Instructions to use rootxhacker/HobbyLM-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rootxhacker/HobbyLM-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rootxhacker/HobbyLM-gguf",
	filename="HobbyLM-Base.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 rootxhacker/HobbyLM-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rootxhacker/HobbyLM-gguf
# Run inference directly in the terminal:
llama-cli -hf rootxhacker/HobbyLM-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rootxhacker/HobbyLM-gguf
# Run inference directly in the terminal:
llama-cli -hf rootxhacker/HobbyLM-gguf

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 rootxhacker/HobbyLM-gguf
# Run inference directly in the terminal:
./llama-cli -hf rootxhacker/HobbyLM-gguf

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 rootxhacker/HobbyLM-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rootxhacker/HobbyLM-gguf

Use Docker

docker model run hf.co/rootxhacker/HobbyLM-gguf

LM Studio
Jan
Ollama
How to use rootxhacker/HobbyLM-gguf with Ollama:
```
ollama run hf.co/rootxhacker/HobbyLM-gguf
```

Unsloth Studio

How to use rootxhacker/HobbyLM-gguf 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 rootxhacker/HobbyLM-gguf 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 rootxhacker/HobbyLM-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rootxhacker/HobbyLM-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use rootxhacker/HobbyLM-gguf with Docker Model Runner:
```
docker model run hf.co/rootxhacker/HobbyLM-gguf
```

Lemonade

How to use rootxhacker/HobbyLM-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rootxhacker/HobbyLM-gguf

Run and chat with the model

lemonade run user.HobbyLM-gguf-{{QUANT_TAG}}

List all available models

lemonade list

HobbyLM-GGUF

GGUF builds of every HobbyLM language model — one file per variant, all sharing the same 500M sparse-MoE core. These are the files you actually run on a laptop CPU.

File	Model	What it's for	Headline number
`HobbyLM-Base.gguf`	Base	pretrained foundation LM	44.05 avg (0-shot, our harness)
`HobbyLM-Chat.gguf`	Chat	instruction / chat	42.5 avg (alignment-tax dip from base)
`HobbyLM-Computer-Use.gguf`	Computer-Use	GUI agent + tool calling	95% name-F1, 0% param-hallucination
`HobbyLM-Omni.gguf`	Omni	multimodal core (text+image+audio)	VQAv2 47.0 / GQA 39.2
`HobbyLM-Diffusion.gguf`	Diffusion	masked-diffusion LM	117 tok/s on H100 (~2.7× AR)

Full benchmark tables, methodology, and limitations are on each model's own card (linked above).

Running them

# from https://github.com/harishsg993010/HobbyLM
hobby-rs --model HobbyLM-Chat.gguf --prompt "The capital of France is" --n 48

⚠️ These use a custom `hobbylm` architecture

Every GGUF sets general.architecture = hobbylm (all metadata keys are hobbylm.*). Stock llama.cpp will not load them — they need the from-scratch hobby-rs engine, or a llama.cpp patched to register the hobbylm arch (GQA + per-head QK-norm + sigmoid-gated MoE + aux-free routing bias + 1 shared expert + a leading dense layer). HobbyLM-Diffusion additionally carries diffusion.* metadata and needs the diffusion-aware (bidirectional, iterative-denoise) decoder.

License

Apache-2.0.

Downloads last month: 173

GGUF

Model size

0.5B params

Architecture

hobbylm

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Collection including rootxhacker/HobbyLM-gguf

HobbyLM

Collection

8 items • Updated about 9 hours ago

HobbyLM-GGUF

Running them

⚠️ These use a custom hobbylm architecture

License

Collection including rootxhacker/HobbyLM-gguf

⚠️ These use a custom `hobbylm` architecture