Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 datasets:
-- USERNAME/QueryBridge
+- aorogat/QueryBridge
 ---
 
 # Model Overview
@@ -28,7 +28,7 @@ The tagged questions in the QueryBridge dataset are designed to train language m
 | `<ref>`| **References**: Tags in questions that refer back to previously mentioned entities or concepts. These can indicate cycles or self-references in queries. Example: In "Who is the CEO of the company founded by himself?", the word 'himself' is tagged as `<ref>himself</ref>`. |
 
 
-## How to use the model?
+# How to use the model?
 To use the model, you can run it with TorchTune commands. I have provided the necessary Python code to automate the process. Follow these steps to get started:
 
 <details>
@@ -168,50 +168,58 @@ python command.py
 </details>
 
 
-## How we finetuned the model?
+# How We Fine-Tuned the Model
+
+We fine-tuned the `Meta-Llama-3-8B` model by two key steps: preparing the dataset and executing the fine-tuning process.
+
+### Prepare the Dataset
+
+For this fine-tuning, we utilized the [QueryBridge dataset](https://huggingface.co/datasets/USERNAME/QueryBridge), specifically the pairs of questions and their corresponding tagged questions. However, before we can use this dataset, it is necessary to convert the data into instruct prompts suitable for fine-tuning the model. You can find these prompts at [this link](https://huggingface.co/datasets/aorogat/Questions_to_Tagged_Questions_Prompts). Download the prompts and save them in the directory: `/home/YOUR_USERNAME/data`
+
+### Fine-Tune the Model
+
+To fine-tune the `Meta-Llama-3-8B` model, we leveraged [Torchtune](https://pytorch.org/torchtune/stable/index.html). Follow these steps to complete the process:
 
 
 <details>
   <summary>Steps</summary>
-  
-### Model Configuration
-See https://pytorch.org/torchtune/stable/tutorials/e2e_flow.html to know how to use torchtune.
 
-To finetune the model:
-- Download the model:
-  tune download \
+
+### Step 1: Download the Model
+Begin by downloading the model with the following command. Replace `<ACCESS TOKEN>` with your actual Huggingface token and adjust the output directory as needed:
+
+```bash
+tune download \
   meta-llama/Meta-Llama-3-8B \
   --output-dir /home/YOUR_USERNAME/Meta-Llama-3-8B \
   --hf-token <ACCESS TOKEN>
-  
-- Prepare the config file.
+```
 
-### Download config file
-Run the command:
+### Step 2: Prepare the Configuration File
+Next, you need to set up a configuration file. Start by downloading the default configuration:
+
+```bash
 tune cp llama3/8B_lora_single_device custom_config.yaml
+```
+Then, open custom_config.yaml and update it as follows:
 
-Update the file as follows:
-<details>
-  <summary>Configuration File</summary>
 ```yaml
 # Config for single device LoRA finetuning in lora_finetune_single_device.py
 # using a Llama3 8B model
 #
-# This config assumes that you've run the following command before launching
-# this run:
+# Ensure the model is downloaded using the following command before launching:
 #   tune download meta-llama/Meta-Llama-3-8B --output-dir /tmp/Meta-Llama-3-8B --hf-token <HF_TOKEN>
 #
-# To launch on a single device, run the following command from root:
+# To launch on a single device, run this command from the root directory:
 #   tune run lora_finetune_single_device --config llama3/8B_lora_single_device
 #
-# You can add specific overrides through the command line. For example
-# to override the checkpointer directory while launching training
-# you can run:
+# You can add specific overrides through the command line. For example,
+# to override the checkpointer directory, use:
 #   tune run lora_finetune_single_device --config llama3/8B_lora_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
 #
-# This config works only for training on single device.
+# This config is for training on a single device.
 
-\# Model Arguments
+# Model Arguments
 model:
   _component_: torchtune.models.llama3.lora_llama3_8b
   lora_attn_modules: ['q_proj', 'v_proj']
@@ -220,7 +228,7 @@ model:
   lora_rank: 8
   lora_alpha: 16
 
-\# Tokenizer
+# Tokenizer
 tokenizer:
   _component_: torchtune.models.llama3.llama3_tokenizer
   path: /home/YOUR_USERNAME/Meta-Llama-3-8B/original/tokenizer.model
@@ -236,7 +244,7 @@ checkpointer:
   model_type: LLAMA3
 resume_from_checkpoint: False
 
-\# Dataset and Sampler
+# Dataset and Sampler
 dataset:
   _component_: torchtune.datasets.instruct_dataset
   split: train
@@ -247,7 +255,7 @@ seed: null
 shuffle: True
 batch_size: 1
 
-\# Optimizer and Scheduler
+# Optimizer and Scheduler
 optimizer:
   _component_: torch.optim.AdamW
   weight_decay: 0.01
@@ -259,75 +267,37 @@ lr_scheduler:
 loss:
   _component_: torch.nn.CrossEntropyLoss
 
-\# Training
+# Training
 epochs: 1
 max_steps_per_epoch: null
 gradient_accumulation_steps: 64
 compile: False
 
-\# Logging
+# Logging
 output_dir: /home/YOUR_USERNAME/lora_finetune_output
 metric_logger:
   _component_: torchtune.utils.metric_logging.DiskLogger
   log_dir: ${output_dir}
 log_every_n_steps: null
 
-\# Environment
+# Environment
 device: cuda
 dtype: bf16
 enable_activation_checkpointing: True
 
-\# Profiler (disabled)
+# Profiler (disabled)
 profiler:
   _component_: torchtune.utils.profiler
   enabled: False
 ```
-</summary>
-</details>
-
-Run the finetune: tune run lora_finetune_single_device --config /home/YOUR_USERNAME/.../custom_config.yaml
-Inference Configuration
-Copy the generation config: tune cp generation ./custom_generation_config.yaml
-
-Update the file:
-```yaml
-# Config for running the InferenceRecipe in generate.py to generate output from an LLM
-#
-# To launch, run the following command from root torchtune directory:
-#    tune run generate --config generation
 
-# Model arguments
-model:
-  _component_: torchtune.models.llama3.llama3_8b
+### Step 3: Run the Finetuning Process
+After configuring the file, you can start the finetuning process with the following command:
 
-checkpointer:
-  _component_: torchtune.utils.FullModelMetaCheckpointer
-  
-  checkpoint_dir: /home/YOUR_USERNAME/Meta-Llama-3-8B/
-  checkpoint_files: [
-    meta_model_0.pt
-  ]
-  output_dir: /home/YOUR_USERNAME/Meta-Llama-3-8B/
-  model_type: LLAMA3
-
-device: cuda
-dtype: bf16
-
-seed: 1234
-
-# Tokenizer arguments
-tokenizer:
-  _component_: torchtune.models.llama3.llama3_tokenizer
-  path: /home/YOUR_USERNAME/Meta-Llama-3-8B/original/tokenizer.model
-
-# Generation arguments; defaults taken from gpt-fast
-prompt: "### Instruction: \nYou are a powerful model trained to convert questions to tagged questions. Use the tags as follows: \n<qt> to surround question keywords like 'What', 'Who', 'Which', 'How many', 'Return' or any word that represents requests. \n<o> to surround entities as an object like person name, place name, etc. It must be a noun or a noun phrase. \n<s> to surround entities as a subject like person name, place name, etc. The difference between <s> and <o>, <s> only appear in yes/no questions as in the training data you saw before. \n<cc> to surround coordinating conjunctions that connect two or more phrases like 'and', 'or', 'nor', etc. \n<p> to surround predicates that may be an entity attribute or a relationship between two entities. It can be a verb phrase or a noun phrase. The question must contain at least one predicate. \n<off> for offset in questions asking for the second, third, etc. For example, the question 'What is the second largest country?', <off> will be located as follows. 'What is the <off>second</off> largest country?' \n<t> to surround entity types like person, place, etc. \n<op> to surround operators that compare quantities or values, like 'greater than', 'more than', etc. \n<ref> to indicate a reference within the question that requires a cycle to refer back to an entity (e.g., 'Who is the CEO of a company founded by himself?' where 'himself' would be tagged as <ref>himself</ref>). \nInput: Which films directed by a director died in 2014 and starring both Julia Roberts and Richard Gere?\nResponse:"
-max_new_tokens: 100
-temperature: 0.6 # 0.8 and 0.6 are popular values to try
-top_k: 1
-
-quantizer: null
+```bash
+tune run lora_finetune_single_device --config /home/YOUR_USERNAME/.../custom_config.yaml
 ```
 
-Run the generation: tune run generate --config /home/YOUR_USERNAME/.../custom_generation_config.yaml
+The new model can be found in `/home/YOUR_USERNAME/Meta-Llama-3-8B/` directory.
+
 </details>