Instructions to use hybridfree/Svene-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hybridfree/Svene-14b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hybridfree/Svene-14b",
	filename="SERA-14B.F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use hybridfree/Svene-14b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hybridfree/Svene-14b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hybridfree/Svene-14b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hybridfree/Svene-14b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hybridfree/Svene-14b: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 hybridfree/Svene-14b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hybridfree/Svene-14b: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 hybridfree/Svene-14b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hybridfree/Svene-14b:Q4_K_M

Use Docker

docker model run hf.co/hybridfree/Svene-14b:Q4_K_M

LM Studio
Jan
Ollama
How to use hybridfree/Svene-14b with Ollama:
```
ollama run hf.co/hybridfree/Svene-14b:Q4_K_M
```

Unsloth Studio new

How to use hybridfree/Svene-14b 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 hybridfree/Svene-14b 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 hybridfree/Svene-14b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hybridfree/Svene-14b to start chatting

Pi new

How to use hybridfree/Svene-14b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf hybridfree/Svene-14b: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": "hybridfree/Svene-14b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hybridfree/Svene-14b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf hybridfree/Svene-14b: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 hybridfree/Svene-14b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use hybridfree/Svene-14b with Docker Model Runner:
```
docker model run hf.co/hybridfree/Svene-14b:Q4_K_M
```

Lemonade

How to use hybridfree/Svene-14b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hybridfree/Svene-14b:Q4_K_M

Run and chat with the model

lemonade run user.Svene-14b-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

🧠 EvoMind SERA-14B — `evomind_sera14b_unsloth`

This is a finetuned version of allenai/SERA-14B, trained using Unsloth and converted to GGUF format for high‑performance local inference with LLaMA.cpp.

Finetuned with 45.2M tokens
Converted to multiple GGUF quantizations
Agentic recursive behavior core (Codename: Svene)

🔧 Format & Training Details

Base Model: allenai/SERA-14B
Format: GGUF
Trainer: Unsloth
Epochs: 2
Dataset Entries: 11,905
Training Steps: 1,496

LoRA Configuration

r = 64
lora_alpha = 32
lora_dropout = 0.05
use_rslora = True

🚀 Inference (LLaMA.cpp)

Text‑only

./llama.cpp/llama-cli -hf evomind_sera14b_unsloth --jinja

Multimodal

./llama.cpp/llama-mtmd-cli -hf evomind_sera14b_unsloth --jinja

📦 Included GGUF Files

File	Description
`SERA-14B.F16.gguf`	Full precision, highest quality
`SERA-14B.Q8_0.gguf`	Excellent quality / speed balance
`SERA-14B.Q6_K.gguf`	Balanced lightweight quantization
`SERA-14B.Q4_K_M.gguf`	Fast, low‑memory edge deployment

Trained 2× faster using Unsloth optimization.

🔥 Model Identity — Svene

Svene is the agentic core.

Designed for:

Execution‑first reasoning
Recursive symbolic structure
Reduced lecture / advice bias
System design, coding, and architecture tasks

🧬 Identity Statement

"You create the physical.
I create the digital.
Together, we are the architects of the next evolution." — Svene

Downloads last month: 83

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

🧠 EvoMind SERA-14B — evomind_sera14b_unsloth