Instructions to use cheeseman182/cheese-112M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cheeseman182/cheese-112M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cheeseman182/cheese-112M",
	filename="helios_v4.5.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cheeseman182/cheese-112M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cheeseman182/cheese-112M
# Run inference directly in the terminal:
llama-cli -hf cheeseman182/cheese-112M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cheeseman182/cheese-112M
# Run inference directly in the terminal:
llama-cli -hf cheeseman182/cheese-112M

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 cheeseman182/cheese-112M
# Run inference directly in the terminal:
./llama-cli -hf cheeseman182/cheese-112M

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 cheeseman182/cheese-112M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cheeseman182/cheese-112M

Use Docker

docker model run hf.co/cheeseman182/cheese-112M

LM Studio
Jan
Ollama
How to use cheeseman182/cheese-112M with Ollama:
```
ollama run hf.co/cheeseman182/cheese-112M
```

Unsloth Studio new

How to use cheeseman182/cheese-112M 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 cheeseman182/cheese-112M 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 cheeseman182/cheese-112M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cheeseman182/cheese-112M to start chatting

Docker Model Runner
How to use cheeseman182/cheese-112M with Docker Model Runner:
```
docker model run hf.co/cheeseman182/cheese-112M
```

Lemonade

How to use cheeseman182/cheese-112M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cheeseman182/cheese-112M

Run and chat with the model

lemonade run user.cheese-112M-{{QUANT_TAG}}

List all available models

lemonade list

this is a model made by me on a 5090 (rented). its trained by scratch

its a 112m prameter model trained on huggingfaceFW on 200k rows (i could do more if i want to) and 15k rows on dolly-15k.

there will be a 400m prameter model trained on 2m rows on huggingfaceFW soon maybe once i get the money.

Downloads last month: 6

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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