Instructions to use wordsum/for-the-small-shield with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use wordsum/for-the-small-shield with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wordsum/for-the-small-shield:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wordsum/for-the-small-shield:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wordsum/for-the-small-shield:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wordsum/for-the-small-shield:Q4_K_M

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 wordsum/for-the-small-shield:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf wordsum/for-the-small-shield:Q4_K_M

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 wordsum/for-the-small-shield:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wordsum/for-the-small-shield:Q4_K_M

Use Docker

docker model run hf.co/wordsum/for-the-small-shield:Q4_K_M

LM Studio
Jan

vLLM

How to use wordsum/for-the-small-shield with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wordsum/for-the-small-shield"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wordsum/for-the-small-shield",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wordsum/for-the-small-shield:Q4_K_M

Ollama
How to use wordsum/for-the-small-shield with Ollama:
```
ollama run hf.co/wordsum/for-the-small-shield:Q4_K_M
```

Unsloth Studio

How to use wordsum/for-the-small-shield 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 wordsum/for-the-small-shield 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 wordsum/for-the-small-shield to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wordsum/for-the-small-shield to start chatting

Docker Model Runner
How to use wordsum/for-the-small-shield with Docker Model Runner:
```
docker model run hf.co/wordsum/for-the-small-shield:Q4_K_M
```

Lemonade

How to use wordsum/for-the-small-shield with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wordsum/for-the-small-shield:Q4_K_M

Run and chat with the model

lemonade run user.for-the-small-shield-Q4_K_M

List all available models

lemonade list

Atomic Chat

KalabOster commited on 2 days ago

Commit

19f2b2e

verified ·

1 Parent(s): a493048

Publish the forward.

Browse files

Files changed (1) hide show

README.md +12 -0

README.md CHANGED Viewed

@@ -22,6 +22,18 @@ language:
 model_type: llama
 quantized_by: kalaboster
 ---
 # Llama-3-for-the-small-shield (v5)

 model_type: llama
 quantized_by: kalaboster
 ---
+# Foreward
+This is part of a bard model I created with a first draft of an epic fantasy adventure novel I wrote called For The Small Shield, published in GitHub: https://github.com/wordsum/For_The_Small_Shield
+The size of the bard model is the smallest I was able to use to generate meaningful output, given the limited amount ot fine-tuing data I had to train the bard LLM, only 2,000 QA pairs: https://huggingface.co/datasets/wordsum/for-the-small-shield-instruct
+The bard LLM is not terrible, and the stories of the bard are more historically accurate for the fantasy world when the bard is given the For The Small Shield GraphRAG of Chapters: https://huggingface.co/datasets/wordsum/for-the-small-shield-chapters
+The bard is a happy mess of a song that I will revisit in a few months, with all the lessons learned from creating this bard LLM. And I will likely add bonus QA Pairs for the history of Gods and Realms from Of The Small Shield: https://www.instagram.com/ofthesmallshield
+Like the original For The Small Shield, Of The Small Shield are stories in dioramas created by me and words written by me, Kalab J. Oster, unlike the words that follow.
 # Llama-3-for-the-small-shield (v5)