Instructions to use ApollonLabs/Heliactis-1-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ApollonLabs/Heliactis-1-4B-GGUF 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 ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApollonLabs/Heliactis-1-4B-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 ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ApollonLabs/Heliactis-1-4B-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 ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ApollonLabs/Heliactis-1-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApollonLabs/Heliactis-1-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ApollonLabs/Heliactis-1-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

Ollama
How to use ApollonLabs/Heliactis-1-4B-GGUF with Ollama:
```
ollama run hf.co/ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ApollonLabs/Heliactis-1-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ApollonLabs/Heliactis-1-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M
```

Lemonade

How to use ApollonLabs/Heliactis-1-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Heliactis-1-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ApollonLabs/Heliactis-1-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ApollonLabs/Heliactis-1-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ApollonLabs/Heliactis-1-4B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Heliactis-1-4B-GGUF / DEFECT.md

apollonlabsai

Card, defect details, ban ids, insignia

1dbb94c verified 10 days ago

preview code

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2.99 kB

Heliactis 1 — the orphan-byte defect in detail

This file backs the short summary in the model card.

What the defect is

Spark-X2.5-4B has 502 vocabulary entries that begin with an orphan UTF-8 continuation byte. When the model emits one, the output contains invalid UTF-8 (�) and llama-server answers HTTP 500. We reported it upstream: discussion #25.

Reproduction

Reproduced on the unmodified base (transformers, bf16 and fp32), greedy decoding:

Μετάφρασε στα Ελληνικά: "The dog followed him to the end of the road."
base:        Το χάρι του τον�ິດຕ              → HTTP 500
Heliactis 1: Το χάριο τον ακολούθησε…         → no broken token; "dog" is still mistranslated

What we changed

Heliactis 1 was trained with on-policy unlikelihood against those 502 tokens. Probability mass on them at the worst position we found:

	worst position	20 held-out prompts (max)
before (our unreleased intermediate fine-tune)	12.7%	0.25%
Heliactis 1	0.17%	0.02%

It is rarer, not impossible. Pushing the mass further down also damaged Greek vocabulary. We measured three training depths and kept the one that holds vocabulary inside the noise of our unreleased intermediate fine-tune (the starting point of this training):

training depth	worst-position mass	Greek vocabulary (gate ≥ 0.949)
shipped	0.0017	0.9553 ✓
deeper	0.0010	0.9480 ✗
deepest	0.0001	0.9325 ✗

Blocking the ids at inference

The 502 tokens occur 0 times in the correct tokenization of our 374,541-token Greek set, so blocking them outright costs nothing in Greek. Block them at inference to close this route completely:

# ban-ids.json ships in this repo: the 502 token ids
llama-server -m Heliactis-1-4B-Q4_K_M.gguf --jinja \
  $(python -c "import json;print(' '.join(f'-l {i}-inf' for i in json.load(open('ban-ids.json'))))")

Via the API, per request: "logit_bias": [[<id>, false], ...] for the same ids. A system prompt does not fix this: the failing test above already runs with one.

A second route: Lao

Blocking the 502 ids does not close every route to invalid UTF-8. The model can emit a token that ends mid-character and then continue with something that does not finish it. Example on Lao (greedy, official llama-server): token 21417 ends in bytes e0 ba, and the next token is Mek instead of the missing byte. llama-server then answers HTTP 500. 1,451 vocabulary entries end with an unfinished character.

In our test (one prompt per language) the Lao paragraph failed on Heliactis 1 and on our unreleased intermediate fine-tune, but not on the base model. Thai passed on all three. Do not use this model for Lao.