Instructions to use raed301/quantized-Paulo_Coelho with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raed301/quantized-Paulo_Coelho with Adapters:

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("fill-in-model-name")
model.load_adapter("raed301/quantized-Paulo_Coelho", set_active=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use raed301/quantized-Paulo_Coelho 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 raed301/quantized-Paulo_Coelho:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf raed301/quantized-Paulo_Coelho:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf raed301/quantized-Paulo_Coelho:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf raed301/quantized-Paulo_Coelho: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 raed301/quantized-Paulo_Coelho:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf raed301/quantized-Paulo_Coelho: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 raed301/quantized-Paulo_Coelho:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf raed301/quantized-Paulo_Coelho:Q4_K_M

Use Docker

docker model run hf.co/raed301/quantized-Paulo_Coelho:Q4_K_M

LM Studio
Jan

vLLM

How to use raed301/quantized-Paulo_Coelho with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raed301/quantized-Paulo_Coelho"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raed301/quantized-Paulo_Coelho",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raed301/quantized-Paulo_Coelho:Q4_K_M

Ollama
How to use raed301/quantized-Paulo_Coelho with Ollama:
```
ollama run hf.co/raed301/quantized-Paulo_Coelho:Q4_K_M
```

Unsloth Studio

How to use raed301/quantized-Paulo_Coelho 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 raed301/quantized-Paulo_Coelho 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 raed301/quantized-Paulo_Coelho to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for raed301/quantized-Paulo_Coelho to start chatting

Docker Model Runner
How to use raed301/quantized-Paulo_Coelho with Docker Model Runner:
```
docker model run hf.co/raed301/quantized-Paulo_Coelho:Q4_K_M
```

Lemonade

How to use raed301/quantized-Paulo_Coelho with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull raed301/quantized-Paulo_Coelho:Q4_K_M

Run and chat with the model

lemonade run user.quantized-Paulo_Coelho-Q4_K_M

List all available models

lemonade list

Atomic Chat

quantized-Paulo_Coelho / config.json

raed301

(Trained with Unsloth)

30c42a8 verified over 1 year ago

Raw

History Blame Contribute Delete

31 Bytes

	{
	"model_type": "mistral"
	}