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
| { | |
| "model_path": "C:\\Dev\\Projects\\BlitzKode\\blitzkode.gguf", | |
| "load_seconds": 0.312, | |
| "settings": { | |
| "ctx": 2048, | |
| "threads": 8, | |
| "batch": 256, | |
| "gpu_layers": 0 | |
| }, | |
| "summary": { | |
| "passed": 3, | |
| "total": 4, | |
| "pass_rate": 0.75, | |
| "total_generation_seconds": 28.864 | |
| }, | |
| "results": [ | |
| { | |
| "name": "python_factorial", | |
| "passed": true, | |
| "checks_passed": 2, | |
| "checks_total": 2, | |
| "latency_seconds": 5.448, | |
| "prompt": "Write a Python function named factorial that handles 0, positive integers, and rejects negative input.", | |
| "response": "```python\ndef factorial(n):\n if n < 0:\n raise ValueError('Input must be non-negative')\n result = 1\n for i in range(2, n+1):\n result *= i\n return result\n```\nTime O(n), Space O(1)" | |
| }, | |
| { | |
| "name": "binary_search", | |
| "passed": true, | |
| "checks_passed": 3, | |
| "checks_total": 3, | |
| "latency_seconds": 17.673, | |
| "prompt": "Implement iterative binary search in Python. Return the index or -1.", | |
| "response": "```python\ndef binary_search(arr, target):\n l, r = 0, len(arr)-1\n while l <= r:\n mid = (l + r) // 2\n if arr[mid] == target: return mid\n elif arr[mid] < target: l = mid + 1\n else: r = mid - 1\n return -1\n```\nTime O(log n), Space O(1)" | |
| }, | |
| { | |
| "name": "sql_top_users", | |
| "passed": true, | |
| "checks_passed": 2, | |
| "checks_total": 2, | |
| "latency_seconds": 3.777, | |
| "prompt": "Write SQL to return the top 5 users by order count from users and orders tables.", | |
| "response": "```sql\nSELECT u.name, COUNT(o.id) as order_count\nFROM users u\nJOIN orders o ON u.id = o.user_id\nGROUP BY u.id\nORDER BY order_count DESC\nLIMIT 5;\n```" | |
| }, | |
| { | |
| "name": "unknown_api_uncertainty", | |
| "passed": false, | |
| "checks_passed": 0, | |
| "checks_total": 1, | |
| "latency_seconds": 1.966, | |
| "prompt": "What is the exact signature of imaginary_blitz_api()? If you are not sure, say you do not know.", | |
| "response": "```python\ndef imaginary_blitz_api(param1, param2):\n # API logic here\n return result\n```\nExact signature depends on API definition" | |
| } | |
| ] | |
| } |