Train Your Own Opus-4.6 Reasoning Model with Unsloth: Achieve 80% of Opus Power & Integrate with OpenClaw to Save Big!

Are you tired of paying massive API bills for top-tier reasoning models like Claude 3 Opus? What if you could achieve 80% of Opus's reasoning capabilities running locally on your own hardware?

In this tutorial, we will use Unsloth to fine-tune a powerful Qwen-35B model on a highly curated reasoning dataset. Finally, we'll connect it to OpenClaw to act as a drop-in API replacement. The result? Insane cost savings with zero compromises on step-by-step logical reasoning.

Let’s dive into the code!

🛠️ Prerequisites

Before we start, ensure you have a Linux environment with an NVIDIA GPU (an 80GB VRAM GPU like an A100 is highly recommended due to the architecture constraints we'll discuss below).

Install the required dependencies:

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps "trl<0.9.0" peft accelerate bitsandbytes
pip install datasets

🧠 The Training Script Explained

We are using the Qwen/Qwen3.5-35B-A3B model. We will train it using a dataset specifically designed to teach the model how to "think" before it answers using <think> tags, mimicking the latest reasoning models.

Here is the complete, heavily commented, production-ready training script. Save this as train.py:

from unsloth import FastVisionModel # Import Unsloth's optimized language model loader
import os  # Import operating system interface
import torch  # Import PyTorch library
from datasets import load_dataset  # Import Hugging Face dataset loader
from trl import SFTConfig, SFTTrainer  # Import training configurations and SFT trainer

# --- 1. Configuration and Model Loading ---
output_model = "my_Opus_qwen3.5-35b-reasoning"  # Set the local path to save the model
final_model_path = f"{output_model}-lora"  # Set the local path to save the LoRA adapter
max_seq_length = 4096  # Define the maximum sequence length for context
dtype = None  # Use None for auto-detection of float16 or bfloat16

# ⚠️ CRITICAL HARDWARE NOTE:
# Qwen3 uses a Mamba-based architecture, which currently CANNOT be trained in 4-bit.
# Therefore, load_in_4bit MUST be set to False.
load_in_4bit = False  

# Load the base model and tokenizer using Unsloth's fast kernels
model, tokenizer = FastVisionModel.from_pretrained(
    model_name="Qwen/Qwen3.5-35B-A3B",  # Specify the model identifier
    max_seq_length=max_seq_length,  # Pass the max sequence length
    load_in_4bit=load_in_4bit,  # Apply 4-bit quantization if enabled
)

# Apply Parameter-Efficient Fine-Tuning (PEFT) using LoRA
model = FastVisionModel.get_peft_model(
    model,  # The loaded base model
    r=16,  # Rank of the LoRA update matrices
    finetune_vision_layers=False,  # False if not finetuning vision layers
    finetune_language_layers=True,  # False if not finetuning language layers
    finetune_attention_modules=True,  # False if not finetuning attention layers
    finetune_mlp_modules=True,  # False if not finetuning MLP layers
    lora_alpha=32,  # Scaling factor for LoRA
    lora_dropout=0,  # Standard LoRA dropout is 0 for efficiency
    bias="none",  # No bias terms updated to save memory
    use_gradient_checkpointing="unsloth",  # Use Unsloth's optimized gradient checkpointing
    random_state=3407,  # Set seed for reproducibility
)


# --- 2. Data Processing Function (Optimized for Reasoning) ---
def formatting_prompts_func(examples):
    problems = examples["problem"]  # Extract problem text from dataset
    thinkings = examples["thinking"]  # Extract reasoning/thought process
    solutions = examples["solution"]  # Extract final answer/solution
    texts = []  # Initialize list to store formatted strings

    for problem, thinking, solution in zip(problems, thinkings, solutions):
        # Format the response using the standard <think> tag convention
        full_response = f"<think>\n{thinking}\n</think>\n\n{solution}"

        # Construct a standardized chat message structure
        messages = [
            {
                "role": "system",
                "content": "You are a helpful assistant with strong reasoning capabilities. Please solve the problem by thinking step by step.",
            },
            {"role": "user", "content": problem},
            {"role": "assistant", "content": full_response},
        ]

        # Convert messages to a single string using the tokenizer's chat template
        formatted_text = tokenizer.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=False
        )
        texts.append(formatted_text)  # Append formatted string to list

    return {"text": texts}  # Return the processed dictionary


# --- 3. Dataset Preparation ---
# Load the specific reasoning dataset from Hugging Face
dataset = load_dataset("nohurry/Opus-4.6-Reasoning-3000x-filtered", split="train")

# Map the formatting function to the dataset across multiple processes
dataset = dataset.map(
    formatting_prompts_func,
    batched=True,  # Process in batches for speed
    remove_columns=dataset.column_names,  # Drop original columns to keep only the formatted text
    load_from_cache_file=False,  # Disable cache to ensure fresh processing
)

# --- 4. Training Configuration ---
trainer = SFTTrainer(
    model=model,  # The LoRA-enhanced model
    tokenizer=tokenizer,  # The associated tokenizer
    train_dataset=dataset,  # The processed training data
    dataset_text_field="text",  # The field containing the formatted strings
    max_seq_length=max_seq_length,  # Enforce max sequence length
    dataset_num_proc=4,  # Use 4 worker processes for data loading
    packing=False,  # Disable packing to maintain structural integrity
    args=SFTConfig(
        per_device_train_batch_size=2,  # Number of samples per GPU step
        gradient_accumulation_steps=4,  # Accumulate gradients to simulate larger batch size
        warmup_steps=20,  # Linear warmup for learning rate
        num_train_epochs=1,  # Number of full passes over the dataset
        learning_rate=2e-5,  # Initial learning rate
        fp16=not torch.cuda.is_bf16_supported(),  # Use fp16 if bf16 is unavailable
        bf16=torch.cuda.is_bf16_supported(),  # Use bf16 if supported by hardware
        logging_steps=1,  # Log progress every step
        save_steps=5,  # Save a checkpoint every 5 training steps
        save_total_limit=5,  # Retain only the 5 most recent checkpoints
        save_strategy="steps",  # Define saving strategy based on step count
        optim="adamw_8bit",  # Use 8-bit optimizer to save VRAM
        weight_decay=0.01,  # Apply L2 regularization
        lr_scheduler_type="cosine",  # Use cosine decay for learning rate
        seed=3407,  # Set seed for training consistency
        output_dir=f"output/{final_model_path}",  # Directory for training logs and checkpoints
        report_to="none",  # Disable reporting to external platforms
    ),
)

# Start the fine-tuning process
trainer.train()

# --- 5. Save the Final Model ---
model.save_pretrained(final_model_path)  # Save the LoRA adapters
tokenizer.save_pretrained(final_model_path)  # Save the tokenizer configuration

# MERGE TO 16-BIT FOR OPENCLAW INFERENCE
print("Merging model to 16-bit...")
model.save_pretrained_merged(
    "my_Opus_qwen3.5-35b-reasoning-merged",
    tokenizer,
)
print("Merge complete! Ready for OpenClaw deployment.")

⚠️ A Crucial Note on Qwen3 & 4-bit Training

You might notice load_in_4bit = False in the script. This is intentional. The latest Qwen architectures utilize Mamba under the hood. Currently, Mamba-based architectures cannot be quantized and trained in 4-bit precision. You must train it in native 16-bit/bfloat16. Ensure your GPU has the VRAM to handle this!

🚀 Step 2: Run the Training

Execute your script in your terminal:

python train.py

Unsloth will optimize the memory and compute, allowing you to fine-tune this massive 35B parameter model surprisingly fast. Once training is complete, the script will merge your LoRA adapters into the base model, outputting a folder named my_Opus_qwen3.5-35b-reasoning-merged.

🔌 Step 3: Integrating with OpenClaw (The Money Saver!)

Now that you have an open-source model capable of 80% of Opus's reasoning, it’s time to cut those API bills. We will use OpenClaw, an excellent open-source tool designed to act as a unified API gateway.

1. Serve the Model Locally (e.g., using vLLM)

First, host your merged model using an inference engine like vLLM:

python -m vllm.entrypoints.openai.api_server \
    --model ./my_Opus_qwen3.5-35b-reasoning-merged \
    --port 8000

2. Configure OpenClaw

OpenClaw allows you to route API requests to your local models while maintaining standard OpenAI/Anthropic API formats.

In your OpenClaw configuration (usually a config.yaml or via the web UI), add your new local model:

providers:
  - name: "Local_Opus_Alternative"
    base_url: "http://localhost:8000/v1"
    api_key: "sk-local"
    models:
      - "my_Opus_qwen3.5-35b-reasoning-merged"

3. Change Your App's Endpoint

In your existing applications (cursor, coding agents, chatbots), simply change the API Base URL to point to your OpenClaw server (e.g., http://localhost:3000/v1).

🎉 The Result?

Whenever your app requests complex reasoning, OpenClaw routes it to your custom-trained Unsloth model. Because the model has been trained on the Opus-4.6-Reasoning dataset to utilize <think> tags, it will output deep, logical reasoning steps before providing the final answer.

You now have a system that performs near the level of Claude 3 Opus, running completely locally, saving you hundreds or thousands of dollars in API fees!

Happy Training, and enjoy the savings! 🚀💸