---
base_model: unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---

# Reverse Browser 90B Model

- **License:** Apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit

This is a vector-to-ui model. It was fine-tuned on the HTML+CSS code of [hundreds of thousands of public web pages](https://huggingface.co/datasets/tcz/rb-large) and their SVG counterparts.

The goal of the model is to aid rapid prototyping and web development.

The model was trained on mobile resolution (393×852) SVGs only. Its test set accuracy was 0.3012 [LPIPS](https://arxiv.org/abs/1801.03924) (AlexNet). This suggests that it is not ready for industrial use in a real workflow, but it may serve research purposes.

## Usage

```python
!pip install vllm

from vllm import LLM, SamplingParams
llm = LLM(
    "tcz/rb-llama-90b",  
    tensor_parallel_size=4,   # The was tested on four H100s (96GB) GPUs
    dtype="bfloat16",
    gpu_memory_utilization=0.9, 
    enforce_eager=True,         
    max_model_len=12_000,
    max_num_seqs=64,
)

data_prompt = """Your job is to take an SVG file of a web design and convert it into a pixel-perfect HTML and CSS markup and stylesheet.

### Input:
{}

### Response:
{}"""

max_tokens = 12_000

# Experiment with different temperature and top-p settings
sampling_params = SamplingParams(
    temperature   = 0.0,
    top_p         = 1.0,
    top_k         = 1,
    n             = 1,
    max_tokens    = max_tokens,
    seed          = 0
)

prompt = data_prompt.format(
    YOUR_SVG_CONTENT,
    "",
)

output = llm.generate([prompt], sampling_params)
print(output[0].outputs[0].text)
```