Instructions to use splendidcomputer/new-dim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("splendidcomputer/new-dim")
model = AutoModelForCausalLM.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

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 new

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

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

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

@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

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Model size

8B params

Architecture

llama

Hardware compatibility

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