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 / README.md

hotdogs

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	---
	license: apache-2.0
	language:
	- en
	- th
	tags:
	- qwen
	- moe
	- mixture-of-experts
	- agent
	- agent-world
	- tool-use
	- tool-calling
	- reasoning
	- agents-a1
	- model-soup
	- weight-averaging
	- transformers
	- text-generation
	base_model:
	- hotdogs/Qwen35B-Agent-R2
	library_name: transformers
	pipeline_tag: text-generation
	---

	<p align="center">
	<img src="https://img.shields.io/badge/license-Apache--2.0-green">
	<img src="https://img.shields.io/badge/Qwen3.5-35B%20A3B-blue">
	<img src="https://img.shields.io/badge/MoE-256%20experts-orange">
	<img src="https://img.shields.io/badge/Model_Soup-0.7%20R2%20%2B%200.3%20Agents--A1-ff69b4">
	<img src="https://img.shields.io/badge/R2A103-purple">
	</p>

	<p align="center"><b>🚀 Qwen35-Agent-R2A103 — R2 + Agents-A1 Model Soup (0.7 : 0.3)</b></p>

	<p align="center"><i>Building on hotdogs/Qwen35B-Agent-R2 as the base, blended with insights from InternScience/Agents-A1 via model soup (0.7 : 0.3).</i></p>

	---

	## 🧬 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

	```python
	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)

	```bash
	# 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

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

	Modelfile:
	```dockerfile
	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](https://huggingface.co/hotdogs/Qwen35B-Agent-R2)
	- Agents-A1: [InternScience/Agents-A1](https://huggingface.co/InternScience/Agents-A1)
	- Qwen3.5 MoE: [Qwen/Qwen-AgentWorld-35B-A3B](https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B)
	- GGUF: [llama.cpp](https://github.com/ggml-org/llama.cpp)

	---

	## 🙏 Credits

	- [hotdogs](https://huggingface.co/hotdogs) — Qwen35B-Agent-R2 as the base model
	- [InternScience / Agents-A1](https://huggingface.co/InternScience/Agents-A1) — Multi-domain agent capabilities (tool-use, search, engineering, scientific reasoning, instruction following). Check out their [paper](https://arxiv.org/abs/2606.30616)
	- [Qwen Team (Alibaba)](https://huggingface.co/Qwen) — Qwen3.5 MoE architecture
	- llama.cpp — GGUF conversion and inference framework

	---

	## 📄 License

	Apache 2.0