Text Generation
llama-cpp-python
GGUF
English
code-generation
coding-assistant
llama.cpp
qwen2.5
python
javascript
fine-tuned
conversational
Instructions to use neuralbroker/blitzkode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- 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?" } ] ) - 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 β Docker Compose | |
| # | |
| # Quick start (CPU-only): | |
| # docker compose up --build | |
| # | |
| # With GPU (see blitzkode-gpu service below): | |
| # docker compose --profile gpu up --build | |
| # | |
| # Override GPU layers at runtime without editing this file: | |
| # BLITZKODE_GPU_LAYERS=35 docker compose up | |
| services: | |
| # βββ CPU service (default) ββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| blitzkode: | |
| build: . | |
| image: blitzkode:latest | |
| ports: | |
| - "7860:7860" | |
| volumes: | |
| # The GGUF model is NOT baked into the image. | |
| # Place blitzkode.gguf next to this file and it will be mounted read-only. | |
| - ./blitzkode.gguf:/app/blitzkode.gguf:ro | |
| environment: | |
| BLITZKODE_MODEL_PATH: /app/blitzkode.gguf | |
| BLITZKODE_HOST: "0.0.0.0" | |
| BLITZKODE_PORT: "7860" | |
| # Set BLITZKODE_GPU_LAYERS in your shell or a .env file to override. | |
| # 0 = CPU-only (default), -1 = all layers on GPU. | |
| BLITZKODE_GPU_LAYERS: "${BLITZKODE_GPU_LAYERS:-0}" | |
| BLITZKODE_N_CTX: "2048" | |
| BLITZKODE_THREADS: "4" | |
| BLITZKODE_BATCH: "128" | |
| BLITZKODE_PRELOAD_MODEL: "true" | |
| restart: unless-stopped | |
| healthcheck: | |
| test: ["CMD", "curl", "-sf", "http://localhost:7860/health"] | |
| interval: 30s | |
| timeout: 10s | |
| start_period: 90s | |
| retries: 3 | |
| # βββ GPU service (commented out β requires nvidia-container-toolkit) βββββββββ | |
| # | |
| # Prerequisites on the host: | |
| # 1. NVIDIA driver installed | |
| # 2. nvidia-container-toolkit installed (https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) | |
| # 3. Docker daemon configured with nvidia runtime (nvidia-ctk runtime configure --runtime=docker) | |
| # | |
| # To start: docker compose --profile gpu up --build | |
| # | |
| # blitzkode-gpu: | |
| # build: . | |
| # image: blitzkode:latest | |
| # profiles: [gpu] | |
| # ports: | |
| # - "7860:7860" | |
| # volumes: | |
| # - ./blitzkode.gguf:/app/blitzkode.gguf:ro | |
| # environment: | |
| # BLITZKODE_MODEL_PATH: /app/blitzkode.gguf | |
| # BLITZKODE_HOST: "0.0.0.0" | |
| # BLITZKODE_PORT: "7860" | |
| # # Tune to your GPU's layer count (run `./scripts/healthcheck.sh` after start) | |
| # BLITZKODE_GPU_LAYERS: "35" | |
| # BLITZKODE_N_CTX: "4096" | |
| # BLITZKODE_THREADS: "4" | |
| # BLITZKODE_BATCH: "512" | |
| # BLITZKODE_PRELOAD_MODEL: "true" | |
| # deploy: | |
| # resources: | |
| # reservations: | |
| # devices: | |
| # - driver: nvidia | |
| # count: 1 | |
| # capabilities: [gpu] | |
| # runtime: nvidia | |
| # restart: unless-stopped | |
| # healthcheck: | |
| # test: ["CMD", "curl", "-sf", "http://localhost:7860/health"] | |
| # interval: 30s | |
| # timeout: 10s | |
| # start_period: 90s | |
| # retries: 3 | |