mzzavaa
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otto_model_demo_1

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mzzavaa commited on Feb 17, 2025

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 ---
 base_model: instructlab/merlinite-7b-lab
 library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
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-- **Developed by:** [More Information Needed]
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 ## Uses
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 ### Direct Use
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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.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
 ### Training Data
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 ### Training Procedure
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 ## Evaluation
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 ### Testing Data, Factors & Metrics
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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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-## Technical Specifications [optional]
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 ## Model Card Contact
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 ### Framework versions
-- PEFT 0.14.0

 ---
 base_model: instructlab/merlinite-7b-lab
 library_name: peft
+license: mit
+language:
+- en
 ---
+# OTTO Model Demo 1 - LoRA Adapter
 ## Model Details
 ### Model Description
+**OTTO Model Demo 1** is a **LoRA-adapted model** fine-tuned on AWS-related queries, leveraging the [InstructLab framework](https://github.com/instructlab/instructlab). The model is built on [`instructlab/merlinite-7b-lab`](https://huggingface.co/instructlab/merlinite-7b-lab) and trained using **Low-Rank Adaptation (LoRA)** to enhance instruction-following capabilities, particularly for AWS User Group and cloud computing-related questions.
+- **Developed by:** mzzavaa
+- **Model type:** LoRA Adapter for Instruction-Tuned LLMs
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** instructlab/merlinite-7b-lab
+## Model Sources
+- **Repository:** [Hugging Face Model Page](https://huggingface.co/mzzavaa/otto_model_demo_1)
+- **Base Model:** [`instructlab/merlinite-7b-lab`](https://huggingface.co/instructlab/merlinite-7b-lab)
 ## Uses
 ### Direct Use
+This adapter is fine-tuned for **AWS-related queries and instruction-based interactions**, such as:
+- AWS User Group-related questions
+- Technical cloud computing inquiries
+- Community-driven event and FAQ automation
 ### Out-of-Scope Use
+- General-purpose conversation outside of AWS topics
+- Real-time AWS infrastructure decision-making
+## How to Get Started with the Model
+Since this is a **LoRA adapter**, it must be **loaded on top of the base model**.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained("instructlab/merlinite-7b-lab", torch_dtype="auto")
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "mzzavaa/otto_model_demo_1")
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("instructlab/merlinite-7b-lab")
+# Test inference
+input_text = "What is the typical time and venue for AWS User Group Vienna meetups?"
+inputs = tokenizer(input_text, return_tensors="pt")
+output = model.generate(**inputs)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
 ## Training Details
 ### Training Data
+- Synthetic AWS-related queries generated using `ilab data generate`
 ### Training Procedure
+- **Base Model:** `instructlab/merlinite-7b-lab`
+- **Fine-tuning Method:** `LoRA (Low-Rank Adaptation)`
+- **Precision:** `bf16` for efficiency
+- **Optimizer:** `AdamW`
+- **Learning Rate:** `2e-5`
 ## Evaluation
 ### Testing Data, Factors & Metrics
 #### Metrics
+| **Metric**       | **Value** |
+|------------------|----------|
+| **Accuracy**     | [To be tested] |
+| **Perplexity**   | [To be tested] |
+| **F1 Score**     | [To be tested] |
+## Bias, Risks, and Limitations
+### Known Limitations
+- This LoRA adapter **only works well for AWS-related topics**.
+- Since it’s a lightweight adapter, it **inherits biases** from the base model.
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{mzzavaa2024otto,
+  title={OTTO Model Demo 1 - LoRA Adapter for AWS},
+  author={mzzavaa},
+  year={2025},
+  howpublished={\url{https://huggingface.co/mzzavaa/otto_model_demo_1}}
+}
+```
 ## Model Card Contact
+For questions or feedback, reach out via Hugging Face or email at: [Your Contact Info]
 ### Framework versions
+- PEFT 0.14.0