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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- 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.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[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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[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]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **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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#### Hardware
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#### Software
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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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## 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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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.15.2
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language: en
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license: apache-2.0
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library_name: transformers
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tags:
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- chatbot
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- caregiver-support
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- autism-support
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- conversational
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- mental-health
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- support-chatbot
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pipeline_tag: text-generation
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widget:
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- text: "### Question:\nI'm feeling overwhelmed with my autistic child's needs\n### Answer:\n"
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example_title: "Caregiver Support"
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- text: "### Question:\nMy child is having a meltdown, what should I do?\n### Answer:\n"
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example_title: "Meltdown Support"
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- text: "### Question:\nI'm exhausted and don't know what to do\n### Answer:\n"
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example_title: "Caregiver Burnout"
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# Rafiki Chatbot 🤗
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<div align="center">
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*A supportive AI assistant for caregivers of autistic children*
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[](https://huggingface.co/Tizzu668/rafiki-chatbot)
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[](https://opensource.org/licenses/Apache-2.0)
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</div>
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## 🌟 Overview
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Rafiki Chatbot is a fine-tuned language model specifically designed to provide emotional support, practical advice, and compassionate responses to caregivers of autistic children. The name "Rafiki" means "friend" in Swahili, reflecting the chatbot's supportive and friendly nature.
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## 🎯 Purpose
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This model helps caregivers who are:
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- Feeling overwhelmed or exhausted
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- Seeking strategies for managing challenging behaviors
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- Looking for emotional support and validation
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- Needing practical advice for daily caregiving challenges
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- Seeking understanding and community
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## 🏗️ Model Details
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- **Base Model:** [microsoft/phi-3-mini-4k-instruct](https://huggingface.co/microsoft/phi-3-mini-4k-instruct)
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- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
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- **Training Data:** Custom caregiver support conversations
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- **Language:** English
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- **Context Length:** 4,000 tokens
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## 📊 Training Data
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The model was fine-tuned on a carefully curated dataset including:
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- Caregiver support conversations
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- Autism parenting strategies
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- Emotional support dialogues
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- Crisis management scenarios
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- Daily routine optimization tips
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## 🚀 Usage
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### Using the Inference API
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```python
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from transformers import pipeline
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# Initialize the chatbot
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chatbot = pipeline(
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"text-generation",
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model="Tizzu668/rafiki-chatbot",
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device_map="auto"
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)
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# Example conversation
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prompt = "### Question:\nI'm feeling overwhelmed with my autistic child's constant needs\n### Answer:\n"
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response = chatbot(
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prompt,
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max_new_tokens=128,
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temperature=0.7,
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do_sample=True
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)
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print(response[0]['generated_text'])
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