Instructions to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M",
	filename="qwen2.5-abliterated-v2-q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

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 koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

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 koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

LM Studio
Jan
Ollama
How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with Ollama:
```
ollama run hf.co/koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M
```

Unsloth Studio

How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M 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 koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M 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 koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M to start chatting

How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

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": "koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

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 koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with Docker Model Runner:
```
docker model run hf.co/koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M
```

Lemonade

How to use koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-7b-instruct-abliterated-v2-q4_K_M-Q4_K_M

List all available models

lemonade list

Qwen2.5-7B-Instruct-Abliterated-v2 Q4_K_M

This is a Q4_K_M quantized version of the huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2 model.

Model Details

Base Model: huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2
Quantization: Q4_K_M (4-bit quantization with K-quantization)
File Size: ~4.36 GB
VRAM Usage: ~4.5 GB
Format: GGUF

Usage

With Ollama

# Download the GGUF file
# Then create a Modelfile:
cat > Modelfile << EOF
FROM ./qwen2.5-abliterated-v2-q4_K_M.gguf
TEMPLATE "{{{{ if .System }}}}<|im_start|>system
{{{{ .System }}}}<|im_end|>
{{{{ end }}}}{{{{ if .Prompt }}}}<|im_start|>user
{{{{ .Prompt }}}}<|im_end|>
{{{{ end }}}}<|im_start|>assistant
"
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
EOF

# Import into Ollama
ollama create qwen2.5-abliterated-v2-q4 -f Modelfile

With llama.cpp

# Download the GGUF file and use with llama.cpp
./llama-cli -m qwen2.5-abliterated-v2-q4_K_M.gguf -p "Your prompt here"

Quantization Details

Original Size: 14.19 GB (FP16)
Quantized Size: 4.36 GB (Q4_K_M)
Reduction: ~69.3%
Quantization Method: llama.cpp Q4_K_M

Performance

This quantized model maintains high quality while using significantly less VRAM:

Full precision: ~8 GB VRAM
Q4_K_M: ~4.5 GB VRAM

Credits

Base model: huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2
Quantization tool: llama.cpp

Downloads last month: 43

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for koorbmeh/qwen2.5-7b-instruct-abliterated-v2-q4_K_M

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2

Quantized

(13)

this model