Instructions to use Gianloko/apex-coder-1.5b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

llama.cpp

How to use Gianloko/apex-coder-1.5b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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 new

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-server -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-server -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

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: 104

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Gianloko/apex-coder-1.5b-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

Qwen/Qwen2.5-Coder-3B-Instruct

Quantized

unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit

Finetuned

Gianloko/apex-coder-1.5b

Quantized

(2)

this model

Gianloko
/

apex-coder-1.5b-GGUF

ApexCoder-1.5B · GGUF Q4_K_M

📊 Evaluation — Cycle 2

By reasoning type

Cycle history

🦙 Ollama — recommended

🔧 llama.cpp

Related repos

License

Model tree for Gianloko/apex-coder-1.5b-GGUF

Dataset used to train Gianloko/apex-coder-1.5b-GGUF