Instructions to use Umranz/Hydra-Turbo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Umranz/Hydra-Turbo-GGUF 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 Umranz/Hydra-Turbo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Umranz/Hydra-Turbo-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Umranz/Hydra-Turbo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Umranz/Hydra-Turbo-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 Umranz/Hydra-Turbo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Umranz/Hydra-Turbo-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 Umranz/Hydra-Turbo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Umranz/Hydra-Turbo-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Umranz/Hydra-Turbo-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Umranz/Hydra-Turbo-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Umranz/Hydra-Turbo-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": "Umranz/Hydra-Turbo-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Umranz/Hydra-Turbo-GGUF:Q4_K_M

Ollama
How to use Umranz/Hydra-Turbo-GGUF with Ollama:
```
ollama run hf.co/Umranz/Hydra-Turbo-GGUF:Q4_K_M
```

Unsloth Studio

How to use Umranz/Hydra-Turbo-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 Umranz/Hydra-Turbo-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 Umranz/Hydra-Turbo-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Umranz/Hydra-Turbo-GGUF to start chatting

Docker Model Runner
How to use Umranz/Hydra-Turbo-GGUF with Docker Model Runner:
```
docker model run hf.co/Umranz/Hydra-Turbo-GGUF:Q4_K_M
```

Lemonade

How to use Umranz/Hydra-Turbo-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Umranz/Hydra-Turbo-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hydra-Turbo-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Hydra-Turbo-GGUF

This repository contains official GGUF quantized weights for Umranz/Hydra-Turbo, a 9B parameter model based on the Qwen 3.5 architecture.

The GGUF files in this repository were converted using the latest build of llama.cpp with the --no-nextn conversion flag to ensure compatibility across local inference runtimes.

Model Overview

Base Model: Umranz/Hydra-Turbo
Architecture: Qwen 3.5 (Hybrid Linear Attention + Full Attention)
Parameters: 8.95B (9.0B label)
Context Length: 262,144 tokens
Vocabulary Size: 248,320
License: Apache 2.0

Available Files and Quantization Formats

File Name	Quantization Type	File Size	Description
`hydra-turbo-q4_0.gguf`	Q4_0	4.95 GB	Legacy 4-bit quantization. Fast execution, low VRAM footprint.
`hydra-turbo-q4_k_m.gguf`	Q4_K_M	5.24 GB	Recommended medium 4-bit quant. Balanced accuracy and memory usage.
`hydra-turbo-q5_k_m.gguf`	Q5_K_M	6.02 GB	High-precision 5-bit quant. Reduced perplexity loss with minimal speed overhead.
`hydra-turbo-q8_0.gguf`	Q8_0	8.87 GB	Near-lossless 8-bit quantization. Maximum output quality.

Technical Specifications

Layers: 32 Main Transformer Blocks (SSM / Gated Delta Net + Full Attention)
Embedding Length: 4096
Feed Forward Dimension: 12288
Attention Heads: 16 (Key-Value Heads: 4)
Rotary Position Embedding (RoPE): Frequency Base 10,000,000
Layer Norm Epsilon: 1e-6

Prompt Template

Hydra-Turbo uses the standard Qwen Chat Template:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Your query here<|im_end|>
<|im_start|>assistant

Downloads last month: 96

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for Umranz/Hydra-Turbo-GGUF

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