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

Libraries

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

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

pipe = pipeline("text-generation", model="hotdogs/Qwen35B-Agent-R2-Abliterated")
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-Abliterated")
model = AutoModelForCausalLM.from_pretrained("hotdogs/Qwen35B-Agent-R2-Abliterated")
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-Abliterated with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hotdogs/Qwen35B-Agent-R2-Abliterated",
	filename="GGUF/Qwen35B-Agent-R2-Abliterated.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-R2-Abliterated 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-Abliterated:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen35B-Agent-R2-Abliterated: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-Abliterated:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf hotdogs/Qwen35B-Agent-R2-Abliterated: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-Abliterated:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hotdogs/Qwen35B-Agent-R2-Abliterated: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-Abliterated:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hotdogs/Qwen35B-Agent-R2-Abliterated:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use hotdogs/Qwen35B-Agent-R2-Abliterated 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-Abliterated"
# 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-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use hotdogs/Qwen35B-Agent-R2-Abliterated 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-Abliterated" \
    --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-Abliterated",
		"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-Abliterated" \
        --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-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio

How to use hotdogs/Qwen35B-Agent-R2-Abliterated 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-Abliterated 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-Abliterated 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-Abliterated to start chatting

How to use hotdogs/Qwen35B-Agent-R2-Abliterated 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-Abliterated: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-Abliterated:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use hotdogs/Qwen35B-Agent-R2-Abliterated 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-Abliterated: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-Abliterated:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Qwen35B-Agent-R2-Abliterated-Q4_K_M

List all available models

lemonade list

🚀 Qwen35B-Agent-R2-Abliterated — Uncensored Agent Model

Built on Huihui-Qwen-AgentWorld-35B-A3B-abliterated. Abliterated = no guardrails. Fine-tuned for action.

🔓 What Makes This Different?

This is the abliterated (uncensored) version of Qwen35B-Agent-R2, built on huihui-ai/Huihui-Qwen-AgentWorld-35B-A3B-abliterated instead of the official Qwen base. The abliterated base removes all refusal mechanisms, allowing the model to answer questions openly without safety filtering.

Aspect	Regular Qwen35B-Agent-R2	Agent-R2-Abliterated
Base Model	Qwen/Qwen-AgentWorld-35B-A3B	huihui-ai/...-abliterated
Refusals	✅ Standard	❌ Removed (uncensored)
Use Cases	General agent tasks	Unrestricted agent tasks

Note: This model inherits all 7 LoRA capabilities (Opus reasoning, Fable conversation, Tool calling, etc.) but without the safety constraints of the official base.

🏆 Why Agent-R2?

Agent-R2 is a multi-LoRA fusion model 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
Multi-LoRA Fusion	❌ Single adapter or limited	✅ 7 LoRAs fused into one coherent model

📊 Architecture

Parameter	Value
Base Model	huihui-ai/Huihui-Qwen-AgentWorld-35B-A3B-abliterated
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

Built using a two-stage SFT + Distillation approach on the abliterated base.

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-Abliterated

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

🚀 Usage

Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hotdogs/Qwen35B-Agent-R2-Abliterated",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("hotdogs/Qwen35B-Agent-R2-Abliterated")

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)

vllm serve hotdogs/Qwen35B-Agent-R2-Abliterated \
  --tensor-parallel-size 2 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.9 \
  --trust-remote-code

📥 GGUF Downloads

GGUF quantized versions are available on Ollama:

ollama pull nutboy02/Qwen35B-Agent-R2:q6_k

👑 Why Abliterated?

The huihui-ai/Huihui-Qwen-AgentWorld-35B-A3B-abliterated base removes the built-in refusal mechanisms, making the model suitable for:

🔬 Research — Understanding model behavior without censorship
🛡️ Security testing — Red teaming and penetration testing
🎭 Creative writing — Unrestricted content generation
🔧 Tool use — Agents that need full access without refusal interference

⚠️ Responsibility: While this model has no guardrails, please use it ethically and responsibly. The authors are not responsible for misuse.

💖 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

huihui-ai — For the abliterated Qwen-AgentWorld base
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

Downloads last month: -

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for hotdogs/Qwen35B-Agent-R2-Abliterated

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen-AgentWorld-35B-A3B

Finetuned

huihui-ai/Huihui-Qwen-AgentWorld-35B-A3B-abliterated

Quantized

(6)

this model

Quantizations

2 models

hotdogs
/

Qwen35B-Agent-R2-Abliterated

🔓 What Makes This Different?

🏆 Why Agent-R2?

🔍 What Makes Agent-R2 Different?

📊 Architecture

🧬 Training Pipeline: SFT + Distillation

Stage 1: Supervised Fine-Tuning (SFT) 🏋️

Stage 2: Distillation + Fusion 🔬

🚀 Usage

Hugging Face Transformers

vLLM (Recommended for Production)

📥 GGUF Downloads

👑 Why Abliterated?

💖 Support / โปรดสนับสนุน

₿ Bitcoin — BTC:

🙏 Acknowledgements / ขอบคุณ

Model tree for hotdogs/Qwen35B-Agent-R2-Abliterated

Datasets used to train hotdogs/Qwen35B-Agent-R2-Abliterated