Instructions to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("zero-shot-classification", model="sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF", device_map="auto")

llama-cpp-python

How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF",
	filename="Qwen2.5-14B-CIC-ACLARC-F16.gguf",
)

llm.create_chat_completion(
	messages = "\"Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-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 sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

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 sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

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 sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

Use Docker

docker model run hf.co/sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

LM Studio
Jan
Ollama
How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with Ollama:
```
ollama run hf.co/sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16
```

Unsloth Studio

How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-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 sknow-lab/Qwen2.5-14B-CIC-ACLARC-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 sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF to start chatting

How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

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": "sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

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 sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

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 "sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16" \
  --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 sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with Docker Model Runner:
```
docker model run hf.co/sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16
```

Lemonade

How to use sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sknow-lab/Qwen2.5-14B-CIC-ACLARC-GGUF:F16

Run and chat with the model

lemonade run user.Qwen2.5-14B-CIC-ACLARC-GGUF-F16

List all available models

lemonade list

Llamacpp imatrix Quantizations of Qwen2.5-14B-CIC-ACLARC

Using llama.cpp for quantization.

Original model: https://huggingface.co/sknow-lab/Qwen2.5-14B-CIC-ACLARC

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Citation

@misc{koloveas2025llmspredictcitationintent,
      title={Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs}, 
      author={Paris Koloveas and Serafeim Chatzopoulos and Thanasis Vergoulis and Christos Tryfonopoulos},
      year={2025},
      eprint={2502.14561},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.14561}, 
}