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 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 / UPLOAD_GUIDE.md

splendidcomputer

Upload folder using huggingface_hub

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Hugging Face Upload Guide

Prerequisites

Hugging Face Account: Create an account at https://huggingface.co
Git LFS: Install Git Large File Storage for handling large model files
```
git lfs install
```
Hugging Face CLI: Install the Hugging Face CLI
```
pip install huggingface_hub[cli]
```

Step 1: Create a New Model Repository

Go to https://huggingface.co/new
Choose "Model" as the repository type
Name your repository (e.g., llama3-dementia-care)
Set it to Public or Private as desired
Click "Create Repository"

Step 2: Clone Your Repository

git clone https://huggingface.co/your-username/llama3-dementia-care
cd llama3-dementia-care

Step 3: Copy Repository Files

Copy all the files from this directory to your cloned Hugging Face repository:

# From your LLAMA3_DEMENTIA_SHARE directory
cp README.md /path/to/your-username/llama3-dementia-care/
cp config.json /path/to/your-username/llama3-dementia-care/
cp tokenizer_config.json /path/to/your-username/llama3-dementia-care/
cp special_tokens_map.json /path/to/your-username/llama3-dementia-care/
cp Modelfile /path/to/your-username/llama3-dementia-care/
cp model_info.json /path/to/your-username/llama3-dementia-care/
cp usage_example.py /path/to/your-username/llama3-dementia-care/
cp requirements.txt /path/to/your-username/llama3-dementia-care/
cp NOTICE /path/to/your-username/llama3-dementia-care/
cp .gitignore /path/to/your-username/llama3-dementia-care/

Step 4: Add Model Weights (Critical Step)

This is the most complex part. You have several options:

Option A: Convert Ollama Model (Recommended)

Run the export script:
```
./export_model.sh
```
Use a conversion tool like ollama-export or similar to convert your Ollama model to PyTorch format

Common conversion commands:

# Example conversion (may vary based on tool)
ollama export llama3-dementia-care:latest model.gguf
# Then convert GGUF to PyTorch format using appropriate tools

Option B: Use Base Model + Fine-tuning Weights

Download the base Llama 3 8B model from Hugging Face
Add your fine-tuning weights/adapters
Upload the complete model

Option C: Re-create the Model

Start with the official Llama 3 8B model
Fine-tune it using your dementia care dataset
Upload the fine-tuned result

Step 5: Set up Git LFS for Large Files

cd your-username/llama3-dementia-care
git lfs track "*.bin"
git lfs track "*.safetensors"
git lfs track "*.gguf"
git add .gitattributes

Step 6: Commit and Push

git add .
git commit -m "Add Llama 3 Dementia Care Assistant model"
git push

Step 7: Update Model Card

Go to your model page on Hugging Face
Edit the README.md if needed
Add any additional information about training data, evaluation metrics, etc.
Test the inference widget with sample prompts

Sample Model Files You Need

For a complete Hugging Face model, you typically need:

✅ README.md (with YAML frontmatter)
✅ config.json
✅ tokenizer_config.json
✅ special_tokens_map.json
⚠️ pytorch_model.bin or model.safetensors (converted model weights)
⚠️ tokenizer.model or tokenizer.json (if needed)
✅ Optional: generation_config.json, training_args.bin

Testing Your Model

After upload, test your model:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "your-username/llama3-dementia-care"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Test with a dementia care question
prompt = "What are some strategies for managing sundown syndrome?"
# ... rest of inference code

Troubleshooting

Common Issues:

Large file errors: Make sure Git LFS is properly configured
Token errors: Use huggingface-cli login to authenticate
Model loading errors: Ensure all config files are correct
Inference issues: Test the model locally before uploading

Getting Help:

Hugging Face Documentation: https://huggingface.co/docs
Community Forum: https://discuss.huggingface.co
Discord: https://discord.gg/huggingface

Important Notes

License Compliance: Ensure your model respects the Llama 3 Community License
Attribution: Always include "Built with Meta Llama 3" as required
Medical Disclaimers: Include appropriate disclaimers for medical/health content
Model Safety: Test thoroughly before public release

Final Checklist

Repository created on Hugging Face
All configuration files uploaded
Model weights converted and uploaded
README.md is complete and accurate
License information is included
Model card is comprehensive
Inference widget works
Example usage is provided
Appropriate disclaimers are included

Good luck with your model upload! 🚀