Instructions to use hotdogs/Qwen35b-agent-R2O3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hotdogs/Qwen35b-agent-R2O3 with Transformers:

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

pipe = pipeline("text-generation", model="hotdogs/Qwen35b-agent-R2O3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hotdogs/Qwen35b-agent-R2O3")
model = AutoModelForCausalLM.from_pretrained("hotdogs/Qwen35b-agent-R2O3")
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-R2O3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hotdogs/Qwen35b-agent-R2O3",
	filename="GGUF-MTP/Qwen35b-agent-R2O3-MTP.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/Qwen35b-agent-R2O3 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-R2O3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen35b-agent-R2O3: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-R2O3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen35b-agent-R2O3: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-R2O3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hotdogs/Qwen35b-agent-R2O3: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-R2O3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hotdogs/Qwen35b-agent-R2O3:Q4_K_M

Use Docker

docker model run hf.co/hotdogs/Qwen35b-agent-R2O3:Q4_K_M

LM Studio
Jan

vLLM

How to use hotdogs/Qwen35b-agent-R2O3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hotdogs/Qwen35b-agent-R2O3"
# 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-R2O3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hotdogs/Qwen35b-agent-R2O3:Q4_K_M

SGLang

How to use hotdogs/Qwen35b-agent-R2O3 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-R2O3" \
    --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-R2O3",
		"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-R2O3" \
        --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-R2O3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use hotdogs/Qwen35b-agent-R2O3 with Ollama:
```
ollama run hf.co/hotdogs/Qwen35b-agent-R2O3:Q4_K_M
```

Unsloth Studio

How to use hotdogs/Qwen35b-agent-R2O3 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-R2O3 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-R2O3 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-R2O3 to start chatting

How to use hotdogs/Qwen35b-agent-R2O3 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-R2O3: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-R2O3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hotdogs/Qwen35b-agent-R2O3 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-R2O3: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-R2O3:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use hotdogs/Qwen35b-agent-R2O3 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-R2O3: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-R2O3: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-R2O3 with Docker Model Runner:
```
docker model run hf.co/hotdogs/Qwen35b-agent-R2O3:Q4_K_M
```

Lemonade

How to use hotdogs/Qwen35b-agent-R2O3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hotdogs/Qwen35b-agent-R2O3:Q4_K_M

Run and chat with the model

lemonade run user.Qwen35b-agent-R2O3-Q4_K_M

List all available models

lemonade list

Qwen35b-agent-R2O3 / README.md

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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
  - distillation
  - svd
  - lora
  - weight-diff
  - ornith
  - transformers
  - text-generation
  - thai
base_model:
  - hotdogs/Qwen35B-Agent-R2
datasets:
  - deepreinforce-ai/Ornith-1.0-35B
  - 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-R2O3 — Agent-R2 + Ornith (α=0.3)

Built on Qwen35B-Agent-R2 with SVD-extracted Ornith LoRA. The best of both worlds.

🧬 How This Model Was Built

┌─────────────────────────────────────────────────────────────┐
│              Qwen35b-Agent-R2O3 Construction                │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Qwen35B-Agent-R2 (Base)  ────────── 70% weights kept       │
│       │                                                     │
│       ├── 7 LoRAs already fused:                            │
│       │   Opus | Fable | Routing | Tool | Math | Mythos     │
│       │   | ToolFmt (all trained via SFT)                   │
│       │                                                     │
│       └── + Ornith LoRA (α=0.3) ← SVD Weight-Diff          │
│                                                             │
│  Ornith-1.0-35B                         Qwen-AgentWorld     │
│       │                                       │             │
│       └──────── Weight-Diff SVD ──────────────┘             │
│                    │                                        │
│             ┌──────┴──────┐                                 │
│             │  LoRA r=32  │ → Merged at α=0.3               │
│             │  422 tensors│                                  │
│             └─────────────┘                                  │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Step 1: SVD Weight-Diff Extraction

We extract Ornith's unique knowledge by computing the weight difference between Ornith-1.0-35B and the shared Huihui-Qwen-AgentWorld base:

delta = W_ornith - W_base         # What Ornith learned
U, S, Vh = torch.linalg.svd(delta) # Decompose
lora_A = diag(S[:32].sqrt()) @ Vh[:32, :]
lora_B = U[:, :32] @ diag(S[:32].sqrt())

422 tensors extracted across:

MLP layers (gate_proj, up_proj, down_proj) — knowledge execution
Expert weights (256 MoE experts) — specialized routing (3D tensor → flatten → SVD → reshape)
Shared expert — common computation
Norms + lm_head — output calibration
Attention layers skipped — R2 uses linear_attn vs Ornith's self_attn (incompatible architecture)

Step 2: Multi-LoRA Fusion

The extracted Ornith LoRA (r=32, α=64) is merged into Qwen35B-Agent-R2 at scale α=0.3:

merged = R2 * 0.7 + Ornith_LoRA * 0.3

This preserves 70% of R2's original capabilities (its 7 LoRAs) while adding 30% of Ornith's algorithm/reasoning strength.

Why α=0.3?

Scale	R2 Preserved	Ornith Added	Best For
0.3	70%	30%	Balanced — general agent use
0.4	60%	40%	Algorithm-heavy tasks
0.5+	<50%	>50%	⚠️ May dilute tool-calling

🔬 Technique: SVD Weight-Diff for MoE

MoE models (256 experts) require special handling for SVD extraction:

Component	Standard Approach	MoE Adaptation
2D tensors (MLP, norms)	`SVD(delta)` — normal	Same
3D expert tensors `[out, in, 256]`	N/A	`flatten → SVD → reshape`
Attention mismatch	Direct diff	❌ Skipped (R2 uses `linear_attn`)
language_model prefix	Exact match	Strip prefix after loading

Expert tensor handling:

delta = W_a - W_b                          # [512, 2048, 256]
delta_flat = delta.transpose(0,2).reshape(-1, delta.shape[1])  # [131072, 2048]
U, S, Vh = torch.linalg.svd(delta_flat)
lora_B = U[:, :32] @ diag(S[:32].sqrt())   # [131072, 32]

# On merge: reconstruct
delta = lora_B @ lora_A                    # [131072, 2048]
delta = delta.reshape(512, 256, 2048).permute(0, 2, 1)  # [512, 2048, 256]

📊 What You Get

Capability	Source	Retained
🧠 Reasoning (Opus 4.8)	R2	✅ 100%
🔧 Tool Calling	R2	✅ 100%
🧭 Agent Routing	R2	✅ 100%
📐 Math	R2 + Ornith	✅ Enhanced
⚡ Algorithm	Ornith 🆕	✅ +30%
💬 Conversation (Fable)	R2	✅ 100%
🎭 Creative (Mythos)	R2	✅ 100%

🏆 Why Agent-R2O3?

Aspect	Other Models	Agent-R2O3
Tool Call Format	❌ Often malformed	✅ Guaranteed valid `<tool_call>`
Algorithm Tasks	❌ Struggles on hard	✅ Orithm-enhanced
Thai Support	❌ Poor tokenization	✅ Native Thai + English
Knowledge	❌ Single source	✅ R2 (7 LoRAs) + Ornith

🚀 Usage

# llama.cpp
./llama-cli -m Qwen35b-agent-R2O3.Q4_K_M.gguf \
  -p "Hello" -n 100 --temp 0.6

# Full server with tool calling
./llama-server \
  -m Qwen35b-agent-R2O3.Q4_K_M.gguf \
  --host 0.0.0.0 --port 8081 -c 262144 -ngl 99 \
  --cache-type-k bf16 --cache-type-v bf16 \
  --flash-attn on --tools all --cont-batching \
  --temp 0.6 --top-k 40 --top-p 0.9 \
  --min-p 0.05 --repeat-penalty 1.03 \
  --jinja

📦 Downloads

File	Size	Quant
`Qwen35b-agent-R2O3.Q4_K_M.gguf`	20 GB	Recommended
`Qwen35b-agent-R2O3.Q6_K.gguf`	27 GB	High quality
`Qwen35b-agent-R2O3.f16.gguf`	65 GB	Full precision

🙏 Acknowledgements

Contribution	Source
Base Agent Model	hotdogs/Qwen35B-Agent-R2
Algorithm Knowledge	deepreinforce-ai/Ornith-1.0-35B
SVD Extraction Method	Weight-Diff SVD (Universial Adapter Extraction)
Infrastructure	Nous Research — Hermes Agent

💖 Support

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