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

Instructions to use lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lhca521/Huihui-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="lhca521/Huihui-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("lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF", dtype="auto")

llama-cpp-python

How to use lhca521/Huihui-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="lhca521/Huihui-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 lhca521/Huihui-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 lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf lhca521/Huihui-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 lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf lhca521/Huihui-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 lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf lhca521/Huihui-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 lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Use Docker

docker model run hf.co/lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use lhca521/Huihui-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 "lhca521/Huihui-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": "lhca521/Huihui-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/lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

SGLang

How to use lhca521/Huihui-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 "lhca521/Huihui-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": "lhca521/Huihui-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 "lhca521/Huihui-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": "lhca521/Huihui-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 lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF with Ollama:
```
ollama run hf.co/lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
```

Unsloth Studio

How to use lhca521/Huihui-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 lhca521/Huihui-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 lhca521/Huihui-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 lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF to start chatting

How to use lhca521/Huihui-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 lhca521/Huihui-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": "lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lhca521/Huihui-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 lhca521/Huihui-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 lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K
```

Lemonade

How to use lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lhca521/Huihui-Kimi-K2.5-BF16-abliterated-GGUF:Q2_K

Run and chat with the model

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

List all available models

lemonade list

Huihui-Kimi-K2.5-BF16-abliterated-GGUF / README.md

lhca521

Duplicate from bakosh/Huihui-Kimi-K2.5-BF16-abliterated-GGUF

e62d646 2 months ago

preview code

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	---
	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](https://huggingface.co/moonshotai/Kimi-K2.5) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).

	## Download and merge

	[Q2_K](https://huggingface.co/huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF/tree/main/Q2_K-GGUF)


	[Q3_K](https://huggingface.co/huihui-ai/Huihui-Kimi-K2.5-BF16-abliterated-GGUF/tree/main/Q3_K-GGUF)

	Use the [llama.cpp](https://github.com/ggml-org/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.


	### Donation

	If you like it, please click 'like' and follow us for more updates.
	You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai.

	##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
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