Qwen35B-Agent-R2

🏆 Why Agent-R2?

Agent-R2 is a multi-LoRA fusion model built on Qwen/Qwen-AgentWorld-35B-A3B — combining 7 specialized LoRA adapters into one cohesive agent powerhouse:

Result: A model that thinks, acts, and communicates — not just a chatbot, but an agent.

🔍 What Makes Agent-R2 Different?

📊 Architecture

🧬 Training Pipeline: SFT + Distillation

Agent-R2 is built using a two-stage SFT + Distillation approach:

Stage 1: Supervised Fine-Tuning (SFT) 🏋️

Each LoRA adapter was trained via SFT on a specialized dataset:

Stage 2: Distillation + Fusion 🔬

Teacher Models (Claude Opus 4.8 + Fable + AgentWorld)
         │
         ├── SFT LoRA Training (individually)
         │     Opus SFT  ────►  LoRA_opus
         │     Fable SFT ────►  LoRA_fable
         │     Routing   ────►  LoRA_routing
         │     Tool Call ────►  LoRA_tool
         │     Math Fix  ────►  LoRA_math
         │     Mythos    ────►  LoRA_mythos
         │     ToolFmt   ────►  LoRA_toolfmt
         │
         └── Multi-LoRA Fusion Merge (SFT → Distill)
               Weighted fusion → Agent-R2

Why SFT + Distill?

• SFT teaches the model what to do via supervised examples
• Distillation (via LoRA fusion) transfers knowledge from multiple teacher domains into a single student model
• The result: one model that inherits reasoning depth from Opus, conversational warmth from Fable, and tool precision from AgentWorld — without needing RL/CPT

Each LoRA was trained independently on carefully curated datasets, then fused at optimized ratios through iterative testing on AgentWorld benchmarks. The result is a model where each capability complements the others — not competing, but collaborating.

🚀 Usage

ollama run nutboy02/Qwen35B-Agent-R2

Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

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

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]))

vLLM (Recommended for Production)

The model works directly with vLLM from HuggingFace Safetensors — no AWQ/GPTQ conversion needed:

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

# Or use with local safetensors
vllm serve /path/to/Qwen35B-Agent-R2 \
  --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. Needs 2× GPUs for full speed.
• bitsandbytes 4-bit — AutoModelForCausalLM.from_pretrained(..., load_in_4bit=True) for limited VRAM.

🧪 Benchmark Results

AgentWorld Evaluation

Detailed benchmark numbers available upon request — we continuously improve.

✅ What Agent-R2 Excels At

• 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
• Knowledge Q&A — Up-to-date knowledge with admit-when-unknown honesty
• Reasoning Tasks — Step-by-step chain-of-thought via Opus 4.8 training

💖 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

• Qwen Team (Alibaba) — For the incredible Qwen3.6 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