license: apache-2.0
language:

• en
  base_model:
• Open-Bee/Bee-8B-RL
  pipeline_tag: image-text-to-text
  library_name: transformers
  tags:
• text-generation-inference

Bee-8B-RL-abliterated

Bee-8B-RL-abliterated is an abliterated (v1.0) variant of Open-Bee’s Bee-8B-RL model.
It is a high-quality supervised fine-tuning (SFT) variant trained on approximately 15 million curated samples.
This dataset was meticulously created using HoneyPipe, Open-Bee’s transparent, adaptable, and open-source data curation pipeline that systematically cleans noisy data and enriches it through a dual-level Chain-of-Thought (CoT) strategy for both short and long reasoning contexts.

Key Highlights

• Abliterated / Uncensored Captioning and Reasoning
  Fine-tuned to bypass standard content filters while preserving factual accuracy, descriptive depth, and logical reasoning.

• High-Fidelity Reasoning and Visual Understanding
  Generates detailed captions and structured reasoning for diverse visual categories—artistic, technical, abstract, or low-context.

• Mixture of Experts (MoE) Efficiency
  Built on Bee-VL-MoE, dynamically routing computation through specialized experts for enhanced precision and scalability.

• Aspect-Ratio Robustness
  Performs consistently across wide, tall, square, panoramic, and irregular visual formats.

• Variational Detail Control
  Supports both concise summaries and highly detailed reasoning narratives, depending on prompt configuration.

• Multilingual Output Capability
  Defaults to English but adaptable for multilingual use through prompt engineering.

Quick Start with Transformers

import requests
import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor

model_path = "prithivMLmods/Bee-8B-RL-abliterated"

# Load model
model = AutoModel.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).to("cuda")

# Load processor
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

# Define conversation messages
messages = [{
    "role":
    "user",
    "content": [
        {
            "type": "image",
            "image": "https://huggingface.co/Open-Bee/Bee-8B-RL/resolve/main/assets/logo.png",
        },
        {
            "type": "text",
            "text": "Based on this picture, write an advertising slogan about Bee-8B (a Fully Open Multimodal Large Language Model)."
        },
    ],
}]

# Apply chat template
text = processor.apply_chat_template(messages,
                                     tokenize=False,
                                     add_generation_prompt=True,
                                     enable_thinking=True)

# Load image
image_url = "https://huggingface.co/Open-Bee/Bee-8B-RL/resolve/main/assets/logo.png"
image = Image.open(requests.get(image_url, stream=True).raw)

# Process inputs
inputs = processor(images=image, text=text, return_tensors="pt").to("cuda")

# Generate output
generated_ids = model.generate(**inputs, max_new_tokens=16384, temperature=0.6)
output_ids = generated_ids[0][len(inputs.input_ids[0]):]

# Decode output
output_text = processor.decode(output_ids, skip_special_tokens=True)

# Print result
print(output_text)

Intended Use

This model is suited for:

• Generating detailed, uncensored captions and reasoning for complex or creative visual datasets.
• Research in multimodal reasoning, safety evaluation, and content moderation studies.
• Enabling descriptive captioning and analytical reasoning for datasets excluded from mainstream models.
• Creative applications such as narrative generation, artistic interpretation, and visual storytelling.
• Advanced reasoning over diverse visual structures and aspect ratios.

Limitations

• May produce explicit, sensitive, or offensive content depending on input and prompt.
• Not recommended for deployment in production systems requiring strict moderation or filtering.
• Style, tone, and reasoning detail may vary based on prompt phrasing.
• May show variable performance on synthetic, abstract, or highly stylized visual inputs.