Instructions to use neuralbroker/blitzkode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neuralbroker/blitzkode with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="neuralbroker/blitzkode",
	filename="blitzkode.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

llama-cpp-python

How to use neuralbroker/blitzkode with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="neuralbroker/blitzkode",
	filename="blitzkode.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 neuralbroker/blitzkode with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf neuralbroker/blitzkode
# Run inference directly in the terminal:
llama-cli -hf neuralbroker/blitzkode

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf neuralbroker/blitzkode
# Run inference directly in the terminal:
llama-cli -hf neuralbroker/blitzkode

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 neuralbroker/blitzkode
# Run inference directly in the terminal:
./llama-cli -hf neuralbroker/blitzkode

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 neuralbroker/blitzkode
# Run inference directly in the terminal:
./build/bin/llama-cli -hf neuralbroker/blitzkode

Use Docker

docker model run hf.co/neuralbroker/blitzkode

LM Studio
Jan

vLLM

How to use neuralbroker/blitzkode with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/neuralbroker/blitzkode

Ollama
How to use neuralbroker/blitzkode with Ollama:
```
ollama run hf.co/neuralbroker/blitzkode
```

Unsloth Studio new

How to use neuralbroker/blitzkode 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 neuralbroker/blitzkode 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 neuralbroker/blitzkode to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for neuralbroker/blitzkode to start chatting

Pi new

How to use neuralbroker/blitzkode with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf neuralbroker/blitzkode

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": "neuralbroker/blitzkode"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use neuralbroker/blitzkode with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf neuralbroker/blitzkode

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 neuralbroker/blitzkode

Run Hermes

hermes

Docker Model Runner
How to use neuralbroker/blitzkode with Docker Model Runner:
```
docker model run hf.co/neuralbroker/blitzkode
```

Lemonade

How to use neuralbroker/blitzkode with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull neuralbroker/blitzkode

Run and chat with the model

lemonade run user.blitzkode-{{QUANT_TAG}}

List all available models

lemonade list

blitzkode / Dockerfile

neuralbroker

Update Dockerfile (v2.1 production)

ba3b381 verified 7 days ago

raw

history blame

2.48 kB

	# ─── Stage 1: Build frontend ─────────────────────────────────────────────────
	FROM node:20-alpine AS frontend-build

	WORKDIR /app/frontend

	# Install deps first for better layer caching
	COPY frontend/package*.json ./
	RUN npm ci --silent

	COPY frontend/ ./
	RUN npm run build

	# ─── Stage 2: Python runtime ─────────────────────────────────────────────────
	FROM python:3.11-slim AS runtime

	# Install curl (primary healthcheck tool) and keep the image lean
	RUN apt-get update \
	&& apt-get install -y --no-install-recommends curl \
	&& rm -rf /var/lib/apt/lists/*

	# Create non-root user early; home directory is created at /home/appuser
	RUN useradd --create-home appuser

	WORKDIR /app

	# Install Python dependencies as root (system-wide, before dropping privileges)
	COPY requirements.txt .
	RUN pip install --no-cache-dir -r requirements.txt

	# Copy application source
	COPY server.py .

	# Copy built frontend assets from stage 1
	COPY --from=frontend-build /app/frontend/dist ./frontend/dist

	# Create a zero-byte placeholder so Docker sees the expected mount path.
	# At runtime this file is replaced by the volume-mounted blitzkode.gguf.
	RUN touch /app/blitzkode.gguf \
	&& chown -R appuser:appuser /app

	# ─── Sensible runtime defaults ────────────────────────────────────────────────
	# All of these can be overridden at runtime via -e / docker-compose environment.
	ENV BLITZKODE_HOST=0.0.0.0 \
	BLITZKODE_PORT=7860 \
	BLITZKODE_MODEL_PATH=/app/blitzkode.gguf \
	BLITZKODE_FRONTEND_PATH=/app/frontend/dist/index.html \
	BLITZKODE_GPU_LAYERS=0 \
	BLITZKODE_THREADS=4 \
	BLITZKODE_PRELOAD_MODEL=true \
	BLITZKODE_N_CTX=2048 \
	BLITZKODE_BATCH=128

	EXPOSE 7860

	# Healthcheck: prefer curl (installed above); fall back to Python urllib so the
	# check still works if this image is rebuilt without the curl layer.
	HEALTHCHECK --interval=30s --timeout=10s --start-period=90s --retries=3 \
	CMD curl -sf http://localhost:7860/health \
	\|\| python -c "import urllib.request; urllib.request.urlopen('http://localhost:7860/health')" \
	\|\| exit 1

	# Drop to non-root for the actual process
	USER appuser

	CMD ["python", "server.py"]