Update README.md

README.md

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 ---
 library_name: peft
 base_model: mistralai/Mistral-7B-Instruct-v0.2
+datasets:
+- iamshnoo/alpaca-cleaned-bengali
+language:
+- bn
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# Model Card for Rashik24/Mistral-Instruct-Bangla
 
+The Rashik24/Mistral-Instruct-Bangla model is a language model specifically tailored for the Bengali language. Based on the Mistralai/Mistral-7B-Instruct-v0.2 base model, it has been fine-tuned using the iamshnoo/alpaca-cleaned-bengali dataset. This model is designed to understand and generate Bengali text, making it a valuable tool for a variety of natural language processing tasks in the Bengali language context.
 ## 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. -->
-
+The Mistral-Instruct-Bangla model is intended for a range of applications where understanding and generating Bengali text is crucial. This includes but is not limited to machine translation, content creation, sentiment analysis, and language understanding tasks in Bengali. The model is suited for both academic researchers and industry practitioners who are working on Bengali language processing.
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This model can be directly used for generating Bengali text, understanding Bengali context in conversations, and translating between Bengali and other languages. It is designed to be straightforward to implement in various software environments, requiring minimal additional setup for direct use cases.
+## How to Get Started with the Model
 
-[More Information Needed]
+To start using the Rashik24/Mistral-Instruct-Bangla model, you can use the following code as a basic guide. This will help you integrate the model into your application or research project.
 
-### Recommendations
+```Python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-<!-- 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.
+def load_model(model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return model, tokenizer
 
-## How to Get Started with the Model
+def generate_text(prompt, model, tokenizer):
+    inputs = tokenizer.encode(prompt, return_tensors='pt')
+    outputs = model.generate(inputs, max_length=50, num_return_sequences=1)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-Use the code below to get started with the model.
+#Load the model
+model_name = 'Rashik24/Mistral-Instruct-Bangla'
+model, tokenizer = load_model(model_name)
 
-[More Information Needed]
+#Example usage
+prompt = "আমি কেমন আছি?"  # Bengali for "How am I?"
+generated_text = generate_text(prompt, model, tokenizer)
+print(generated_text)
+```
 
 ## 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]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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 -->
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-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## 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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## 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. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
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-### Framework versions
+The model has been trained on the 'iamshnoo/alpaca-cleaned-bengali' dataset.
 
-- PEFT 0.7.1
+For more details on the training data and methodology, refer to the dataset card linked here:https://huggingface.co/datasets/iamshnoo/alpaca-cleaned-bengali