rbinrs-Kimi-K2.5-BF16-abliterated-GGUF

Instructions to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF", dtype="auto")

llama-cpp-python

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF",
	filename="Q2_K-GGUF/Q2_K-GGUF-00001-of-00041.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Use Docker

docker model run hf.co/rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

SGLang

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with Ollama:
```
ollama run hf.co/rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
```

Unsloth Studio

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF to start chatting

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
```

Lemonade

How to use rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Run and chat with the model

lemonade run user.rbinrs-Kimi-K2.5-BF16-abliterated-GGUF-Q2_K

List all available models

lemonade list

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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Downloads last month: 251

GGUF

Model size

1T params

Architecture

deepseek2

Hardware compatibility

2-bit

Model tree for rbinrs/rbinrs-Kimi-K2.5-BF16-abliterated-GGUF

Base model

moonshotai/Kimi-K2.5

Quantized

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