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
- llama.cpp
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
- Pi
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"
File size: 2,993 Bytes
1dbb94c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | # Heliactis 1 — the orphan-byte defect in detail
This file backs the short summary in the [model card](README.md).
## 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](https://huggingface.co/XHToken/Spark-X2.5-4B/discussions/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:
```bash
# 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.**
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