Instructions to use Gianloko/apex-coder-1.5b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Gianloko/apex-coder-1.5b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Gianloko/apex-coder-1.5b-GGUF", filename="apex-coder-1.5b-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Gianloko/apex-coder-1.5b-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 Gianloko/apex-coder-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Gianloko/apex-coder-1.5b-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 Gianloko/apex-coder-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Gianloko/apex-coder-1.5b-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 Gianloko/apex-coder-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Gianloko/apex-coder-1.5b-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 Gianloko/apex-coder-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Gianloko/apex-coder-1.5b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gianloko/apex-coder-1.5b-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": "Gianloko/apex-coder-1.5b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
- Ollama
How to use Gianloko/apex-coder-1.5b-GGUF with Ollama:
ollama run hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
- Unsloth Studio
How to use Gianloko/apex-coder-1.5b-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 Gianloko/apex-coder-1.5b-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 Gianloko/apex-coder-1.5b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Gianloko/apex-coder-1.5b-GGUF to start chatting
- Pi
How to use Gianloko/apex-coder-1.5b-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Gianloko/apex-coder-1.5b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Gianloko/apex-coder-1.5b-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 Gianloko/apex-coder-1.5b-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 Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Gianloko/apex-coder-1.5b-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Gianloko/apex-coder-1.5b-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 "Gianloko/apex-coder-1.5b-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"
- Docker Model Runner
How to use Gianloko/apex-coder-1.5b-GGUF with Docker Model Runner:
docker model run hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
- Lemonade
How to use Gianloko/apex-coder-1.5b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.apex-coder-1.5b-GGUF-Q4_K_M
List all available models
lemonade list
ApexCoder-1.5B · GGUF Q4_K_M
Last updated: 2026-03-20 — Cycle 2
Quantized GGUF (Q4_K_M, 986 MB) for local inference with Ollama or llama.cpp. This is the recommended format for running ApexCoder locally on any hardware.
📊 Evaluation — Cycle 2
| Metric | Value |
|---|---|
| LLM-as-judge (avg) | 12.6/15 |
| Perplexity | 1.14 |
| Δ vs previous cycle | +12.6 |
| File size | 986 MB |
| Quantization | Q4_K_M (4-bit, good quality/size balance) |
By reasoning type
| Type | Status | Score | Progress |
|---|
Cycle history
| Cycle | Date | Score | PPL | Δ | vs Published |
|---|---|---|---|---|---|
| 1 | 2026-03-20 | 12.9/15 | 1.17 | +12.9 | 12.9 |
| 2 | 2026-03-20 | 12.6/15 | 1.14 | +12.6 | 13.2 |
🦙 Ollama — recommended
# Pull and run
ollama pull hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
ollama run hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
# Or use in a Modelfile for a custom system prompt
cat > Modelfile << 'EOF'
FROM hf.co/Gianloko/apex-coder-1.5b-GGUF:Q4_K_M
SYSTEM "You are ApexCoder, a world-class Salesforce platform expert specializing in Apex, LWC, SOQL, and all Salesforce coded artifacts. You write clean, production-ready, governor-limit-aware code following Salesforce best practices."
PARAMETER temperature 0.1
PARAMETER num_predict 1024
EOF
ollama create apex-coder -f Modelfile
ollama run apex-coder "Write a bulkified Apex trigger on Opportunity..."
🔧 llama.cpp
# Download the GGUF file
wget https://huggingface.co/Gianloko/apex-coder-1.5b-GGUF/resolve/main/apex-coder-1.5b-Q4_K_M.gguf
# Run with llama.cpp
./llama-cli -m apex-coder-1.5b-Q4_K_M.gguf --system-prompt "You are ApexCoder, a Salesforce expert." -p "Write a bulkified Apex trigger..." -n 512 --temp 0.1
Related repos
- 🔗 Merged model — full 16-bit model for transformers
- 🔌 LoRA adapter — lightweight adapter (~150 MB)
- 📦 Training dataset
License
Apache 2.0
- Downloads last month
- 17
4-bit
Model tree for Gianloko/apex-coder-1.5b-GGUF
Base model
Qwen/Qwen2.5-3B