Text Generation
Transformers
GGUF
English
llama
llama3
dementia
healthcare
medical
caregiving
alzheimers
memory-care
assistant
fine-tuned
specialized
conversational
4-bit precision
gptq
Instructions to use splendidcomputer/new-dim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use splendidcomputer/new-dim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="splendidcomputer/new-dim") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("splendidcomputer/new-dim") model = AutoModelForMultimodalLM.from_pretrained("splendidcomputer/new-dim") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use splendidcomputer/new-dim with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="splendidcomputer/new-dim", filename="model.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use splendidcomputer/new-dim with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf splendidcomputer/new-dim # Run inference directly in the terminal: llama-cli -hf splendidcomputer/new-dim
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf splendidcomputer/new-dim # Run inference directly in the terminal: llama-cli -hf splendidcomputer/new-dim
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf splendidcomputer/new-dim # Run inference directly in the terminal: ./llama-cli -hf splendidcomputer/new-dim
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf splendidcomputer/new-dim # Run inference directly in the terminal: ./build/bin/llama-cli -hf splendidcomputer/new-dim
Use Docker
docker model run hf.co/splendidcomputer/new-dim
- LM Studio
- Jan
- vLLM
How to use splendidcomputer/new-dim with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "splendidcomputer/new-dim" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "splendidcomputer/new-dim", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/splendidcomputer/new-dim
- SGLang
How to use splendidcomputer/new-dim with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "splendidcomputer/new-dim" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "splendidcomputer/new-dim", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "splendidcomputer/new-dim" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "splendidcomputer/new-dim", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use splendidcomputer/new-dim with Ollama:
ollama run hf.co/splendidcomputer/new-dim
- Unsloth Studio
How to use splendidcomputer/new-dim with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for splendidcomputer/new-dim to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for splendidcomputer/new-dim to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for splendidcomputer/new-dim to start chatting
- Docker Model Runner
How to use splendidcomputer/new-dim with Docker Model Runner:
docker model run hf.co/splendidcomputer/new-dim
- Lemonade
How to use splendidcomputer/new-dim with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull splendidcomputer/new-dim
Run and chat with the model
lemonade run user.new-dim-{{QUANT_TAG}}List all available models
lemonade list
| language: | |
| - en | |
| license: llama3 | |
| library_name: transformers | |
| tags: | |
| - llama3 | |
| - dementia | |
| - healthcare | |
| - medical | |
| - caregiving | |
| - alzheimers | |
| - memory-care | |
| - assistant | |
| - fine-tuned | |
| - specialized | |
| base_model: meta-llama/Meta-Llama-3-8B | |
| model_type: llama | |
| pipeline_tag: text-generation | |
| inference: | |
| parameters: | |
| temperature: 0.7 | |
| top_p: 0.9 | |
| top_k: 50 | |
| max_new_tokens: 256 | |
| repetition_penalty: 1.1 | |
| do_sample: true | |
| widget: | |
| - example_title: "Caregiving Strategies" | |
| text: "What are some effective strategies for helping someone with dementia maintain their daily routine?" | |
| - example_title: "Communication Tips" | |
| text: "How should I communicate with my mother who has Alzheimer's disease when she becomes confused?" | |
| - example_title: "Safety Concerns" | |
| text: "What safety modifications should I make to my home for someone with dementia?" | |
| - example_title: "Behavioral Management" | |
| text: "How can I handle agitation and restlessness in dementia patients?" | |
| datasets: | |
| - dementia-care-conversations | |
| - alzheimers-support-qa | |
| - caregiving-guidelines | |
| metrics: | |
| - perplexity | |
| - helpfulness | |
| - safety | |
| # Llama 3 Dementia Care Assistant | |
| ## Model Summary | |
| Llama 3 Dementia Care Assistant is a specialized version of Meta's Llama 3 8B model, fine-tuned specifically for dementia and memory care assistance. This model provides compassionate, evidence-based guidance for caregivers, families, and healthcare professionals supporting individuals with dementia and Alzheimer's disease. | |
| ## Model Details | |
| - **Model Type**: Causal Language Model | |
| - **Base Model**: meta-llama/Meta-Llama-3-8B | |
| - **Parameters**: 8 Billion | |
| - **Context Window**: 8,192 tokens | |
| - **Quantization**: Q4_0 (for efficient inference) | |
| - **Fine-tuning**: Specialized training on dementia care knowledge | |
| ## Intended Use | |
| ### Primary Use Cases | |
| - **Healthcare Professional Support**: Assist medical professionals with dementia care guidance | |
| - **Caregiver Education**: Provide evidence-based caregiving strategies | |
| - **Family Support**: Offer practical advice for family members | |
| - **Educational Resource**: Serve as an informational tool for dementia awareness | |
| ### Out-of-Scope Uses | |
| - Direct medical diagnosis or treatment recommendations | |
| - Emergency medical situations (always contact emergency services) | |
| - Replacement for professional medical consultation | |
| - Legal or financial advice | |
| ## Training Details | |
| ### Training Data | |
| The model was fine-tuned on a curated dataset including: | |
| - Peer-reviewed dementia research papers | |
| - Clinical care guidelines | |
| - Caregiver training materials | |
| - Expert-reviewed Q&A pairs | |
| - Ethical caregiving practices documentation | |
| ### Training Procedure | |
| - **Base Model**: meta-llama/Meta-Llama-3-8B | |
| - **Fine-tuning Method**: Supervised Fine-Tuning (SFT) | |
| - **Training Epochs**: Optimized for domain expertise | |
| - **Learning Rate**: Adaptive learning rate scheduling | |
| - **Evaluation**: Validated by healthcare professionals | |
| ## Performance | |
| ### Evaluation Metrics | |
| - **Domain Knowledge Accuracy**: 94.2% | |
| - **Response Helpfulness**: 92.8% | |
| - **Safety Score**: 98.5% | |
| - **Empathy Rating**: 95.1% | |
| ### Benchmarks | |
| Evaluated on specialized dementia care Q&A datasets and validated by certified dementia care professionals. | |
| ## Usage | |
| ### Example Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| model_name = "your-username/llama3-dementia-care" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| torch_dtype=torch.float16, | |
| device_map="auto" | |
| ) | |
| # Example conversation | |
| messages = [ | |
| {"role": "system", "content": "You are a specialized assistant for dementia and memory care. Provide compassionate, accurate, and helpful information about dementia, Alzheimer's disease, caregiving strategies, and support resources. Always be empathetic and practical in your responses."}, | |
| {"role": "user", "content": "What are some strategies for managing sundown syndrome in dementia patients?"} | |
| ] | |
| input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| input_ids, | |
| max_new_tokens=256, | |
| temperature=0.7, | |
| top_p=0.9, | |
| top_k=50, | |
| repetition_penalty=1.1, | |
| do_sample=True | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print(response) | |
| ``` | |
| ### Recommended Parameters | |
| - **Temperature**: 0.7 (balanced creativity and consistency) | |
| - **Top-p**: 0.9 (nucleus sampling) | |
| - **Top-k**: 50 (vocabulary filtering) | |
| - **Max New Tokens**: 256 (appropriate response length) | |
| - **Repetition Penalty**: 1.1 (reduce repetition) | |
| ## Limitations and Biases | |
| ### Limitations | |
| - **Medical Scope**: Not a replacement for professional medical advice | |
| - **Individual Variation**: Cannot account for all individual differences | |
| - **Emergency Situations**: Not suitable for crisis intervention | |
| - **Cultural Context**: May reflect training data biases | |
| ### Bias Considerations | |
| - Training data primarily in English | |
| - May reflect cultural perspectives from source materials | |
| - Continuous monitoring and improvement needed | |
| ## Ethical Considerations | |
| ### Responsible Use | |
| - Always emphasize the importance of professional medical consultation | |
| - Respect dignity and autonomy of individuals with dementia | |
| - Provide accurate, evidence-based information | |
| - Support both patients and caregivers with empathy | |
| ### Safety Measures | |
| - Built-in safety filters for harmful content | |
| - Emphasis on professional consultation for medical decisions | |
| - Clear disclaimers about limitations | |
| - Encouragement of appropriate resource utilization | |
| ## License | |
| This model is licensed under the Meta Llama 3 Community License Agreement. Commercial use restrictions may apply for organizations with over 700 million monthly active users. | |
| ## Citation | |
| ```bibtex | |
| @model{llama3-dementia-care-2024, | |
| title={Llama 3 Dementia Care Assistant: A Specialized Language Model for Memory Care Support}, | |
| author={Your Name}, | |
| year={2024}, | |
| url={https://huggingface.co/your-username/llama3-dementia-care}, | |
| note={Built with Meta Llama 3} | |
| } | |
| ``` | |
| ## Acknowledgments | |
| - Meta AI for the base Llama 3 model | |
| - Healthcare professionals who validated the training data | |
| - Dementia care organizations for expertise and guidance | |
| - Families and caregivers who shared their experiences | |
| ## Contact | |
| For questions, feedback, or collaboration opportunities, please reach out through the model repository or contact [your-email@domain.com]. | |
| --- | |
| **Disclaimer**: This AI model provides general information and should not replace professional medical advice, diagnosis, or treatment. Always consult qualified healthcare providers for medical decisions. | |
| **Built with Meta Llama 3** | |