empero-ai/Qwen3.5-9B-Claude-Code

user → <think>...</think> → <tool_call> → <tool_result> → response → repeat

<tool_call>
<tool>Read</tool>
<input>
{"file_path": "/path/to/file.py"}
</input>
</tool_call>

<tool_result>
<tool>Read</tool>
<output>
... file contents ...
</output>
</tool_result>

<think>
The user wants to refactor the auth module. I should first read the existing
implementation before suggesting changes.
</think>

You are an expert AI coding assistant. You help users with software engineering
tasks including writing code, debugging, refactoring, explaining code, and more.
You think through problems carefully inside <think> tags before acting.
You have access to tools like Read, Edit, Write, Bash, Grep, Glob, and others
to interact with the user's codebase. You use tools when needed and provide
clear, concise responses. You show your reasoning process and use tools
methodically to accomplish tasks.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "empero-ai/Qwen3.5-9B-Claude-Code"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "system",
        "content": (
            "You are an expert AI coding assistant. You help users with software engineering "
            "tasks including writing code, debugging, refactoring, explaining code, and more. "
            "You think through problems carefully inside <think> tags before acting. "
            "You have access to tools like Read, Edit, Write, Bash, Grep, Glob, and others "
            "to interact with the user's codebase. You use tools when needed and provide "
            "clear, concise responses. You show your reasoning process and use tools "
            "methodically to accomplish tasks."
        ),
    },
    {"role": "user", "content": "Read the file main.py and explain what it does."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1024,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
    )

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))