Instructions to use tensorblock/ghost-7b-alpha-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/ghost-7b-alpha-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="tensorblock/ghost-7b-alpha-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/ghost-7b-alpha-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/ghost-7b-alpha-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 tensorblock/ghost-7b-alpha-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/ghost-7b-alpha-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/ghost-7b-alpha-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/ghost-7b-alpha-GGUF:Q2_K

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 tensorblock/ghost-7b-alpha-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/ghost-7b-alpha-GGUF:Q2_K

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 tensorblock/ghost-7b-alpha-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/ghost-7b-alpha-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/ghost-7b-alpha-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/ghost-7b-alpha-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/ghost-7b-alpha-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/ghost-7b-alpha-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/ghost-7b-alpha-GGUF:Q2_K

SGLang

How to use tensorblock/ghost-7b-alpha-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tensorblock/ghost-7b-alpha-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/ghost-7b-alpha-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tensorblock/ghost-7b-alpha-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensorblock/ghost-7b-alpha-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/ghost-7b-alpha-GGUF with Ollama:
```
ollama run hf.co/tensorblock/ghost-7b-alpha-GGUF:Q2_K
```
Unsloth Desktop
Docker Model Runner
How to use tensorblock/ghost-7b-alpha-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/ghost-7b-alpha-GGUF:Q2_K
```

Lemonade

How to use tensorblock/ghost-7b-alpha-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/ghost-7b-alpha-GGUF:Q2_K

Run and chat with the model

lemonade run user.ghost-7b-alpha-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

ghost-7b-alpha-GGUF / README.md

morriszms

Update README.md

ba2566f verified about 1 year ago

preview code

Raw

History Blame Contribute Delete

7.32 kB

metadata

language:
  - en
  - vi
license: other
license_name: ghost-7b
license_link: https://ghost-x.org/ghost-7b-license
library_name: transformers
tags:
  - ghost
  - tools
  - chat
  - TensorBlock
  - GGUF
pipeline_tag: text-generation
widget:
  - text: Why is the sky blue ?
    output:
      text: >-
        The sky appears blue because of a phenomenon called Rayleigh scattering.
        Sunlight is composed of all colors of the visible spectrum, but blue
        light has a shorter wavelength than other colors. When sunlight enters
        the Earth's atmosphere, it interacts with molecules of nitrogen and
        oxygen, which are the main components of our atmosphere. These molecules
        scatter the shorter wavelengths of light, such as blue and violet, more
        than the longer wavelengths, such as red and orange. As a result, when
        we look up at the sky, we see more blue light than other colors because
        the blue light has been scattered more by the molecules in the
        atmosphere.
base_model: ghost-x/ghost-7b-alpha

ghost-x/ghost-7b-alpha - GGUF

This repo contains GGUF format model files for ghost-x/ghost-7b-alpha.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Our projects

Forge

An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers	TensorBlock Studio

A comprehensive collection of Model Context Protocol (MCP) servers.	A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀	👀 See what we built 👀

## Prompt template

<|system|>
{system_prompt}</s>
<|user|>
{prompt}</s>
<|assistant|>

Model file specification

Filename	Quant type	File Size	Description
ghost-7b-alpha-Q2_K.gguf	Q2_K	2.532 GB	smallest, significant quality loss - not recommended for most purposes
ghost-7b-alpha-Q3_K_S.gguf	Q3_K_S	2.947 GB	very small, high quality loss
ghost-7b-alpha-Q3_K_M.gguf	Q3_K_M	3.277 GB	very small, high quality loss
ghost-7b-alpha-Q3_K_L.gguf	Q3_K_L	3.560 GB	small, substantial quality loss
ghost-7b-alpha-Q4_0.gguf	Q4_0	3.827 GB	legacy; small, very high quality loss - prefer using Q3_K_M
ghost-7b-alpha-Q4_K_S.gguf	Q4_K_S	3.856 GB	small, greater quality loss
ghost-7b-alpha-Q4_K_M.gguf	Q4_K_M	4.068 GB	medium, balanced quality - recommended
ghost-7b-alpha-Q5_0.gguf	Q5_0	4.654 GB	legacy; medium, balanced quality - prefer using Q4_K_M
ghost-7b-alpha-Q5_K_S.gguf	Q5_K_S	4.654 GB	large, low quality loss - recommended
ghost-7b-alpha-Q5_K_M.gguf	Q5_K_M	4.779 GB	large, very low quality loss - recommended
ghost-7b-alpha-Q6_K.gguf	Q6_K	5.534 GB	very large, extremely low quality loss
ghost-7b-alpha-Q8_0.gguf	Q8_0	7.167 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/ghost-7b-alpha-GGUF --include "ghost-7b-alpha-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/ghost-7b-alpha-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'