Add model card README.md

README.md

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+
+---
+library_name: transformers
+tags:
+- Bangla
+- nlp
+- decoder-only
+- causal-lm
+- Lora
+- code-generation
+- agentic-ai
+- from-scratch
+metrics:
+- pass@k
+- task-completion-rate
+model_name: Sheikh-ABF
+language: bn
+license: mit
+---
+
+# Sheikh-ABF: Sheikh Artificial Bangla Foundation
+
+## Model Description
+
+**Sheikh-ABF** is a state-of-the-art, **decoder-only Transformer language model for Bangla NLP**, developed entirely **from scratch**. This project emphasizes a **Bangla-first approach**, focusing on the unique linguistic and cultural aspects of the Bengali language. The name 'Sheikh' explicitly refers to its origin as a model developed in **Bangladesh**, aiming to provide a foundational LLM for the region.
+
+### Goal
+The primary objective is to create a robust base language model capable of advanced **internal reasoning**, moving beyond simple pattern matching to understand and process information more deeply. This base model serves as a strong foundation for future fine-tuning and specialized applications.
+
+### Core Principles
+*   **No Pre-trained Weights**: Trained entirely from scratch, ensuring a truly native Bangla foundation.
+*   **Bangla-First Approach**: Optimized for Bangla, addressing its specific linguistic nuances.
+*   **Internal Reasoning**: Designed to learn explicit 'thought processes' during training via interleaved thinking.
+*   **Base Model Only**: Focused on providing a general-purpose foundation, not end-use applications.
+
+## Model Architecture
+
+The model is a **decoder-only Transformer**, styled after **GPT-2**, with approximately **~60 million parameters**.
+
+*   **Layers**: 8
+*   **Hidden Size (Embedding Dimension)**: 512
+*   **Attention Heads**: 8
+*   **Context Length (Maximum Sequence Length)**: 1024 tokens
+*   **Dropout Rate**: 0.1 (applied to residual connections, embeddings, and attention probabilities)
+
+## Tokenizer Details
+
+The tokenizer is a **SentencePiece BPE (Byte-Pair Encoding) tokenizer**, trained exclusively on a **Bangla-only corpus**. It features a **vocabulary size of 32,000** unique tokens and incorporates several mandatory special tokens:
+
+*   `<bos>`: Beginning of Sentence
+*   `<eos>`: End of Sentence
+*   `<pad>`: Padding token
+*   `<think>`: Start Thinking (for internal reasoning blocks during training)
+*   `</think>`: End Thinking
+
+These tokens are consistently used for proper parsing, context handling, and enabling advanced training strategies like loss masking.
+
+## Dataset and Mixing Ratios
+
+The training corpus is a blend of three distinct dataset types:
+
+*   **70% Raw Bangla Text**: For foundational language modeling and fluency.
+*   **20% Instruction/QA**: For improving instruction following and question answering capabilities.
+*   **10% Reasoning**: Incorporates interleaved thinking (`<think>...</think>`) patterns to foster internal reasoning processes.
+
+## Interleaved Thinking and Loss Weighting
+
+**Interleaved Thinking** is a core training strategy where explicit 'thought processes' (`<think>...</think>`) are included in the training data to teach the model logical reasoning. During inference, the model is expected to internalize this reasoning and produce direct answers without generating the `<think>` blocks.
+
+To facilitate this, a **differential loss weighting strategy** is applied:
+*   **Normal Tokens**: Loss weight of 1.0 (emphasizing accurate generation of primary content).
+*   **`<think>` Tokens**: Loss weight of 0.3 (encouraging internalization of reasoning logic without over-prioritizing explicit generation).
+
+## Training Configuration
+
+The base model was trained with efficiency and resource optimization in mind:
+
+*   **FP16 (Mixed Precision)**: Reduces memory and speeds up computations.
+*   **Gradient Checkpointing**: Further reduces memory footprint.
+*   **Gradient Accumulation Steps**: 8 (effective batch size of 16, with micro-batch size of 2).
+
+## LoRA Fine-Tuning for Coding and Agentic Workflows
+
+This model has been conceptually prepared for **LoRA (Low-Rank Adaptation) fine-tuning**, specifically targeting **coding tasks and agentic workflows**. LoRA allows for efficient adaptation by training only a small fraction of parameters while keeping the base model frozen.
+
+### LoRA Strategy
+*   **Target Modules**: `c_attn` (query, key, and value projections in attention mechanism).
+*   **Rank (`r`)**: 8
+*   **Scaling Coefficient (`lora_alpha`)**: 16
+*   **Dropout (`lora_dropout`)**: 0.05
+
+### Adapter Training Configuration (Conceptual)
+*   **Learning Rate**: `5e-4` (0.0005)
+*   **Epochs**: 5 (initial)
+*   **Effective Batch Size**: 16 (micro-batch of 2, 8 gradient accumulation steps)
+*   **Scheduler**: Linear warmup (10%) and linear decay.
+
+## Evaluation Benchmarks (Conceptual)
+
+To assess the LoRA fine-tuned model's performance on specialized tasks, hypothetical benchmarks were considered:
+
+### Coding Tasks
+*   **Benchmarks**: HumanEval-like (Bangla adaptation), LeetCode-style (simplified Bangla), Code Correction/Refactoring.
+*   **Metrics**: Functional Correctness (Pass@k), Adherence to Problem Constraints, Code Generation Quality, Safety/Security.
+
+### Agentic Workflows
+*   **Benchmarks**: Simulated Environment Tasks, Tool-Use Scenarios, Multi-step Reasoning Chains.
+*   **Metrics**: Task Completion Rate, Efficiency of Steps Taken, Correct Use of Tools, Adherence to User Intent, Robustness to Ambiguity.
+
+## Conceptual Benchmark Results
+
+Below are hypothetical performance metrics for the LoRA fine-tuned model on coding and agentic tasks. These illustrate the expected types of evaluation results.
+
+### Coding Task Metrics:
+
+![Coding Metrics](coding_metrics.png)
+
+### Agentic Task Metrics:
+
+![Agentic Metrics](agentic_metrics.png)
+
+## Usage Instructions
+
+To load and use the fine-tuned Bangla Decoder-Only Transformer model and its tokenizer from the Hugging Face Hub, you can use the `transformers` library.
+
+### Loading the Model and Tokenizer
+
+First, ensure you have the `transformers` and `torch` libraries installed. Then, you can load the model and tokenizer using their `from_pretrained` methods, specifying the `repo_id`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Define the repository ID on Hugging Face Hub
+repo_id = "likhonsheikh/bangla-decoder-only-transformer"
+
+# Load the model
+model = AutoModelForCausalLM.from_pretrained(repo_id)
+# Ensure the model is in evaluation mode and on the correct device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device).eval()
+print(f"Model loaded from {repo_id} and moved to {device}.")
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+print(f"Tokenizer loaded from {repo_id}.")
+
+# Set pad_token_id if not already set (important for generation)
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token': '<pad>'})
+    # Resize model embeddings if new tokens were added
+    model.resize_token_embeddings(len(tokenizer))
+```
+
+### Performing Text Generation
+
+Once the model and tokenizer are loaded, you can use the `model.generate()` method to create new text. It's important to prepare your prompt with the `<bos>` (beginning of sentence) token to signal the start of generation, similar to how the model was trained. The model was trained with loss masking for `<think>` tokens, meaning it focuses on generating the surrounding context rather than the content within `<think>` blocks. During inference, if the model generates a `<think>` token, it would typically generate an empty thought or move past it as it was trained to not predict its content explicitly.
+
+```python
+# Example prompt for text generation
+prompt = "<bos> বাংলাদেশের জাতীয় ফল হলো " # Bangla for: "The national fruit of Bangladesh is "
+
+# Encode the prompt
+input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+
+# Generate text
+# You can adjust parameters like max_new_tokens, num_beams, temperature, top_k, top_p
+output_ids = model.generate(
+    input_ids,
+    max_new_tokens=50, # Generate up to 50 new tokens
+    num_return_sequences=1,
+    do_sample=True, # Enable sampling for more diverse outputs
+    top_k=50, # Sample from top 50 probable tokens
+    top_p=0.95, # Sample from tokens that cumulatively exceed 95% probability
+    temperature=0.7, # Controls randomness: lower means less random
+    pad_token_id=tokenizer.pad_token_id, # Use the pad token ID
+    eos_token_id=tokenizer.eos_token_id # Stop generation at EOS token
+)
+
+# Decode the generated text
+generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=False)
+
+print("
+Generated Text:")
+print(generated_text)
+```
+
+## Future Work and Next Steps
+
+This project provides a foundational decoder-only Transformer model and a custom Bangla BPE tokenizer, trained according to the 'Sheikh-ABF Final Training Plan'. To further enhance its capabilities and utility, the following next steps and future work are suggested:
+
+1.  **Dataset Expansion and Diversification**: The current training corpus is a small placeholder. Expanding the dataset significantly with more diverse and high-quality Bangla text, covering various domains (e.g., news, literature, technical, social media), will greatly improve the model's fluency, coherence, and knowledge.
+
+2.  **Advanced Benchmarking**: Conduct comprehensive benchmarking against existing state-of-the-art Bangla NLP models across a suite of downstream tasks, such as text summarization, question answering, sentiment analysis, and machine translation. This will provide a clearer understanding of the model's strengths and weaknesses.
+
+3.  **Fine-tuning for Specific Tasks**: Fine-tune the base model on task-specific datasets to adapt it for specialized applications. For instance, fine-tuning on a Bangla chatbot dataset for conversational AI, or on a legal document corpus for legal NLP tasks.
+
+4.  **Experiment with Loss Weighting**: Further experimentation with the loss weighting strategy for `<think>` tokens is crucial. Different weighting schemes and dynamic adjustment based on training progress could lead to more effective learning of reasoning patterns.
+
+5.  **Model Optimization and Scaling**: Explore techniques for model optimization, such as knowledge distillation or quantization, to deploy the model more efficiently on resource-constrained devices. Consider scaling the model up (more layers, larger hidden size) with a larger dataset for improved performance, if computational resources allow.
+
+6.  **Integrate More Special Tokens/Structures**: Depending on specific use cases, introduce and train for additional special tokens or structural markers to guide model behavior, similar to the `<think>` tags.
+
+7.  **Human Evaluation**: Beyond automated metrics, conduct human evaluations to assess the quality of generated text, particularly focusing on the coherence and correctness of reasoning responses when `<think>` tokens are involved.