Instructions to use pixelmelt/Incelgpt-24B_Q4_K_M_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pixelmelt/Incelgpt-24B_Q4_K_M_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pixelmelt/Incelgpt-24B_Q4_K_M_GGUF",
	filename="Incelgpt-24B_q4_k_m.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pixelmelt/Incelgpt-24B_Q4_K_M_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pixelmelt/Incelgpt-24B_Q4_K_M_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 pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pixelmelt/Incelgpt-24B_Q4_K_M_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 pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pixelmelt/Incelgpt-24B_Q4_K_M_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 pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M

Use Docker

docker model run hf.co/pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use pixelmelt/Incelgpt-24B_Q4_K_M_GGUF with Ollama:
```
ollama run hf.co/pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M
```

Unsloth Studio

How to use pixelmelt/Incelgpt-24B_Q4_K_M_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 pixelmelt/Incelgpt-24B_Q4_K_M_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 pixelmelt/Incelgpt-24B_Q4_K_M_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pixelmelt/Incelgpt-24B_Q4_K_M_GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use pixelmelt/Incelgpt-24B_Q4_K_M_GGUF with Docker Model Runner:
```
docker model run hf.co/pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M
```

Lemonade

How to use pixelmelt/Incelgpt-24B_Q4_K_M_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pixelmelt/Incelgpt-24B_Q4_K_M_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Incelgpt-24B_Q4_K_M_GGUF-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

This model is superceeded by V1.1 you will get much better results with that one

Incelgpt

Heard of GPT4-Chan? Same deal, has been known to act like an anxty andrew tate follower. Dont use instruct, use any mistral 24b sampler settings, dont name the bot "assistant"

As always, the author is not responsible for what you do with this model.

Downloads last month: 7

GGUF

Model size

22B params

Architecture

llama

Hardware compatibility

4-bit

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