Instructions to use Daemonrat/Qwen2.5-32B-AGI_Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Daemonrat/Qwen2.5-32B-AGI_Q4_K_M with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Daemonrat/Qwen2.5-32B-AGI_Q4_K_M", dtype="auto")

llama-cpp-python

How to use Daemonrat/Qwen2.5-32B-AGI_Q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Daemonrat/Qwen2.5-32B-AGI_Q4_K_M",
	filename="Qwen2.5-32B-AGI_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 Daemonrat/Qwen2.5-32B-AGI_Q4_K_M 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 Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Daemonrat/Qwen2.5-32B-AGI_Q4_K_M: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 Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Daemonrat/Qwen2.5-32B-AGI_Q4_K_M: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 Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M

LM Studio
Jan
Ollama
How to use Daemonrat/Qwen2.5-32B-AGI_Q4_K_M with Ollama:
```
ollama run hf.co/Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use Daemonrat/Qwen2.5-32B-AGI_Q4_K_M 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 Daemonrat/Qwen2.5-32B-AGI_Q4_K_M 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 Daemonrat/Qwen2.5-32B-AGI_Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Daemonrat/Qwen2.5-32B-AGI_Q4_K_M to start chatting

Atomic Chat new
Docker Model Runner
How to use Daemonrat/Qwen2.5-32B-AGI_Q4_K_M with Docker Model Runner:
```
docker model run hf.co/Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M
```

Lemonade

How to use Daemonrat/Qwen2.5-32B-AGI_Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Daemonrat/Qwen2.5-32B-AGI_Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-32B-AGI_Q4_K_M-Q4_K_M

List all available models

lemonade list

GGUF Q-4_K_M Quantization of AiCloser/Qwen2.5-32B-AGI. Fit for 24GB card.

Modelcard included so that you can send it straight to ollama with ollama create Qwen2.5-32B-AGI_Q4_K_M -f Modelfile then ollama run Qwen2.5-32B-AGI_Q4_K_M

From my testing, it's not uncensored, but it's basically a lvl 1 guardrail and you just need to use a generic jailbreak to step around it.

AGI means Aspirational Grand Illusion

First Qwen2.5 32B Finetune, to fix its Hypercensuritis

Hyper means high, and censura means censor, the suffix "-itis" is used to denote inflammation of a particular part or organ of the body.

Downloads last month: 7

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

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

Model tree for Daemonrat/Qwen2.5-32B-AGI_Q4_K_M

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Quantized

(147)

this model

Daemonrat
/

Qwen2.5-32B-AGI_Q4_K_M

Model tree for Daemonrat/Qwen2.5-32B-AGI_Q4_K_M

Datasets used to train Daemonrat/Qwen2.5-32B-AGI_Q4_K_M