Update README.md

README.md

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-base_model: meta-llama/Llama-3.2-1B
-library_name: peft
+base_model: meta-llama/Llama-3.2-1B  
+library_name: peft  
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
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+This model is fine-tuned using Direct Preference Optimization (DPO) and is based on the `meta-llama/Llama-3.2-1B` model. It was trained to optimize user preferences and improve interaction quality in various conversational scenarios.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
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-- **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 `meta-llama/Llama-3.2-1B`, adapted using Direct Preference Optimization (DPO). The goal of this fine-tuning was to improve the model's ability to respond more effectively and empathetically to user queries. It has been optimized using user preferences data and follows conversational modeling techniques for improved natural language understanding.
 
-### Model Sources [optional]
+- **Developed by:** Haocheng Fan  
+- **Model type:** Causal Language Model  
+- **Language(s) (NLP):** English  
+- **License:** MIT  
+- **Finetuned from model:** meta-llama/Llama-3.2-1B
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Link to the Hugging Face model repo]  
+- **Paper [optional]:** [If applicable, link to related research or documentation]  
+- **Demo [optional]:** [If a demo exists, provide a link]
 
 ## 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. -->
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 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### 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]
+This model can be used for general conversational tasks and natural language understanding. It is optimized for dialogue and Q&A scenarios, where user preferences matter for generating responses.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
+This model is not intended for high-risk applications, such as healthcare diagnostics, legal advice, or any other scenarios that require certified professional expertise. Misuse, such as generating harmful or biased content, should be avoided.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
+As with any language model, there are risks of biased outputs, especially if the fine-tuning data contains bias. The model can generate unintended or harmful responses based on the input it receives. Users should be cautious when deploying this model in sensitive applications.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-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 conduct thorough testing and validation to identify any biases or risks in the model's responses, especially in critical environments where high accuracy or fairness is required.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- 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. -->
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-[More Information Needed]
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-### 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]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **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]
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-<!-- 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
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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]
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-<!-- 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]
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-<!-- 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]
+To use the model, follow the code example below:
 
-## Model Card Contact
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-[More Information Needed]
-### Framework versions
+# Load the model from Hugging Face Hub
+model_url = "your-username/my-dpo-model"
+tokenizer = AutoTokenizer.from_pretrained(model_url)
+model = AutoModelForCausalLM.from_pretrained(model_url)
 
-- PEFT 0.10.0
+# Generate a response from the model
+input_text = "What is the capital of France?"
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids
+output = model.generate(input_ids, max_length=50)
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+print(response)