Instructions to use McG-221/GhostFace-24B-v1-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use McG-221/GhostFace-24B-v1-mlx-8Bit with Transformers:

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

pipe = pipeline("text-generation", model="McG-221/GhostFace-24B-v1-mlx-8Bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("McG-221/GhostFace-24B-v1-mlx-8Bit")
model = AutoModelForCausalLM.from_pretrained("McG-221/GhostFace-24B-v1-mlx-8Bit")

MLX

How to use McG-221/GhostFace-24B-v1-mlx-8Bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("McG-221/GhostFace-24B-v1-mlx-8Bit")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use McG-221/GhostFace-24B-v1-mlx-8Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "McG-221/GhostFace-24B-v1-mlx-8Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McG-221/GhostFace-24B-v1-mlx-8Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/McG-221/GhostFace-24B-v1-mlx-8Bit

SGLang

How to use McG-221/GhostFace-24B-v1-mlx-8Bit 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 "McG-221/GhostFace-24B-v1-mlx-8Bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McG-221/GhostFace-24B-v1-mlx-8Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "McG-221/GhostFace-24B-v1-mlx-8Bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McG-221/GhostFace-24B-v1-mlx-8Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use McG-221/GhostFace-24B-v1-mlx-8Bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "McG-221/GhostFace-24B-v1-mlx-8Bit" --prompt "Once upon a time"

Docker Model Runner
How to use McG-221/GhostFace-24B-v1-mlx-8Bit with Docker Model Runner:
```
docker model run hf.co/McG-221/GhostFace-24B-v1-mlx-8Bit
```

GhostFace-24B-v1-mlx-8Bit / README.md

McG-221

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4b6a79d verified 3 months ago

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metadata

license: apache-2.0
base_model: Naphula/GhostFace-24B-v1
datasets:
  - OccultAI/illuminati_imatrix_v1
language:
  - en
library_name: transformers
tags:
  - della
  - sce
  - model_stock
  - scream
  - merge
  - mergekit
  - mlx
  - mlx-my-repo
widget:
  - text: GhostFace 24B v1
    output:
      url: >-
        https://cdn-uploads.huggingface.co/production/uploads/68e840caa318194c44ec2a04/54Fa6PFEckcln-7quE4pQ.png

McG-221/GhostFace-24B-v1-mlx-8Bit

The Model McG-221/GhostFace-24B-v1-mlx-8Bit was converted to MLX format from Naphula/GhostFace-24B-v1 using mlx-lm version 0.29.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("McG-221/GhostFace-24B-v1-mlx-8Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)