Instructions to use RhinoWithAcape/helium-1-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RhinoWithAcape/helium-1-2b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RhinoWithAcape/helium-1-2b-GGUF",
	filename="helium-1-2b.Q2_K.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 RhinoWithAcape/helium-1-2b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RhinoWithAcape/helium-1-2b-GGUF: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 RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RhinoWithAcape/helium-1-2b-GGUF: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 RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use RhinoWithAcape/helium-1-2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RhinoWithAcape/helium-1-2b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RhinoWithAcape/helium-1-2b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M

Ollama
How to use RhinoWithAcape/helium-1-2b-GGUF with Ollama:
```
ollama run hf.co/RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use RhinoWithAcape/helium-1-2b-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 RhinoWithAcape/helium-1-2b-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 RhinoWithAcape/helium-1-2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RhinoWithAcape/helium-1-2b-GGUF to start chatting

Docker Model Runner
How to use RhinoWithAcape/helium-1-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M
```

Lemonade

How to use RhinoWithAcape/helium-1-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RhinoWithAcape/helium-1-2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.helium-1-2b-GGUF-Q4_K_M

List all available models

lemonade list

Helium-1-2B — GGUF

🟢 Fits on: every GPU class — even integrated graphics. Runs on phones at Q2_K.

GGUF conversion of kyutai/helium-1-2b — Kyutai's lightweight 2B base language model targeting edge and mobile devices, with native support for all 24 official EU languages.

This is a community quantization. The base model is by Kyutai (creators of Mimi, Moshi, and the Kyutai TTS/STT family). Until now, only MLX (Apple Silicon) variants existed — this fills the GGUF gap for llama.cpp and ollama users.

Model details

Field	Value
Architecture	`LlamaForCausalLM` (standard Llama; works with stock llama.cpp)
Parameters	2B
Layers	28
Hidden size	2048
Vocab	64,000 (multilingual)
Context	4K
Type	Base model — not instruction-tuned
License	CC-BY-SA 4.0 + Gemma Terms of Use (Helium is distilled from Gemma 2)

Use case

Edge / mobile inference — fits comfortably on consumer hardware, including phones and small GPUs
EU multilingual base — train your own instruction-following model on top of this with the language coverage you need
Research — distillation lineage from Gemma 2 with smaller footprint
Not for chat out-of-the-box — this is a base model, no instruction tuning. For chat, fine-tune it first.

Quants

Quant	Size	Use case
Q2_K	~0.8 GB	tiniest footprint — phones, microcontrollers, 4 GB cards
Q3_K_M	~1.0 GB	balance for 6 GB cards
Q4_K_M	~1.2 GB	recommended default — fits anywhere
Q5_K_M	~1.5 GB	quality bump if you have headroom
Q6_K	~1.8 GB	near-lossless
Q8_0	~2.3 GB	reference quality
F16	~4.0 GB	full precision

Usage — Ollama

hf download RhinoWithAcape/helium-1-2b-GGUF \
  helium-1-2b.Q4_K_M.gguf Modelfile --local-dir ./helium
cd ./helium
ollama create helium-1-2b:Q4_K_M -f Modelfile
ollama run helium-1-2b:Q4_K_M "Once upon a time"

Usage — llama.cpp

./build/bin/llama-completion \
    -m helium-1-2b.Q4_K_M.gguf \
    -p "The capital of France is" \
    -n 30 --temp 0.6

(Sample: "The capital of France is Paris...")

License notes

This conversion is CC-BY-SA 4.0 (matching the source release).
Helium-1 is distilled from Gemma 2, so use is also subject to the Gemma Terms of Use.
This GGUF inherits both terms.

Conversion details

Source: kyutai/helium-1-2b (downloaded 2026-04-29; Q2_K + Q3_K_M backfilled 2026-05-02)
Tools: stock llama.cpp (no patches required — standard Llama arch)
Steps: convert_hf_to_gguf.py → llama-quantize

Acknowledgments

Kyutai for the open release of Helium-1, targeting under-served EU language coverage at edge scale
Google DeepMind for the Gemma 2 base from which Helium was distilled
llama.cpp maintainers

Downloads last month: 184

GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for RhinoWithAcape/helium-1-2b-GGUF

Base model

kyutai/helium-1-2b

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this model

RhinoWithAcape
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helium-1-2b-GGUF