Instructions to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier",
	filename="qwen2.5-14b-instruct-reddit-rpg-classifier.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "\"I like you. I love you\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0
# Run inference directly in the terminal:
llama-cli -hf eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0
# Run inference directly in the terminal:
llama-cli -hf eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

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 eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

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 eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

Use Docker

docker model run hf.co/eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

LM Studio
Jan
Ollama
How to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier with Ollama:
```
ollama run hf.co/eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0
```

Unsloth Studio

How to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier 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 eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier 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 eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier to start chatting

How to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

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": "eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

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 eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier with Docker Model Runner:
```
docker model run hf.co/eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0
```

Lemonade

How to use eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier:Q8_0

Run and chat with the model

lemonade run user.qwen2.5-14b-instruct-reddit-rpg-classifier-Q8_0

List all available models

lemonade list

qwen2.5-14b-instruct-reddit-rpg-classifier (GGUF)

Binary classifier for tabletop RPG subreddit posts:

Rules Question
Other

Files

qwen2.5-14b-instruct-reddit-rpg-classifier.Q8_0.gguf — quantized GGUF (Q8_0)
Modelfile.ollama — Ollama Modelfile for local use

Ollama usage

ollama create qwen2.5-14b-instruct-reddit-rpg-classifier -f Modelfile.ollama
ollama run qwen2.5-14b-instruct-reddit-rpg-classifier "How much damage does a flamethrower do?"

Expected output: exactly one label: Rules Question or Other.

Prompt format

The model was trained to respond to prompts like:

You are a classifier for tabletop RPG subreddit posts. Classify each post into one of two categories: `Rules Question`, `Other`. ONLY output the label, and NOTHING else.

Post:
<POST TEXT>

Label:

Downloads last month: 11

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

8-bit

Model tree for eriksalt/qwen2.5-14b-instruct-reddit-rpg-classifier

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Quantized

(138)

this model