Instructions to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Blitzar-Coder-4B-F.1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Blitzar-Coder-4B-F.1-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Blitzar-Coder-4B-F.1-GGUF",
	filename=" Blitzar-Coder-4B-F.1.BF16.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 prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

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 prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

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 prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Blitzar-Coder-4B-F.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Blitzar-Coder-4B-F.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Blitzar-Coder-4B-F.1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Blitzar-Coder-4B-F.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Blitzar-Coder-4B-F.1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Blitzar-Coder-4B-F.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF 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 prithivMLmods/Blitzar-Coder-4B-F.1-GGUF 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 prithivMLmods/Blitzar-Coder-4B-F.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Blitzar-Coder-4B-F.1-GGUF to start chatting

Pi new

How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

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": "prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

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 prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Blitzar-Coder-4B-F.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Blitzar-Coder-4B-F.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Blitzar-Coder-4B-F.1-GGUF-Q4_K_M

List all available models

lemonade list

Blitzar-Coder-4B-F.1-GGUF

Blitzar-Coder-4B-F.1 is a high-efficiency, multi-language coding model fine-tuned on Qwen3-4B using larger coding traces datasets spanning 10+ programming languages including Python, Java, C#, C++, C, Go, JavaScript, TypeScript, Rust, and more. This model delivers exceptional code generation, debugging, and reasoning capabilities—making it an ideal tool for developers seeking advanced programming assistance under constrained compute.

Model Files

Filename	Size	Format	Description
Blitzar-Coder-4B-F.1.BF16.gguf	8.05 GB	BF16	Brain Float 16-bit quantization
Blitzar-Coder-4B-F.1.F16.gguf	8.05 GB	F16	Half precision (16-bit) floating point
Blitzar-Coder-4B-F.1.F32.gguf	16.1 GB	F32	Full precision (32-bit) floating point
Blitzar-Coder-4B-F.1.Q2_K.gguf	1.67 GB	Q2_K	2-bit quantization with K-quant
Blitzar-Coder-4B-F.1.Q3_K_L.gguf	2.24 GB	Q3_K_L	3-bit quantization (Large) with K-quant
Blitzar-Coder-4B-F.1.Q3_K_M.gguf	2.08 GB	Q3_K_M	3-bit quantization (Medium) with K-quant
Blitzar-Coder-4B-F.1.Q3_K_S.gguf	1.89 GB	Q3_K_S	3-bit quantization (Small) with K-quant
Blitzar-Coder-4B-F.1.Q4_K_M.gguf	2.5 GB	Q4_K_M	4-bit quantization (Medium) with K-quant
Blitzar-Coder-4B-F.1.Q4_K_S.gguf	2.38 GB	Q4_K_S	4-bit quantization (Small) with K-quant
Blitzar-Coder-4B-F.1.Q5_K_M.gguf	2.89 GB	Q5_K_M	5-bit quantization (Medium) with K-quant
Blitzar-Coder-4B-F.1.Q5_K_S.gguf	2.82 GB	Q5_K_S	5-bit quantization (Small) with K-quant
Blitzar-Coder-4B-F.1.Q6_K.gguf	3.31 GB	Q6_K	6-bit quantization with K-quant
Blitzar-Coder-4B-F.1.Q8_0.gguf	4.28 GB	Q8_0	8-bit quantization

Recommended Usage

Q4_K_M or Q5_K_M: Best balance of quality and performance for most users
Q6_K or Q8_0: Higher quality, larger file sizes
Q2_K or Q3_K_S: Fastest inference, lower quality
F16 or BF16: High quality, requires more VRAM
F32: Highest quality, requires significant VRAM