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", device_map="auto")
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]:]))

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 Desktop

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 @earendil-works/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

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

Hermes Agent

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

OpenClaw

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"

hotdogs commited on Jul 15

Commit

97952a2

verified ·

1 Parent(s): 4631c5c

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +26 -38

README.md CHANGED Viewed

@@ -44,44 +44,32 @@ pipeline_tag: text-generation
 │               Qwen35-Agent-R2A103 Construction                   │
 ├──────────────────────────────────────────────────────────────────┤
 │                                                                  │
-│  ┌─────────────────────────────────────┐                         │
-│  │      Qwen35B-Agent-R2  (BASE)       │                         │
-│  │  ┌───────────────────────────────┐  │                         │
-│  │  │  7 LoRAs fused via merge.py   │  │                         │
-│  │  │  - Opus    (α=0.5)            │  │                         │
-│  │  │  - Fable   (α=0.15)           │  │                         │
-│  │  │  - Tool    (α=0.5)            │  │                         │
-│  │  │  - Routing (α=0.1)            │  │                         │
-│  │  │  - Math    (α=0.3)            │  │                         │
-│  │  │  - Mythos  (α=0.3)            │  │                         │
-│  │  │  - ToolFmt (α=0.3)            │  │                         │
-│  │  └───────────────────────────────┘  │                         │
-│  └────────────────┬────────────────────┘                         │
-│                   │                                              │
-│                   │  Model Soup: 0.7 × R2 + 0.3 × Agents-A1     │
-│                   │                                              │
-│  ┌────────────────▼────────────────────┐                         │
-│  │      InternScience/Agents-A1        │                         │
-│  │  (Multi-teacher distilled)          │──► blended at 0.3       │
-│  │  - Tool Use | Reasoning             │                         │
-│  │  - Search | Engineering             │                         │
-│  │  - Scientific | Instruction         │                         │
-│  └─────────────────────────────────────┘                         │
-│                   │                                              │
-│                   ▼                                              │
-│  ┌─────────────────────────────────────┐                         │
-│  │        Qwen35-Agent-R2A103          │                         │
-│  │        31,666 tensors · 70.2 GB     │                         │
-│  └────────────────┬────────────────────┘                         │
-│                   │                                              │
-│                   ▼                                              │
-│  ┌─────────────────────────────────────┐                         │
-│  │          GGUF Quantization          │                         │
-│  ├─────────────────────────────────────┤                         │
-│  │ f16     → 65 GB                     │                         │
-│  │ Q4_K_M  → 20 GB (Recommended)       │                         │
-│  │ Q6_K    → 27 GB                     │                         │
-│  └─────────────────────────────────────┘                         │
 │                                                                  │
 └──────────────────────────────────────────────────────────────────┘
 ```

 │               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      │                               │
+│            └──────────────────────┘                               │
 │                                                                  │
 └──────────────────────────────────────────────────────────────────┘
 ```