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

Libraries

How to use mberkanbicer/DeepInnovator-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mberkanbicer/DeepInnovator-GGUF",
	filename="DeepInnovator-Q2_K.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 mberkanbicer/DeepInnovator-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mberkanbicer/DeepInnovator-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mberkanbicer/DeepInnovator-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 mberkanbicer/DeepInnovator-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mberkanbicer/DeepInnovator-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 mberkanbicer/DeepInnovator-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mberkanbicer/DeepInnovator-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 mberkanbicer/DeepInnovator-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mberkanbicer/DeepInnovator-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mberkanbicer/DeepInnovator-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use mberkanbicer/DeepInnovator-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mberkanbicer/DeepInnovator-GGUF:Q4_K_M

Ollama
How to use mberkanbicer/DeepInnovator-GGUF with Ollama:
```
ollama run hf.co/mberkanbicer/DeepInnovator-GGUF:Q4_K_M
```

Unsloth Studio new

How to use mberkanbicer/DeepInnovator-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 mberkanbicer/DeepInnovator-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 mberkanbicer/DeepInnovator-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use mberkanbicer/DeepInnovator-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mberkanbicer/DeepInnovator-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": "mberkanbicer/DeepInnovator-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mberkanbicer/DeepInnovator-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 mberkanbicer/DeepInnovator-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 mberkanbicer/DeepInnovator-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use mberkanbicer/DeepInnovator-GGUF with Docker Model Runner:
```
docker model run hf.co/mberkanbicer/DeepInnovator-GGUF:Q4_K_M
```

Lemonade

How to use mberkanbicer/DeepInnovator-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mberkanbicer/DeepInnovator-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepInnovator-GGUF-Q4_K_M

List all available models

lemonade list

DeepInnovator GGUF

This repository provides GGUF quantized variants of T1anyu/DeepInnovator. The original Hugging Face model was converted to GGUF and quantized using llama.cpp.

Original model

Original model: T1anyu/DeepInnovator
Model size: 15B parameters
Original tensor type: BF16
License: Apache-2.0

Description

DeepInnovator is a large language model designed for generating novel research ideas. According to the original model card, it is trained to produce innovative and significant research ideas, with a training methodology centered on structured scientific knowledge extraction and iterative “Next Idea Prediction.” It is published as a 15B-parameter model under the Apache-2.0 license. :contentReference[oaicite:0]{index=0}

Files

DeepInnovator-Q2_K.gguf
DeepInnovator-Q3_K_S.gguf
DeepInnovator-Q3_K_M.gguf
DeepInnovator-Q3_K_L.gguf
DeepInnovator-Q4_K.gguf
DeepInnovator-Q4_K.gguf
DeepInnovator-Q4_K_S.gguf
DeepInnovator-Q6_K.gguf

Example: llama.cpp

./llama-cli -m ./DeepInnovator-Q4_K_M.gguf -c 1024 -ngl 20

Example: Ollama

Create a Modelfile:

FROM ./DeepInnovator-Q4_K_M.gguf
PARAMETER num_ctx 1024
PARAMETER temperature 0.7

Then run:

ollama create deepinnovator-q4 -f Modelfile
ollama run deepinnovator-q4

Example: Transformers

For the original non-GGUF model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "T1anyu/DeepInnovator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)

The original model card also provides example prompts and a vLLM usage example. (Hugging Face)

Notes

These files are quantized GGUF derivatives of the original model. Please refer to the upstream repository for the official model card, usage details, paper, and future updates. The upstream page lists the model as a Qwen2.5-14B-family fine-tuned model and links the paper DeepInnovator: Triggering the Innovative Capabilities of LLMs (arXiv:2602.18920). (Hugging Face)

Upstream links

Original Hugging Face model: https://huggingface.co/T1anyu/DeepInnovator
GitHub repository: https://github.com/HKUDS/DeepInnovator

Citation

If you use this model, please cite the original work:

@article{fan2026deepinnovator,
  title={DeepInnovator: Triggering the Innovative Capabilities of LLMs},
  author={Fan, Tianyu and Zhang, Fengji and Zheng, Yuxiang and Chen, Bei and Niu, Xinyao and Huang, Chengen and Lin, Junyang and Huang, Chao},
  journal={arXiv preprint arXiv:2602.18920},
  year={2026}
}

Downloads last month: 39

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

Model tree for mberkanbicer/DeepInnovator-GGUF

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Finetuned

T1anyu/DeepInnovator

Quantized

(1)

this model

Paper for mberkanbicer/DeepInnovator-GGUF

DeepInnovator: Triggering the Innovative Capabilities of LLMs

Paper • 2602.18920 • Published Feb 21 • 1