Update README.md

README.md

@@ -3,200 +3,57 @@ base_model: unsloth/Llama-3.2-3B-Instruct
 library_name: peft
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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+# Model Card for LLAMAdolu-3B
 
+LLAMAdolu-3B is a fine-tuned Llama 3.2:3B model, designed to process and analyze a specialized dataset of 1003 entities. It has been optimized for structured tasks involving hypothesis analysis and regional language processing, specifically targeting niche applications in data-driven environments.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
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-
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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]
+LLAMAdolu-3B has been fine-tuned to handle structured datasets for hypothesis testing and can leverage regional words to generate professional e-commerce descriptions. The fine-tuning involved 1003 entities, and the model has been developed for specialized business environments where accurate data analysis and natural language generation are critical.
 
-### Model Sources [optional]
+- **Developed by:** LLAMAdolu
+- **Model type:** Llama 3.2: 3B, Fine-Tuned
+- **Language(s) (NLP):** English, Regional dialects (Trabzon dialect support)
+- **License:** [Specify license type]
+- **Finetuned from model:** unsloth/Llama-3.2-3B-Instruct
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Repository link here]
+- **Paper [optional]:** N/A
+- **Demo [optional]:** N/A
 
 ## 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]
+The model can be used directly for structured hypothesis testing, as well as for natural language generation tasks like improving e-commerce descriptions with regional dialect support.
 
-<!-- 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]
+Users can further fine-tune the model for various NLP tasks involving regional language generation, data analysis, and hypothesis testing for niche business and regional markets.
 
 ### 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 suitable for applications requiring common colloquial language generation without regional adaptation. It also may not work effectively for unstructured text or generalized chatbot tasks without fine-tuning.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
+The model may exhibit bias related to regional language features, and care should be taken when applying it to broader contexts outside its intended use. The model may also struggle with unfamiliar entities or domains that were not part of the original training data.
 
 ### 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 be aware of potential bias toward the regional dialect incorporated into the model and its limitations in handling out-of-scope domains. It is recommended to carefully evaluate the model's performance in a specific business or regional context before deployment.
 
 ## 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
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-<!-- 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
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- 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).
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-- **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]
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-## Model Card Contact
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
-### Framework versions
+tokenizer = AutoTokenizer.from_pretrained("LLAMAdolu/LLAMAdolu-3B")
+model = AutoModelForCausalLM.from_pretrained("LLAMAdolu/LLAMAdolu-3B")
 
-- PEFT 0.13.0
+inputs = tokenizer("Example input text", return_tensors="pt")
+outputs = model.generate(**inputs)