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
File size: 2,843 Bytes
4fe8118 | 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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | # 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
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