Update README.md

README.md

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 ---
 library_name: transformers
 tags:
-  - apache-2.0
-  - datasets
-  - transformers
-  - peft
-  - trl
-  - torch
-  - wandb
-  - ipex
+- peft
+- trl
+- torch
+- wandb
+- ipex
+license: apache-2.0
+language:
+- en
+base_model:
+- NousResearch/Llama-2-7b-hf
+datasets:
+- mlabonne/mini-platypus
+pipeline_tag: text-generation
 ---
 
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for Fine-Tuned Llama-2-7b Model
 
 ## 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]
-
-### Model Sources [optional]
+This model is a fine-tuned version of the Llama-2-7b model, specifically adapted for causal language modeling tasks. The fine-tuning utilizes the PEFT (Parameter-Efficient Fine-Tuning) technique with LoRA (Low-Rank Adaptation) to optimize performance while reducing computational costs. The training was conducted using the `mlabonne/mini-platypus` dataset and incorporates features such as integration with W&B for experiment tracking and Intel's Extension for PyTorch (IPEX) for enhanced performance.
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Md. Jannatul Nayem
+- **Model type:** Causal Language Model
+- **Language(s) (NLP):** Engish
+- **License:** Apache 2.0
+- **Finetuned from model :** NousResearch/Llama-2-7b-hf
 
 ## 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]
+The model can be utilized for text generation tasks where the generation of coherent and contextually relevant text is required. This includes applications like chatbots, content creation, and interactive storytelling.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+When fine-tuned, this model can serve in larger ecosystems for tasks like personalized dialogue systems, question answering, and other natural language understanding applications.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is not intended for use in generating harmful or misleading content, and users should exercise caution to prevent misuse in sensitive areas such as misinformation or hate speech.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This model may exhibit biases inherent in the training data and should be evaluated thoroughly before deployment. Users should be aware of the potential risks and limitations associated with its use.
 
 ### 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 should consider implementing bias mitigation strategies and ensure thorough evaluation of the model's outputs, especially in sensitive applications.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the following code snippet to get started with loading and using the model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "nayem-ng/mdjannatulnayem_llama2_7b_finetuned_casuallm_lora"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-[More Information Needed]
+# Example of generating text
+input_text = "Your prompt here"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## 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. -->
-
-[More Information Needed]
+The model was fine-tuned using the mlabonne/mini-platypus dataset, which consists of diverse text inputs designed to enhance the model's capabilities in conversational settings
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
+The training utilized a supervised fine-tuning procedure with the following hyperparameters:
 
-[More Information Needed]
+Training regime: bf16 mixed precision
+Number of epochs: 1
+Batch size: 10
+Learning rate: 2e-4
+Warmup steps: 10
+Gradient accumulation steps: 1
 
 
 #### 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]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+Training regime: bf16 mixed precision
+Explanation: The model was trained using bfloat16 (bf16) mixed precision, which allows for faster training times and reduced memory usage compared to traditional fp32 (float32). This precision format is particularly beneficial when working with large models, as it helps to maintain numerical stability while optimizing performance on compatible hardware.
 
-#### Testing Data
+Number of epochs: 1
 
-<!-- This should link to a Dataset Card if possible. -->
+Batch size: 10
 
-[More Information Needed]
+Learning rate: 2e-4
 
-#### Factors
+Warmup steps: 10
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Gradient accumulation steps: 1
 
-[More Information Needed]
+Evaluation strategy: Evaluations are performed every 1000 steps to monitor the model's performance during training.
 
-#### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+### Testing Data
 
-[More Information Needed]
+Dataset Used: The evaluation was conducted using the same dataset, mlabonne/mini-platypus, used for training. This dataset is suitable for assessing the model's performance on casual language generation tasks.
+[mlabonne/mini-platypus](https://huggingface.co/datasets/mlabonne/mini-platypus)
 
-### Results
 
-[More Information Needed]
+## Model Examination
 
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
+Further interpretability studies can be conducted to understand decision-making processes within the model's responses.
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model is based on the Transformer architecture, specifically designed for Causal Language Modeling (CLM).
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Intel® Tiber™ AI Cloud
 
 #### Hardware
 
-[More Information Needed]
+Intel(R) Xeon(R) Platinum 8480+
 
 #### Software
 
-[More Information Needed]
-
-## 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. -->
-
-**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]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+PyTorch: A popular deep learning framework providing flexibility and support for dynamic computation graphs.
+Transformers Library (from Hugging Face): Used for loading pre-trained models and tokenizers, enabling easy model training and fine-tuning.
+PEFT Library: Specifically designed for efficient fine-tuning techniques like LoRA (Low-Rank Adaptation).
+TRL Library: For supervised fine-tuning training routines.
+WandB: Utilized for experiment tracking and visualizing training metrics.
+Intel Extension for PyTorch (IPEX): Optimizes performance on Intel hardware, enhancing training efficiency.
 
 ## Model Card Contact
 
-[More Information Needed]
+Md. Jannatul nayem | [Mail](nayemalimran106@gmail.com) | [LinkedIn](https://www.linkedin.com/in/md-jannatul-nayem)