Instructions to use hotdogs/Qwen35-Agent-R2A103 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hotdogs/Qwen35-Agent-R2A103 with Transformers:

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

pipe = pipeline("text-generation", model="hotdogs/Qwen35-Agent-R2A103")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("hotdogs/Qwen35-Agent-R2A103")
model = AutoModelForMultimodalLM.from_pretrained("hotdogs/Qwen35-Agent-R2A103")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use hotdogs/Qwen35-Agent-R2A103 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hotdogs/Qwen35-Agent-R2A103",
	filename="GGUF/Qwen35-Agent-R2A103.Q4_K_M.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/Qwen35-Agent-R2A103 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/Qwen35-Agent-R2A103:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen35-Agent-R2A103: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/Qwen35-Agent-R2A103:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen35-Agent-R2A103: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/Qwen35-Agent-R2A103:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hotdogs/Qwen35-Agent-R2A103: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/Qwen35-Agent-R2A103:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hotdogs/Qwen35-Agent-R2A103:Q4_K_M

Use Docker

docker model run hf.co/hotdogs/Qwen35-Agent-R2A103:Q4_K_M

LM Studio
Jan

vLLM

How to use hotdogs/Qwen35-Agent-R2A103 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hotdogs/Qwen35-Agent-R2A103"
# 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/Qwen35-Agent-R2A103",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hotdogs/Qwen35-Agent-R2A103:Q4_K_M

SGLang

How to use hotdogs/Qwen35-Agent-R2A103 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/Qwen35-Agent-R2A103" \
    --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/Qwen35-Agent-R2A103",
		"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/Qwen35-Agent-R2A103" \
        --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/Qwen35-Agent-R2A103",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use hotdogs/Qwen35-Agent-R2A103 with Ollama:
```
ollama run hf.co/hotdogs/Qwen35-Agent-R2A103:Q4_K_M
```

Unsloth Studio

How to use hotdogs/Qwen35-Agent-R2A103 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/Qwen35-Agent-R2A103 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/Qwen35-Agent-R2A103 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hotdogs/Qwen35-Agent-R2A103 to start chatting

How to use hotdogs/Qwen35-Agent-R2A103 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/Qwen35-Agent-R2A103: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/Qwen35-Agent-R2A103:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hotdogs/Qwen35-Agent-R2A103 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/Qwen35-Agent-R2A103: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/Qwen35-Agent-R2A103:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use hotdogs/Qwen35-Agent-R2A103 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf hotdogs/Qwen35-Agent-R2A103: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/Qwen35-Agent-R2A103: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/Qwen35-Agent-R2A103 with Docker Model Runner:
```
docker model run hf.co/hotdogs/Qwen35-Agent-R2A103:Q4_K_M
```

Lemonade

How to use hotdogs/Qwen35-Agent-R2A103 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hotdogs/Qwen35-Agent-R2A103:Q4_K_M

Run and chat with the model

lemonade run user.Qwen35-Agent-R2A103-Q4_K_M

List all available models

lemonade list

🚀 Qwen35-Agent-R2A103 — R2 + Agents-A1 Model Soup (0.7 : 0.3)

Building on hotdogs/Qwen35B-Agent-R2 as the base, blended with insights from InternScience/Agents-A1 via model soup (0.7 : 0.3).

🧬 How This Model Was Built

┌──────────────────────────────────────────────────────────────────┐
│               Qwen35-Agent-R2A103 Construction                   │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌─────────────────────┐      ┌──────────────────────────┐       │
│  │  Qwen35B-Agent-R2   │      │   InternScience/Agents-A1│       │
│  │  (7 LoRAs fused)    │      │   (Multi-teacher distilled)│      │
│  │  - Opus | Fable     │      │   - Tool Use | Reasoning  │       │
│  │  - Tool | Routing   │      │   - Search | Engineering  │       │
│  │  - Math | Mythos    │      │   - Scientific | Instruct │       │
│  │  - ToolFmt          │      │   - Full-domain SFT       │       │
│  └─────────┬───────────┘      └────────────┬──────────────┘       │
│            │                                │                      │
│            └────────── Model Soup ──────────┘                      │
│                        │  0.7 : 0.3                               │
│                        ▼                                          │
│            ┌──────────────────────┐                               │
│            │  Qwen35-Agent-R2A103 │                               │
│            │  31,666 tensors      │                               │
│            │  70.2 GB             │                               │
│            └──────────────────────┘                               │
│                        │                                          │
│                        ▼                                          │
│            ┌──────────────────────┐                               │
│            │  GGUF Quantization   │                               │
│            ├──────────────────────┤                               │
│            │ f16     → 65 GB      │                               │
│            │ Q4_K_M  → 20 GB      │                               │
│            │ Q6_K    → 27 GB      │                               │
│            └──────────────────────┘                               │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Model Soup (Weight Averaging)

Building on hotdogs/Qwen35B-Agent-R2 as the base, we blend corresponding weight tensors with InternScience/Agents-A1:

W_R2A103 = 0.7 × W_R2 + 0.3 × W_Agents-A1

This preserves the agentic reasoning and tool-use capabilities of R2 while incorporating the broader multi-domain agent skills (long-horizon search, engineering, scientific research) from Agents-A1.

Architecture Compatibility

Both models share the same qwen3_5_moe architecture:

Property	Value
Architecture	Qwen3.5 MoE
Hidden size	2048
Layers	40
Attention heads	16
KV heads	2
Experts	256 (8 active per token)
Shared experts	1
Vocab size	248,320
Context length	32,768

📦 Files

File	Size	Format
Safetensors (14 shards)	70 GB	Transformers
`GGUF/Qwen35-Agent-R2A103.f16.gguf`	65 GB	GGUF f16
`GGUF/Qwen35-Agent-R2A103.Q4_K_M.gguf`	20 GB	GGUF Q4_K_M
`GGUF/Qwen35-Agent-R2A103.Q6_K.gguf`	27 GB	GGUF Q6_K

🚀 Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hotdogs/Qwen35-Agent-R2A103",
    device_map="auto",
    trust_remote_code=True,
    torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained("hotdogs/Qwen35-Agent-R2A103")

messages = [{"role": "user", "content": "What is the capital of Thailand?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.6)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

With llama.cpp (GGUF)

# Q4_K_M (recommended - best size/speed/quality balance)
llama-cli \
  -m GGUF/Qwen35-Agent-R2A103.Q4_K_M.gguf \
  -n 256 -p "What is the capital of Thailand?" --temp 0.6 -ngl 99

# Or run as server:
llama-server \
  -m GGUF/Qwen35-Agent-R2A103.Q4_K_M.gguf \
  --port 8080 --host 0.0.0.0 -ngl 99 -c 4096

With Ollama

ollama create qwen35-r2a103 -f Modelfile
ollama run qwen35-r2a103

Modelfile:

FROM ./GGUF/Qwen35-Agent-R2A103.Q4_K_M.gguf

PARAMETER temperature 0.6
PARAMETER top_k 40
PARAMETER top_p 0.9
PARAMETER min_p 0.05
PARAMETER repeat_penalty 1.03

TEMPLATE "{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"

🧠 Capabilities

This model inherits skills from both parents:

Skill	From R2	From Agents-A1
✅ Tool calling	✓	✓
✅ Multi-step reasoning	✓	✓
✅ Instruction following	✓	✓
✅ Code generation	✓	✓
✅ Thai language	✓	✓
✅ Long-horizon search	-	✓
✅ Engineering tasks	-	✓
✅ Scientific reasoning	-	✓
✅ Vision (multimodal)	-	(via separate mmproj)

📊 Performance

Format	Size	BPW	Notes
f16	65 GB	16.0	Full precision reference
Q6_K	27 GB	6.58	High quality, fast
Q4_K_M	20 GB	4.88	Recommended
Q4_K_M inference	20 GB	—	~110 t/s on 7×RTX 3090

Benchmarked on 7× NVIDIA RTX 3090 with llama.cpp:

Prompt processing: 41.7 t/s (11 tokens)
Token generation: 92.1–110 t/s

🔗 References

R2 Base: hotdogs/Qwen35B-Agent-R2
Agents-A1: InternScience/Agents-A1
Qwen3.5 MoE: Qwen/Qwen-AgentWorld-35B-A3B
GGUF: llama.cpp

🙏 Credits

hotdogs — Qwen35B-Agent-R2 as the base model
InternScience / Agents-A1 — Multi-domain agent capabilities (tool-use, search, engineering, scientific reasoning, instruction following). Check out their paper
Qwen Team (Alibaba) — Qwen3.5 MoE architecture
llama.cpp — GGUF conversion and inference framework