Model overview
Huihui-Qwen3.5-35B-A3B-abliterated is an uncensored variant of the Qwen3.5-35B model created using abliteration techniques to remove safety filtering mechanisms. The model represents a proof-of-concept implementation designed for research and experimental use in controlled environments. Similar variants exist in the huihui-ai collection, including QwQ-32B-abliterated and Qwen2.5-32B-Instruct-abliterated, which apply the same abliteration methodology to different base models.
Model inputs and outputs
The model processes text prompts and generates text responses. It supports advanced features including thinking mode through a specialized chat template file, and is compatible with tool calling functionality when deployed with appropriate inference servers.
Inputs
- Text prompts for conversation and instruction following
- System messages to guide model behavior and responses
- Multi-turn conversations for maintaining context across interactions
Outputs
- Generated text responses without standard safety constraints
- Structured outputs compatible with tool calling and function execution
- Extended reasoning when using the thinking mode template
Capabilities
The model handles general language tasks including question answering, text generation, creative writing, and problem-solving. It processes and responds to sensitive or controversial topics without the refusal mechanisms present in standard variants. The model supports integration with inference frameworks like Ollama and llama-server, making it accessible across different deployment scenarios.
What can I use it for?
This model suits research applications, testing scenarios, and controlled experimental environments where reduced content filtering is necessary. Researchers studying safety mechanisms, model behavior, and alignment techniques can use it for investigation purposes. The model also enables development and testing in sandboxed settings where monitoring and manual review processes are in place. However, it is not recommended for production systems, public-facing applications, or any context requiring guaranteed safety compliance.
Things to try
Test how the model responds to requests that standard models would refuse, comparing outputs to understand where safety filtering typically occurs. Explore the thinking mode capability using the chat template file to see how the model approaches complex reasoning tasks. Experiment with tool calling features in llama-server to understand how reduced safety constraints affect function calling behavior. Consider investigating the mechanics of how abliteration changes model outputs compared to the original Qwen3.5-35B base model through comparative testing in isolated environments.
This is a simplified guide to an AI model called Huihui-Qwen3.5-35B-A3B-abliterated maintained by huihui-ai. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.