Image-Text-to-Text
MLX
Safetensors
English
qwen2_5_vl
multimodal
computer-use
cua
web-agent
agent
vision-language
conversational
8-bit precision
Instructions to use mlx-community/Fara-7B-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Fara-7B-8bit 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("mlx-community/Fara-7B-8bit") config = load_config("mlx-community/Fara-7B-8bit") # 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 Settings
- LM Studio
Add MLX 8-bit conversion of microsoft/Fara-7B with vision preserved and weight-level fidelity vs bf16
a39db61 verified | { | |
| "bos_token_id": 151643, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 151645, | |
| 151643 | |
| ], | |
| "pad_token_id": 151643, | |
| "repetition_penalty": 1.05, | |
| "temperature": 1e-06, | |
| "transformers_version": "4.52.4" | |
| } | |