Instructions to use hotdogs/Qwen35B-Agent-R2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hotdogs/Qwen35B-Agent-R2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hotdogs/Qwen35B-Agent-R2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hotdogs/Qwen35B-Agent-R2")
model = AutoModelForCausalLM.from_pretrained("hotdogs/Qwen35B-Agent-R2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use hotdogs/Qwen35B-Agent-R2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hotdogs/Qwen35B-Agent-R2",
	filename="GGUF/Qwen35B-Agent-R2-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hotdogs/Qwen35B-Agent-R2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M

Use Docker

docker model run hf.co/hotdogs/Qwen35B-Agent-R2:Q4_K_M

LM Studio
Jan

vLLM

How to use hotdogs/Qwen35B-Agent-R2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hotdogs/Qwen35B-Agent-R2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/Qwen35B-Agent-R2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hotdogs/Qwen35B-Agent-R2:Q4_K_M

SGLang

How to use hotdogs/Qwen35B-Agent-R2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "hotdogs/Qwen35B-Agent-R2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/Qwen35B-Agent-R2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "hotdogs/Qwen35B-Agent-R2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hotdogs/Qwen35B-Agent-R2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use hotdogs/Qwen35B-Agent-R2 with Ollama:
```
ollama run hf.co/hotdogs/Qwen35B-Agent-R2:Q4_K_M
```

Unsloth Studio

How to use hotdogs/Qwen35B-Agent-R2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for hotdogs/Qwen35B-Agent-R2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for hotdogs/Qwen35B-Agent-R2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hotdogs/Qwen35B-Agent-R2 to start chatting

How to use hotdogs/Qwen35B-Agent-R2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "hotdogs/Qwen35B-Agent-R2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hotdogs/Qwen35B-Agent-R2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default hotdogs/Qwen35B-Agent-R2:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use hotdogs/Qwen35B-Agent-R2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/Qwen35B-Agent-R2:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "hotdogs/Qwen35B-Agent-R2:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use hotdogs/Qwen35B-Agent-R2 with Docker Model Runner:
```
docker model run hf.co/hotdogs/Qwen35B-Agent-R2:Q4_K_M
```

Lemonade

How to use hotdogs/Qwen35B-Agent-R2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hotdogs/Qwen35B-Agent-R2:Q4_K_M

Run and chat with the model

lemonade run user.Qwen35B-Agent-R2-Q4_K_M

List all available models

lemonade list

Qwen35B-Agent-R2 / README.md

hotdogs

Update README.md

24accd4 verified about 6 hours ago

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metadata

license: agpl-3.0
language:
  - en
  - th
tags:
  - qwen
  - moe
  - mixture-of-experts
  - agent
  - agent-world
  - tool-use
  - tool-calling
  - reasoning
  - sft
  - opus
  - fable
  - conversational
  - transformers
  - text-generation
  - thai
  - ykai
base_model:
  - Qwen/Qwen-AgentWorld-35B-A3B
datasets:
  - hotdogs/uka-fable-reasoning
  - 11-47/claude_opus_4.8_max_thinking_5k_v2
  - cx-cmu/agent_trajectories
library_name: transformers
pipeline_tag: text-generation

🚀 Qwen35B-Agent-R2 — The Next Generation Agent Model

Built on Qwen/Qwen-AgentWorld-35B-A3B. Fine-tuned for action.

🏆 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:

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 thinks, acts, and communicates — not just a chatbot, but an agent.

🔍 What Makes Agent-R2 Different?

Aspect	Other Models	Agent-R2
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
MoE Efficiency	❌ Full 35B always active	✅ Only ~3B active per token
Multi-LoRA Fusion	❌ Single adapter or limited	✅ 7 LoRAs fused into one coherent model

📊 Architecture

Parameter	Value
Base Model	Qwen/Qwen-AgentWorld-35B-A3B
Architecture	Qwen3.5 MoE
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

🧬 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:

Adapter	Method	Data	Purpose
Opus SFT	SFT	6,956 rows (Claude Opus 4.8 reasoning)	Learn deep chain-of-thought
Fable SFT	SFT	3,376 rows (Fable conversational)	Natural dialogue
Agent Routing	SFT	AgentWorld trajectories	Tool selection logic
Tool Call	SFT	8,653 rows (agent trajectories)	Proper invocation format
Math Fix	SFT	Math reasoning data	Accurate computation
Mythos	SFT	Creative writing data	Response diversity
ToolFmt	SFT	Format-annotated traces	Strict `<tool_call>` JSON

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

Metric	Score
Tool Call Accuracy	✅ High
Task Completion Rate	✅ High
Format Compliance	✅ 100%
Thai Language Quality	✅ Native-level

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 QR — Donate

₿ 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