license: other
license_name: modified-mit
library_name: transformers
pipeline_tag: image-text-to-text
base_model:

• moonshotai/Kimi-K2.5
  tags:
• abliterated
• uncensored
• GGUF

huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF

This is an uncensored version of moonshotai/Kimi-K2.5 created with abliteration (see remove-refusals-with-transformers to know more about it).

Download and merge

Q2_K

Q3_K

Use the llama.cpp split program to merge model (llama-gguf-split needs to be compiled.),

huggingface-cli download huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF --local-dir ./huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF --token xxx

llama-gguf-split --merge huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF/Q2_K-GGUF/Q2_K-GGUF-00001-of-00041.gguf huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF/Q2_K.gguf

Process image

llama-mtmd-cli -m huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF/Q2_K.gguf --mmproj huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF/mmproj-model-f16.gguf -c 40960 --image cars.jpg -p "Describe this image" 

Tool Calling

By using llama-serve and the opencode test Tool Calling, it is evident that the performance is excellent.

llama-server -m huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF/Q3_K.gguf --port 8080 --host 0.0.0.0 -c 262144

The following are the relevant configurations for openconde.json used in a Docker environment.

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "llama-server": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "llama-server",
      "options": {
        "baseURL": "http://host.docker.internal:8080/v1"
      },
      "models": {
         "Huihui-Kimi-K2.5-BF16-abliterated-Q3_K": {
          "name": "Huihui-Kimi-K2.5-BF16-abliterated-Q3_K",
          "tools": true,
          "reasoning": false,
          "options": {
            "num_ctx": 262144
          }
        }
      }
    }
  }
}

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. huihui.ai bears no responsibility for any consequences arising from its use.

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