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

neuralbroker commited on 10 days ago

Commit

f9b1e65

verified ·

1 Parent(s): 11f64d8

Update clean backend-only project docs and eval

Browse files

Files changed (1) hide show

docs/evaluation_results.json +54 -0

docs/evaluation_results.json ADDED Viewed

	@@ -0,0 +1,54 @@

+{
+  "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"
+    }
+  ]
+}