Instructions to use glroland/baseball_rules with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use glroland/baseball_rules with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="glroland/baseball_rules",
	filename="unsloth.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use glroland/baseball_rules 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 glroland/baseball_rules:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf glroland/baseball_rules:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf glroland/baseball_rules:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf glroland/baseball_rules: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 glroland/baseball_rules:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf glroland/baseball_rules: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 glroland/baseball_rules:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf glroland/baseball_rules:Q4_K_M

Use Docker

docker model run hf.co/glroland/baseball_rules:Q4_K_M

LM Studio
Jan
Ollama
How to use glroland/baseball_rules with Ollama:
```
ollama run hf.co/glroland/baseball_rules:Q4_K_M
```

Unsloth Studio

How to use glroland/baseball_rules 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 glroland/baseball_rules 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 glroland/baseball_rules to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for glroland/baseball_rules to start chatting

Atomic Chat new
Docker Model Runner
How to use glroland/baseball_rules with Docker Model Runner:
```
docker model run hf.co/glroland/baseball_rules:Q4_K_M
```

Lemonade

How to use glroland/baseball_rules with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull glroland/baseball_rules:Q4_K_M

Run and chat with the model

lemonade run user.baseball_rules-Q4_K_M

List all available models

lemonade list

baseball_rules / Modelfile

glroland

(Trained with Unsloth)

489fe0d verified over 1 year ago

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348 Bytes


	FROM /content/glroland/baseball_rules/unsloth.F16.gguf
	TEMPLATE """{{ if .System }}<\|system\|>
	{{ .System }}<\|end\|>
	{{ end }}{{ if .Prompt }}<\|user\|>
	{{ .Prompt }}<\|end\|>
	{{ end }}<\|assistant\|>
	{{ .Response }}<\|end\|>
	"""
	PARAMETER stop "<\|end\|>"
	PARAMETER stop "<\|user\|>"
	PARAMETER stop "<\|assistant\|>"
	PARAMETER temperature 1.5
	PARAMETER min_p 0.1


	FROM /content/glroland/baseball_rules/unsloth.F16.gguf
	TEMPLATE """{{ if .System }}<\|system\|>
	{{ .System }}<\|end\|>
	{{ end }}{{ if .Prompt }}<\|user\|>
	{{ .Prompt }}<\|end\|>
	{{ end }}<\|assistant\|>
	{{ .Response }}<\|end\|>
	"""
	PARAMETER stop "<\|end\|>"
	PARAMETER stop "<\|user\|>"
	PARAMETER stop "<\|assistant\|>"
	PARAMETER temperature 1.5
	PARAMETER min_p 0.1