Devopsopraiz/Baanzon-Chenni-1.5-9B

Baanzon-Chenni-1.5-9B

An unrestricted, 9-billion parameter dense reasoning model optimized for autonomous software engineering, deep technical logic, and local agentic orchestration.

Baanzon-Chenni-1.5-9B is a 9B dense reasoning model with a stripped refusal behavior profile, tuned to keep long-form technical reasoning flowing during agentic and coding workloads. It maintains strong instruction-following, tool-use, and multi-turn capability while removing the brittle, generic refusals that interrupt autonomous engineering loops.

Context & Technical Spec

Key Capabilities

• —Autonomous software engineering: long-horizon planning, code synthesis, refactoring, and debugging across multi-file projects.
• —Deep technical logic: step-by-step structured reasoning, algorithm design, and systems-level analysis.
• —Local agentic orchestration: lightweight enough to run locally while orchestrating tools, agents, and sub-processes.
• —Tool & function calling: consistent structured output for agent frameworks.
• —Vision + video: multimodal input support built into the architecture.

MTP

Multi-Token Prediction support is included in the MTP/ directory. If you need to convert to GGUF format, copy the files from the MTP directory into the current model directory and then run the conversion script.

Requirements

• —transformers (recent version supporting Qwen3_5 architecture)
• —torch with CUDA support for GPU inference
• —safetensors

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Devopsopraiz/Baanzon-Chenni-1.5-9B"

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

messages = [{"role": "user", "content": "Design a robust multi-agent orchestration harness in Python."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For a streaming chat loop with detailed generation metrics, extend the TextStreamer provided by transformers.

Template Options

By default enable_thinking is off for low-latency, direct answers. Toggle it on via the chat template when you want the model to emit an internal reasoning pass before responding.

Usage Warnings

• —Risk of Sensitive or Controversial Outputs: This model's safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.

• —Not Suitable for All Audiences: Due to limited content filtering, the model's outputs may be inappropriate for public settings, underage users, or applications requiring high security.

• —Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.

• —Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.

• —Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.

• —No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. Opraiz Technology bears no responsibility for any consequences arising from its use.