Update README.md

README.md

@@ -3,197 +3,98 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Meta-Llama-3-8B-for-bank
 
+This model, **Meta-Llama-3-8B-for-bank**, is a fine-tuned version of the `meta-llama/Meta-Llama-3-8B-Instruct` model. It is optimized for financial service-related tasks, enabling users to interact with the model using natural language for common financial operations such as balance inquiries, retrieving stock lists, buying stocks, and performing deposit/withdrawal transactions.
 
 ## 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]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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]
-
-### 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. -->
-
-[More Information Needed]
-
-### 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## 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. -->
-
-#### 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 -->
-
-#### 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
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### 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]
-
-### Model Architecture and Objective
+- **Model Name**: Meta-Llama-3-8B-for-bank
+- **Base Model**: `meta-llama/Meta-Llama-3-8B-Instruct`
+- **Fine-tuning Data**: Custom financial chat examples
+- **Version**: 1.0
+- **License**: [Model License (if any)]
+- **Language**: English
 
-[More Information Needed]
+### Model Type
 
-### Compute Infrastructure
+- **Architecture**: LLaMA-3
+- **Type**: Instruction-based language model
 
-[More Information Needed]
+### Model Usage
 
-#### Hardware
+This model is designed for financial service tasks such as:
 
-[More Information Needed]
+- **Balance Inquiry**:
+  - *Example*: "Can you provide the current balance for my account?"
+- **Stock List Retrieval**:
+  - *Example*: "Can you provide me with a list of my stocks?"
+- **Stock Purchase**:
+  - *Example*: "I'd like to buy stocks worth $1,000.00 in Tesla."
+- **Deposit Transactions**:
+  - *Example*: "I'd like to deposit $500.00 into my account."
+- **Withdrawal Transactions**:
+  - *Example*: "I'd like to withdraw $200.00 from my account."
+- **Transaction History**:
+  - *Example*: "I would like to view my transactions. Can you provide it?"
 
-#### Software
+### Inputs and Outputs
 
-[More Information Needed]
+- **Inputs**: Natural language queries related to financial services.
+- **Outputs**: Textual responses or actions based on the input query.
 
-## Citation [optional]
+### Fine-tuning
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+This model has been fine-tuned with a dataset specifically created to simulate financial service interactions, covering a variety of questions related to account management and stock trading.
 
-**BibTeX:**
+## Intended Use
 
-[More Information Needed]
+This model is intended for integration into financial chatbots, virtual assistants, or other systems requiring automated handling of financial queries.
 
-**APA:**
+## Limitations
 
-[More Information Needed]
+- **Domain Specificity**: The model may not perform well outside financial-related tasks.
+- **Misinterpretation Risks**: There is a potential risk of misunderstanding complex or ambiguous queries.
 
-## Glossary [optional]
+## Ethical Considerations
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- **Bias**: Trained on synthetic data, the model may not represent all user demographics.
+- **Privacy**: The model should be used in compliance with financial privacy regulations.
 
-[More Information Needed]
+## How to Use
 
-## More Information [optional]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("jeromecondere/Meta-Llama-3-8B-for-bank")
+model = AutoModelForCausalLM.from_pretrained("jeromecondere/Meta-Llama-3-8B-for-bank").to("cuda")
 
-## Model Card Authors [optional]
+# Example of usage
+name = 'Walter Sensei'
+company = 'Amazon Inc.'
+stock_value = 42.24
+messages = [
+    {'role': 'system', 'content': f'Hi {name}, I\'m your assistant how can I help you'},
+    {"role": "user", "content": f"yo, can you just give me the balance of my account?"}
+]
 
-[More Information Needed]
+# Prepare the message using the chat template
+res1 = tokenizer.apply_chat_template(messages, tokenize=False)
+print(res1+'\n\n')
 
-## Model Card Contact
+# Prepare the messages for the model
+input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
 
-[More Information Needed]
+# Inference
+outputs = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=100,
+        do_sample=True,
+        temperature=0.1,
+        top_k=50,
+        top_p=0.95
+)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])