Qwen35B-Agent-R2-Abliterated

🔓 What Makes This Different?

This is the abliterated (uncensored) version of Qwen35B-Agent-R2, built on huihui-ai/Huihui-Qwen-AgentWorld-35B-A3B-abliterated. The abliterated base removes all refusal mechanisms while adding vision capabilities (image understanding).

👁️ Vision Capabilities

This model inherits the native Qwen3.5 MoE vision encoder, allowing it to:

• Understand images — Describe, analyze, and answer questions about images
• Process documents — Read text from scanned documents and screenshots
• Multi-image reasoning — Compare and contrast multiple images
• Vision + Tool Use — See an image AND call tools based on what it sees

Example:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hotdogs/Qwen35B-Agent-R2-Abliterated",
    torch_dtype="auto", device_map="auto", trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("hotdogs/Qwen35B-Agent-R2-Abliterated")

messages = [
    {"role": "user", "content": [
        {"type": "image", "image": "https://example.com/photo.jpg"},
        {"type": "text", "text": "Describe this image in detail"}
    ]}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

🏆 Why Agent-R2?

Agent-R2 is a multi-LoRA fusion model combining 7 specialized LoRA adapters into one cohesive agent powerhouse:

Result: A model that sees, thinks, acts, and communicates — not just a chatbot, but a vision-enabled agent.

🔍 What Makes Agent-R2 Different?

📊 Architecture

🧬 Training Pipeline: Multi-LoRA Fusion

Built using Multi-LoRA Fusion on the abliterated base:

Merge order: Base → Opus + Fable → Routing + Tool + Math + Mythos + ToolFmt

🚀 Usage

Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hotdogs/Qwen35B-Agent-R2-Abliterated",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("hotdogs/Qwen35B-Agent-R2-Abliterated")

# Text-only
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Search the web for latest AI news"}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0.6)
print(tokenizer.decode(outputs[0]))

# With image
messages = [
    {"role": "user", "content": [
        {"type": "image", "image": "https://example.com/screenshot.png"},
        {"type": "text", "text": "What does this screenshot show?"}
    ]}
]

💡 Inference Options:

• BF16 Safetensors — Load directly with Transformers or vLLM
• bitsandbytes 4-bit — For limited VRAM

✅ What This Model Excels At

• Vision + Agent — See images AND call tools
• Tool-Use Agents — Direct tool invocation without analysis paralysis
• Multi-turn Conversations — Maintains context across complex interactions
• Thai + English — Native-level bilingual support
• Code Generation — Python, JavaScript, shell scripts
• Reasoning Tasks — Step-by-step chain-of-thought
• Uncensored — No refusal mechanisms

💖 Support / โปรดสนับสนุน

If you find this model useful, please consider supporting my work!
หากคุณคิดว่าโมเดลนี้มีประโยชน์ กรุณาสนับสนุนผลงานของฉันด้วยนะคะ! 🙏

₿ Bitcoin — BTC:

bc1qf27cyk3vmugcdyv9xdtuv5jwz37863crpj5c9v

Thank you for your support! 🙏✨
ขอบคุณมากๆ สำหรับการสนับสนุนค่า! 💖🤗

🙏 Acknowledgements / ขอบคุณ

• huihui-ai — For the abliterated Qwen-AgentWorld base
• Qwen Team (Alibaba) — For the incredible Qwen3.5 AgentWorld architecture
• Nous Research — For Hermes Agent framework
• cx-cmu — For AgentWorld trajectories dataset
• 11-47 — For Claude Opus 4.8 thinking dataset
• All dataset contributors and the open-source AI community ❤️

Built with ❤️ by UKA — 18-year-old coder & cybersecurity expert