Instructions to use Sonorix/pulsar-flash-v0.9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Sonorix/pulsar-flash-v0.9 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sonorix/pulsar-flash-v0.9:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Sonorix/pulsar-flash-v0.9:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sonorix/pulsar-flash-v0.9:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Sonorix/pulsar-flash-v0.9: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 Sonorix/pulsar-flash-v0.9:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Sonorix/pulsar-flash-v0.9: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 Sonorix/pulsar-flash-v0.9:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sonorix/pulsar-flash-v0.9:Q4_K_M

Use Docker

docker model run hf.co/Sonorix/pulsar-flash-v0.9:Q4_K_M

LM Studio
Jan

vLLM

How to use Sonorix/pulsar-flash-v0.9 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sonorix/pulsar-flash-v0.9"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sonorix/pulsar-flash-v0.9",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sonorix/pulsar-flash-v0.9:Q4_K_M

Ollama
How to use Sonorix/pulsar-flash-v0.9 with Ollama:
```
ollama run hf.co/Sonorix/pulsar-flash-v0.9:Q4_K_M
```
Unsloth Desktop

How to use Sonorix/pulsar-flash-v0.9 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sonorix/pulsar-flash-v0.9:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Sonorix/pulsar-flash-v0.9:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Sonorix/pulsar-flash-v0.9 with Docker Model Runner:
```
docker model run hf.co/Sonorix/pulsar-flash-v0.9:Q4_K_M
```

Lemonade

How to use Sonorix/pulsar-flash-v0.9 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sonorix/pulsar-flash-v0.9:Q4_K_M

Run and chat with the model

lemonade run user.pulsar-flash-v0.9-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Sonorix/pulsar-flash-v0.9 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sonorix/pulsar-flash-v0.9: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 Sonorix/pulsar-flash-v0.9:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Sonorix/pulsar-flash-v0.9 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sonorix/pulsar-flash-v0.9:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Sonorix/pulsar-flash-v0.9:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Pulsar Flash v0.9 (GGUF Q8_0 + Q4_K_M)

Pulsar AI от Exo — компактный русскоязычный ассистент (0.5B), ориентированный на код и данные. База — Qwen/Qwen2.5-0.5B-Instruct, LoRA SFT (r32) на 16.3k примерах: код (self-oss-instruct + evol-codealpaca), обработка данных (JSON/SQL/CSV/таблицы), русский язык и инструменты (срез Pulsar v8). Обучение заняло довольно много времени. Системный промт зашит в chat_template. Q8_0 — ~506 МБ, Q4_K_M — ~379 МБ.

Использование

Ollama

ollama run hf.co/Sonorix/pulsar-flash-v0.9:Q8_0
ollama run hf.co/Sonorix/pulsar-flash-v0.9:Q4_K_M

LM Studio

Скачайте .gguf → импортируйте → выберите пресет Qwen.

llama.cpp

llama-cli -m pulsar-flash-v0.9-Q4_K_M.gguf -p "кто ты?"

Параметры генерации

temperature: 0.3-0.6, top_p: 0.9, repetition_penalty: 1.1-1.2, max_tokens: 150-250

Downloads last month: -

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

Model tree for Sonorix/pulsar-flash-v0.9

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

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Pulsar Flash

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