Update README.md

# Fine-Tuning LLaMA Model for Instruction-Based Tasks

This repository contains the code and setup for fine-tuning the pre-trained **LLaMA 3.2-1B** model on various datasets, focusing on instruction-following and conversational tasks.

---

## **Model Description**

The model fine-tunes **LLaMA 3.2-1B**, a causal language model designed for generating coherent and contextually relevant text. The fine-tuning process adapts the model to handle instruction-based tasks using datasets specifically curated for this purpose.

Key features:
- Utilizes state-of-the-art pre-trained weights from the Hugging Face Hub.
- Configured to handle instruction-based prompts efficiently.
- Fine-tuned for conversational and general-purpose tasks.

---

## **Intended Uses and Limitations**

### **Intended Uses**:
- Instruction-based question answering.
- Conversational AI applications.
- Text generation for general-purpose use cases.

### **Limitations**:
- May not perform well on domain-specific queries outside its training data.
- Could generate inaccurate or misleading responses for ambiguous or poorly framed prompts.
- Requires adequate computational resources (e.g., GPUs) for fine-tuning and inference.

---

## **Training and Evaluation Data**

### **Datasets Used**
The following datasets are used for training:
- **`fka/awesome-chatgpt-prompts`**: A collection of diverse prompts for chat-based models.
- **`BAAI/Infinity-Instruct`** (configuration: `3M`): Instruction-based datasets.
- **`allenai/WildChat-1M`**: Conversations and queries for language modeling.
- **`lavita/ChatDoctor-HealthCareMagic-100k`**: Medical instructions and conversational data.
- **`zjunlp/Mol-Instructions`**: Molecular and chemical-based instructions.
- **`garage-bAInd/Open-Platypus`**: General-purpose instruction datasets.

### **Preprocessing**
- Input prompts and responses are tokenized with padding and truncation (maximum length: 512 tokens).
- Labels are created by cloning the input IDs and masking padding tokens to ignore them during loss computation.
- Datasets without suitable columns (`prompt`) are skipped.

---

## **Training Procedure**

### **Setup**
1. **Model and Tokenizer**:
- The pre-trained model and tokenizer are loaded from Hugging Face Hub (`meta-llama/Llama-3.2-1B`).
- The tokenizer is adjusted to handle missing padding tokens by using the `eos_token`.

2. **Training Configuration**:
- Configured using Hugging Face's `TrainingArguments`:
- **Output Directory**: `llama_output` for saving checkpoints and logs.
- **Epochs**: 4 epochs for balanced training time and generalization.
- **Batch Size**: 4 examples per device.
- **Gradient Accumulation**: 4 steps for larger effective batch size.
- **Learning Rate**: 1e-4 with a warmup of 500 steps.
- **Weight Decay**: 0.01 to reduce overfitting.
- **Mixed Precision**: FP16 for faster training and reduced memory usage.
- **Logging**: Logs are generated every 10 steps.
- **Push to Hub**: Trained model is uploaded to the Hugging Face Hub (`kssrikar4/Intellecta`).

3. **Data Collation**:
- Uses `DataCollatorForSeq2Seq` to dynamically pad batches during training.

### **Fine-Tuning Process**
- The Hugging Face `Trainer` class orchestrates the training:
- Computes loss using model outputs (`logits`) and prepared labels.
- Gradients are accumulated and applied using AdamW optimizer with learning rate scheduling.
- Checkpoints are saved at the end of each epoch.

---

## **Post-Training**

1. **Model Upload**:
- The fine-tuned model, tokenizer, and configuration are pushed to the Hugging Face Hub under the ID `kssrikar4/Intellecta`.

2. **Inference**:
- The model can be directly downloaded and used for inference or further fine-tuning using the Hugging Face Transformers library.

---

## **Requirements**

### **Dependencies**:
- Python 3.8+
- Hugging Face Transformers
- Datasets
- Torch (with GPU support for fine-tuning)

### **Installation**:
```bash
pip install transformers datasets torch
```

---

## **Usage**

### **Fine-Tuning**
Run the `main` function in the provided Python script:
```bash
python llama.py
```

Ensure that the `config.json` file is present and contains your Hugging Face authentication token:
```json
{
"hf_token": "your_huggingface_token"
}
```

### **Inference**
Use the fine-tuned model for generating text:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("kssrikar4/Intellecta")
model = AutoModelForCausalLM.from_pretrained("kssrikar4/Intellecta")

inputs = tokenizer("Explain quantum physics in simple terms.", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## **License**
This project uses the Apache 2.0 license. Ensure compliance with dataset and model licensing requirements when using this project.

---

Feel free to raise an issue or submit a pull request if you have suggestions or find any bugs!

README.md

@@ -1,57 +1,153 @@
----
-library_name: transformers
-license: llama3.2
-base_model: meta-llama/Llama-3.2-1B
-tags:
-- generated_from_trainer
-model-index:
-- name: Intellecta
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# Intellecta
-
-This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 4
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 16
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 500
-- num_epochs: 4
-- mixed_precision_training: Native AMP
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.48.0
-- Pytorch 2.5.1+cpu
-- Datasets 3.2.0
-- Tokenizers 0.21.0
+---
+library_name: transformers
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B
+tags:
+- generated_from_trainer
+model-index:
+- name: Intellecta
+  results: []
+datasets:
+- fka/awesome-chatgpt-prompts
+- BAAI/Infinity-Instruct
+- allenai/WildChat-1M
+- lavita/ChatDoctor-HealthCareMagic-100k
+- zjunlp/Mol-Instructions
+- garage-bAInd/Open-Platypus
+language:
+- en
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Intellecta
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
+
+## Model description
+
+The model is based on LLaMA (Large Language Model Meta AI), a family of state-of-the-art language models developed for natural language understanding and generation. This specific implementation uses the LLaMA 3.2-1B model, which is fine-tuned for general-purpose conversational AI tasks.
+
+Architecture: Transformer-based causal language model.
+Tokenization: Uses the AutoTokenizer compatible with the LLaMA model, with adjustments to ensure proper padding.
+Pre-trained Foundation: The model builds on the pre-trained weights of LLaMA, focusing on improving performance for conversational and instruction-based tasks.
+Implementation: Developed with Hugging Face’s Transformers library for extensibility and ease of use.
+
+## Intended uses & limitations
+
+Intended Uses
+Instruction-following tasks: Can perform tasks such as answering questions, summarizing, and text generation.
+Conversational agents: Suitable for chatbots and virtual assistants, including those in specialized domains like healthcare or education.
+Research and Development: Fine-tuning and benchmarking against datasets for downstream tasks.
+
+## Training and evaluation data
+
+Datasets Used
+fka/awesome-chatgpt-prompts: General-purpose instruction-following and conversational dataset based on GPT-like interactions.
+BAAI/Infinity-Instruct (3M): A large instruction dataset containing a wide variety of tasks and instructions.
+allenai/WildChat-1M: Focused on open-ended conversational data.
+lavita/ChatDoctor-HealthCareMagic-100k: Healthcare-specific dataset for medical conversational agents.
+zjunlp/Mol-Instructions: Molecular biology-related instructions.
+garage-bAInd/Open-Platypus: Dataset aimed at general-purpose, open-domain reasoning.
+Data Preprocessing
+Text prompts and responses are tokenized with padding and truncation.
+Labels are derived from input tokens, masking padding tokens with -100 to exclude them from loss computation.
+
+## Training procedure
+The training procedure for the model fine-tunes the pre-trained LLaMA 3.2-1B model on various datasets with a focus on instruction-following and conversational tasks. Below are the key aspects of the training process:
+
+1. Preprocessing
+Tokenization:
+
+The input prompts and their responses are tokenized using the AutoTokenizer configured for LLaMA.
+Special considerations:
+Padding tokens are explicitly handled using the pad_token (set to the eos_token if undefined).
+Inputs are truncated to a maximum length of 512 tokens to fit model constraints.
+Label Preparation:
+
+Input IDs are cloned to create labels for supervised learning.
+Padding tokens in labels are masked with -100 to ensure they are ignored during loss computation.
+Dataset Mapping:
+
+Each dataset's prompt field is tokenized and reformatted into the model’s required input-output structure.
+Non-standard datasets without a prompt column are skipped to avoid errors.
+
+2. Model Setup
+Pre-trained Model:
+
+The base model, meta-llama/Llama-3.2-1B, is loaded with pre-trained weights.
+It is fine-tuned for causal language modeling, focusing on instruction-based outputs.
+Tokenizer Setup:
+
+The tokenizer ensures consistency in encoding and decoding for the model.
+Padding is fixed (using eos_token as a fallback).
+
+3. Training Configuration
+TrainingArguments:
+
+The Hugging Face TrainingArguments object is used to configure the training process:
+Output Directory: llama_output stores the model checkpoints and logs.
+Epochs: 4 epochs for a balance between training time and generalization.
+Batch Size: 4 examples per device to handle memory constraints.
+Gradient Accumulation: 4 steps to simulate a larger effective batch size.
+Learning Rate: 1e-4 with a warmup phase of 500 steps for stable optimization.
+Weight Decay: 0.01 to mitigate overfitting.
+Mixed Precision: FP16 (half-precision) is used for faster training and reduced memory usage.
+Logging Steps: Logs are generated every 10 steps to monitor training progress.
+Checkpointing: Model checkpoints are saved at the end of each epoch.
+Push to Hub: The fine-tuned model is uploaded to Hugging Face’s Hub (kssrikar4/Intellecta).
+Data Collator:
+
+The DataCollatorForSeq2Seq ensures that batches are dynamically padded for efficiency during training.
+
+4. Fine-Tuning Process
+Trainer:
+
+The Hugging Face Trainer class orchestrates the training process, combining the model, data, and training configuration.
+Loss is computed for each batch using the model's outputs (e.g., logits) and the prepared labels.
+The optimizer and learning rate scheduler are managed internally by the Trainer.
+Training Loop:
+
+During each epoch:
+The model processes batches of tokenized prompts and computes the causal language modeling (CLM) loss.
+Gradients are accumulated over multiple steps to simulate a larger batch size.
+Optimizer updates are applied after gradient accumulation.
+Validation:
+
+While validation data is not explicitly defined in the code, the Trainer supports evaluation if an eval_dataset is provided.
+Saving checkpoints at each epoch allows model evaluation post-training.
+5. Post-Training
+Push to Hub:
+
+The trained model, along with its tokenizer and configuration, is pushed to the Hugging Face Hub under the ID kssrikar4/Intellecta.
+Usage:
+
+The fine-tuned model can be downloaded and directly used for inference or further fine-tuning.
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 4
+- mixed_precision_training: Native AMP
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.48.0
+- Pytorch 2.5.1+cpu
+- Datasets 3.2.0
+- Tokenizers 0.21.0