Instructions to use QyrouNnet-AI/reasoning_summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QyrouNnet-AI/reasoning_summarizer with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QyrouNnet-AI/reasoning_summarizer",
	filename="reasoning_summarizer-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QyrouNnet-AI/reasoning_summarizer 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 QyrouNnet-AI/reasoning_summarizer:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QyrouNnet-AI/reasoning_summarizer:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/QyrouNnet-AI/reasoning_summarizer:Q4_K_M

LM Studio
Jan
Ollama
How to use QyrouNnet-AI/reasoning_summarizer with Ollama:
```
ollama run hf.co/QyrouNnet-AI/reasoning_summarizer:Q4_K_M
```

Unsloth Studio

How to use QyrouNnet-AI/reasoning_summarizer 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 QyrouNnet-AI/reasoning_summarizer 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 QyrouNnet-AI/reasoning_summarizer to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QyrouNnet-AI/reasoning_summarizer to start chatting

How to use QyrouNnet-AI/reasoning_summarizer with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QyrouNnet-AI/reasoning_summarizer: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": "QyrouNnet-AI/reasoning_summarizer:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QyrouNnet-AI/reasoning_summarizer with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QyrouNnet-AI/reasoning_summarizer: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 QyrouNnet-AI/reasoning_summarizer:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use QyrouNnet-AI/reasoning_summarizer with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QyrouNnet-AI/reasoning_summarizer: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 "QyrouNnet-AI/reasoning_summarizer: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"

Docker Model Runner
How to use QyrouNnet-AI/reasoning_summarizer with Docker Model Runner:
```
docker model run hf.co/QyrouNnet-AI/reasoning_summarizer:Q4_K_M
```

Lemonade

How to use QyrouNnet-AI/reasoning_summarizer with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QyrouNnet-AI/reasoning_summarizer:Q4_K_M

Run and chat with the model

lemonade run user.reasoning_summarizer-Q4_K_M

List all available models

lemonade list

reasoning_summarizer / README.md

QyrouNnet-AI

Update README.md

060f788 verified 2 days ago

preview code

Raw

History Blame Contribute Delete

1.01 kB

	---
	license: apache-2.0
	language:
	- en
	base_model:
	- Qwen/Qwen3.5-2B-Base
	---
	# reasoning_summarizer

	Fine-tuned text-only Qwen3.5 2B Base model for converting reasoning-chain text into JSON metadata.

	## Format

	Input is raw reasoning text. The model is trained to output only JSON:

	```json
	{"title":"...","sub_title":"...","summary":"...","cur_task":"..."}
	```

	No system prompt was used in training.

	## Training

	- Base model: `Qwen/Qwen3.5-2B-Base`
	- Adapter source: `runs/qwen3_5_2b_reasoning_json_lora/best_adapter`
	- LoRA rank: 32
	- LoRA alpha: 64
	- Training context length: 1024
	- Text-only export: merged with `AutoModelForCausalLM`; no mmproj export

	## Files

	- `reasoning_summarizer_hf/`: merged Hugging Face safetensors checkpoint
	- `reasoning_summarizer-f16.gguf`: F16 GGUF
	- `reasoning_summarizer-Q4_K_M.gguf`: Q4 quantized GGUF
	- `reasoning_summarizer-Q5_K_M.gguf`: Q5 quantized GGUF
	- `reasoning_summarizer-Q6_K.gguf`: Q6 quantized GGUF
	- `reasoning_summarizer-Q8_0.gguf`: Q8 quantized GGUF