hotdogs/Qwen35B-Agent-R2-Abliterated

🚀 Qwen35B-Agent-R2-Abliterated — Uncensored Vision + Agent Model

Built on huihui-ai/Huihui-Qwen-AgentWorld-35B-A3B-abliterated. Abliterated = no guardrails. Vision + Agent + Thai.

🔓 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).

Aspect	Regular Qwen35B-Agent-R2	Agent-R2-Abliterated
Base Model	Qwen/Qwen-AgentWorld-35B-A3B	huihui-ai/...-abliterated
Refusals	✅ Standard	❌ Removed (uncensored)
Use Cases	General agent tasks	Unrestricted agent + vision tasks

👁️ 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:

Capability	Benefit
🧠 Reasoning	Opus 4.8-level chain-of-thought for complex tasks
💬 Conversation	Fable SFT for natural, engaging dialogue
🔧 Tool Calling	Precise <tool_call> format — no more stuck planning
🧭 Agent Routing	Correct tool selection on first try
📐 Math	Accurate numerical reasoning
🎭 Mythos	Creative and diverse response generation
✅ Format Integrity	ToolFmt ensures every call is syntactically valid

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

🔍 What Makes Agent-R2 Different?

Aspect	Other Models	Agent-R2-Abliterated
Tool Call Format	❌ Often malformed or hallucinated	✅ Guaranteed valid <tool_call> JSON
Planning vs Action	❌ Thinks forever, never acts	✅ Decides → Calls tool → Done
Thai Support	❌ Poor or tokenization issues	✅ Native Thai + English bilingual
Multi-LoRA Fusion	❌ Single adapter or limited	✅ 7 LoRAs fused into one coherent model
Vision	❌ Text-only or separate model	✅ Built-in image understanding
Uncensored	❌ Guardrails block queries	✅ Abliterated — no refusals

📊 Architecture

Parameter	Value
Base Model	huihui-ai/Huihui-Qwen-AgentWorld-35B-A3B-abliterated
Architecture	Qwen3.5 MoE (Vision + Text)
Hidden Size	2,048
Expert Count	256 (Mixture of Experts)
Active Experts	8 per token (~3B active params)
Parameters	~35B total
Context Length	8,192 tokens
Precision	BF16 (Safetensors)
Format	ChatML
Vision	✅ Native Qwen3.5 vision encoder

🧬 Training Pipeline: Multi-LoRA Fusion

Built using Multi-LoRA Fusion on the abliterated base:

Adapter	Data
Opus SFT	6,956 rows (Opus 4.8 reasoning)
Fable SFT	3,376 rows (Fable conversational)
Agent Routing	AgentWorld trajectories
Tool Call	8,653 rows (agent trajectories)
Math Fix	Math reasoning data
Mythos	Creative writing data
ToolFmt	Format-annotated traces

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?"}
    ]}
]

vLLM

vllm serve hotdogs/Qwen35B-Agent-R2-Abliterated \
  --tensor-parallel-size 2 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.9 \
  --trust-remote-code

💡 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

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💖 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! 💖🤗

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🙏 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 ❤️

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