Update README.md

README.md

@@ -1,199 +1,249 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- md-nishat-008/Bangla-Instruct
+language:
+- en
+- bn
+metrics:
+- bleu
+- accuracy
+base_model:
+- Qwen/Qwen3-1.7B
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Qwen3-1.7B-Bengali-Instruct
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+This model is a fine-tuned version of Qwen/Qwen3-1.7B on Bengali (Bangla) instruction-response pairs. It has been optimized to understand and generate natural Bengali language responses while maintaining cultural appropriateness and proper grammar. The model uses LoRA (Low-Rank Adaptation) for efficient fine-tuning on a 100K Bengali instruction dataset.
 
-### Model Sources [optional]
+- **Developed by:** Ismam Nur Swapnil
+- **Model type:** Causal Language Model (Decoder-only Transformer)
+- **Language(s):** Bengali (Bangla)
+- **License:** Same as base Qwen3-1.7B model license
+- **Finetuned from model:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Base Repository:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
+- **Training Dataset:** [swapnillo/Bangla-Instruction-Tuning-100K](https://huggingface.co/datasets/swapnillo/Bangla-Instruction-Tuning-100K)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model is designed for conversational AI applications in Bengali. It can be used for:
+- Bengali chatbots and virtual assistants
+- Question-answering systems in Bengali
+- Instruction-following tasks in Bengali language
+- General Bengali language generation tasks
 
-### Downstream Use [optional]
+The model is optimized to provide culturally appropriate responses with proper Bengali grammar and natural conversational style.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+This model can be further fine-tuned for specific Bengali NLP tasks such as:
+- Domain-specific question answering (medical, legal, educational)
+- Bengali content generation
+- Translation assistance
+- Customer service chatbots for Bengali-speaking users
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model should not be used for:
+- Generating harmful, biased, or offensive content
+- High-stakes decision making without human oversight
+- Applications requiring 100% accuracy (medical diagnosis, legal advice, etc.)
+- Languages other than Bengali (primary training is Bengali-focused)
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model's responses are limited by the quality and diversity of the training data
+- May occasionally generate factually incorrect information
+- Could reflect biases present in the training dataset
+- Performance may vary across different Bengali dialects and registers
+- Not suitable for tasks requiring real-time critical decision making
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users (both direct and downstream) should:
+- Verify critical information from the model's outputs
+- Implement content filtering for production deployments
+- Monitor for potential biases in model outputs
+- Not use the model for high-stakes decisions without human oversight
+- Test thoroughly on their specific use cases before deployment
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Load base model and tokenizer
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-1.7B",
+    trust_remote_code=True,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-1.7B", trust_remote_code=True)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "path/to/your/model")
+
+# Generate response
+messages = [
+    {"role": "system", "content": "You are a knowledgeable AI assistant fluent in Bengali language and culture."},
+    {"role": "user", "content": "বাংলাদেশের রাজধানী কোথায়?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+The model was fine-tuned on the [Bangla-Instruction-Tuning-100K dataset](https://huggingface.co/datasets/swapnillo/Bangla-Instruction-Tuning-100K), which contains approximately 100,000 Bengali instruction-response pairs covering diverse topics and conversational patterns.
 
-[More Information Needed]
+**Data Split:**
+- Training: 99% (~99,000 examples)
+- Validation: 1% (~1,000 examples)
+- Test split ratio: 0.01, seed: 42
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The model was fine-tuned using LoRA (Low-Rank Adaptation) with DeepSpeed ZeRO-3 optimization for efficient training.
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+#### LoRA Configuration
 
+- **LoRA Rank (r):** 32
+- **LoRA Alpha:** 64
+- **LoRA Dropout:** 0.1
+- **Target Modules:** q_proj, k_proj, v_proj
+- **Task Type:** Causal Language Modeling
+- **Bias:** none
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training regime:** fp16 mixed precision with DeepSpeed ZeRO-3
+- **Number of epochs:** 1
+- **Maximum training steps:** 2,700
+- **Per device train batch size:** 2
+- **Per device eval batch size:** 2
+- **Gradient accumulation steps:** 8
+- **Effective batch size:** 16 (2 × 8)
+- **Learning rate:** 1e-4
+- **Learning rate scheduler:** Cosine
+- **Warmup steps:** 100
+- **Weight decay:** 0.01
+- **Max gradient norm:** 1.0
+- **Optimizer:** AdamW (PyTorch)
+- **Max sequence length:** 1024 tokens
+- **Evaluation strategy:** Every 250 steps
+- **Logging:** Every step
+- **Checkpointing:** Every 500 steps (keeping best checkpoint only)
+
+#### Speeds, Sizes, Times
+
+- **Hardware:** Training performed on Kaggle GPU environment
+- **Optimization:** DeepSpeed ZeRO-3 for memory efficiency
+- **Data workers:** 2 with pin memory enabled
+- **Monitoring:** Weights & Biases (wandb) integration
+- **LoRA adapter size:** Significantly smaller than full model (~1-2% of parameters)
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+Validation set: 1% of the Bangla-Instruction-Tuning-100K dataset (~1,000 examples), randomly split with seed 42.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Evaluation focuses on:
+- Bengali language fluency and grammatical correctness
+- Instruction-following capability
+- Cultural appropriateness of responses
+- Response relevance and coherence
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Primary metric:** Training and validation loss
+- **Best model selection:** Based on lowest validation loss
+- **Monitoring:** Loss tracked at every step via wandb
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model was trained for 2,700 steps with evaluation every 250 steps. The best checkpoint was selected based on validation loss. Specific metrics can be viewed in the associated Weights & Biases project: "qwen-bangla-finetuning".
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Training was performed on Kaggle's GPU infrastructure with DeepSpeed ZeRO-3 optimization for improved efficiency.
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+- **Hardware Type:** GPU (Kaggle environment)
+- **Training time:** ~2,700 training steps with fp16 precision
+- **Compute Region:** Cloud-based (Kaggle)
+- **Optimization:** DeepSpeed ZeRO-3 for memory efficiency, LoRA for parameter efficiency
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+Carbon emissions could be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- **Base Architecture:** Qwen3-1.7B (1.7 billion parameters)
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Trainable Parameters:** Only LoRA adapters (~1-2% of total parameters)
+- **Objective:** Causal language modeling with instruction tuning
+- **Context Length:** 1024 tokens (during training)
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **Platform:** Kaggle GPU environment
+- **Precision:** FP16 mixed precision training
+- **Memory Optimization:** DeepSpeed ZeRO-3
 
 #### Software
 
-[More Information Needed]
+- **Framework:** PyTorch with Hugging Face Transformers
+- **Key Libraries:**
+  - `transformers`: Model and tokenizer
+  - `peft`: LoRA implementation
+  - `datasets`: Data loading
+  - `deepspeed`: Distributed training optimization
+  - `wandb`: Experiment tracking
+- **Python Version:** Compatible with transformers ecosystem
 
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+```bibtex
+@misc{qwen3-bengali-instruct,
+  author = {Ismam Nur Swapnil},
+  title = {Qwen3-1.7B-Bengali-Instruct: A Fine-tuned Bengali Language Model},
+  year = {2024},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/[your-username]/[model-name]}}
+}
+```
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Ismam Nur Swapnil
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or feedback about this model, please open an issue in the model repository or contact the developer through HuggingFace.