Image-Text-to-Text
MLX
Safetensors
English
gemma3
axolotl
gemma
roleplay
mlx-my-repo
conversational
6-bit
Instructions to use dong-99/CardProjector-27B-v4-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dong-99/CardProjector-27B-v4-mlx-6Bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("dong-99/CardProjector-27B-v4-mlx-6Bit") config = load_config("dong-99/CardProjector-27B-v4-mlx-6Bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
dong-99/CardProjector-27B-v4-mlx-6Bit
The Model dong-99/CardProjector-27B-v4-mlx-6Bit was converted to MLX format from SlerpE/CardProjector-27B-v4 using mlx-lm version 0.26.4.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("dong-99/CardProjector-27B-v4-mlx-6Bit")
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)
- Downloads last month
- 7
Model size
27B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
Log In to add your hardware
6-bit
Model tree for dong-99/CardProjector-27B-v4-mlx-6Bit
Base model
google/gemma-3-27b-pt Finetuned
google/gemma-3-27b-it Finetuned
SlerpE/CardProjector-27B-v4