum number of tokens generated by the model. |
+
+### Understanding `--constractive_prompt_student`
+
+The `--constractive_prompt_student` argument accepts an integer from 1 to 4, each corresponding to a type of contrastive prompting. By specifying different types, we can adjust the decoding behavior of the amateur model.
+
+
+#### Arithmetic Task (GSM8K)
+
+| Types | Description of Types Contrastive CoT Prompting |
+| ----- | ---------------------------------------------- |
+| 1     | Rule-based Number Shuffle.                     |
+| 2 | Rule-based Number Shuffle with Calculation Wrong |
+| 3 | Synthetic Demonstration |
+
+#### Commonsense Task (StrategyQA)
+
+| Types | Description of Types Contrastive CoT Prompting |
+| ----- | ---------------------------------------------- |
+| 1     | Synthetic Demonstration.                     |
+
+
+## Citation
+
+If you find this useful in your research, please consider citing:
+
+```
+
+```

dcd_eval/configs/combined/data_full/gsm8k/gsm8k_13b_stullama1,1.sh → dcd_eval/configs/combined/data_full/gsm8k/cd/gsm8k_13b_stullama1,1.sh

@@ -2,7 +2,7 @@
 python3 src/run_generation.py \
     --model_name_or_path meta-llama/Llama-2-13b-hf \
     --max_new_tokens 256 \
-    --prompt_file gsm8k_500 \
+    --prompt_file gsm8k \
     --student_name_or_path TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T \
     --outfile outputs/gsm8k_full_llama2-13b_stu1,1b.json \
     --cot_flag

dcd_eval/configs/combined/data_full/gsm8k/gsm8k_7b_stullama1,1.sh → dcd_eval/configs/combined/data_full/gsm8k/cd/gsm8k_7b_stullama1,1.sh

@@ -2,7 +2,7 @@
 python3 src/run_generation.py \
     --model_name_or_path meta-llama/Llama-2-7b-hf \
     --max_new_tokens 256 \
-    --prompt_file gsm8k_500 \
+    --prompt_file gsm8k \
     --student_name_or_path TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T \
     --outfile outputs/gsm8k_full_llama2-7b_stu1,1b.json \
     --cot_flag