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, 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

new-dim

File size: 2,732 Bytes

fa9878d

# 📋 Repository Summary

## 🎯 What's Included

This repository contains all the necessary files to publish your **Llama 3 Dementia Care Assistant** model on Hugging Face.

## 📁 File Overview

### Essential Hugging Face Files
- ✅ **README.md** - Comprehensive model card with YAML frontmatter
- ✅ **config.json** - Model architecture configuration 
- ✅ **tokenizer_config.json** - Tokenizer settings
- ✅ **special_tokens_map.json** - Special tokens mapping
- ✅ **generation_config.json** - Generation parameters

### Documentation & Usage
- 📚 **UPLOAD_GUIDE.md** - Step-by-step upload instructions
- 🐍 **usage_example.py** - Python usage examples and interactive demo
- 📦 **requirements.txt** - Required Python packages
- ℹ️ **SUMMARY.md** - This overview file

### Model Information
- 🔧 **Modelfile** - Original Ollama model configuration
- 📋 **model_info.json** - Structured model metadata
- 📄 **NOTICE** - License attribution notice

### Utilities
- 🔧 **export_model.sh** - Script to export Ollama model data
- 🙈 **.gitignore** - Git ignore rules

## ⚡ Quick Start

1. **Read the Upload Guide**: Start with `UPLOAD_GUIDE.md` for complete instructions

2. **Create Hugging Face Repo**: 
   ```bash
   # Go to https://huggingface.co/new
   # Create a new model repository
   ```

3. **Clone and Copy Files**:
   ```bash
   git clone https://huggingface.co/your-username/your-repo-name
   cp * /path/to/your-repo/
   ```

4. **Convert Model** (Most Important):
   ```bash
   ./export_model.sh  # Export Ollama model info
   # Then convert Ollama model to PyTorch format
   ```

5. **Upload to Hugging Face**:
   ```bash
   git add .
   git commit -m "Add Llama 3 Dementia Care model"
   git push
   ```

## ⚠️ Important Notes

### Model Conversion Required
Your Ollama model (`llama3-dementia-care:latest`) needs to be converted to PyTorch/Safetensors format for Hugging Face. See the upload guide for conversion options.

### What's Missing
- **Model weights** (*.bin or *.safetensors files)
- **Tokenizer model** (may be included in Llama 3 base)

### License Compliance
- ✅ Includes Meta Llama 3 Community License attribution
- ✅ "Built with Meta Llama 3" notice included
- ✅ Proper medical disclaimers added

## 🚀 Next Steps

1. Follow `UPLOAD_GUIDE.md` completely
2. Convert your Ollama model to Hugging Face format
3. Test the model after upload
4. Share with the community!

## 📞 Support

- **Hugging Face Docs**: https://huggingface.co/docs
- **Model Conversion**: Use ollama-export tools or community converters
- **Issues**: Check the upload guide troubleshooting section

---

**Ready to share your specialized dementia care assistant with the world! 🌟**