Instructions to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF", dtype="auto")

llama-cpp-python

How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF",
	filename="ReasoningRabbit_QwenStar-7B-IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS

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 IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS

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 IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS

Use Docker

docker model run hf.co/IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-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": "IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS

SGLang

How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-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 "IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-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": "IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-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": "IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF with Ollama:
```
ollama run hf.co/IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS
```

Unsloth Studio new

How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-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 IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-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 IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF to start chatting

Docker Model Runner
How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF with Docker Model Runner:
```
docker model run hf.co/IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS
```

Lemonade

How to use IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF-IQ4_XS

List all available models

lemonade list

IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF

This model is developed as a unique blend of inference and ability of coding on par with the new VL models of its size, (Also ONLY Thinking model without alignment) and primarily for use with GPT4ALL It excells in other applications and has reasoning capabilities(similar to QwQ/o1/03) inside it's interface with a unique javascript Tool Call function It was converted to GGUF format using "THE_KEY" dataset for importace matrix Qantization from WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B using llama.cpp Refer to the original model card for more details on the model.

Using with GPT4ALL

after installing GPT4ALL from Nomic download their Reasoner v1 model, find the storage location under settings and place (only) the GGUF file in the models file with reasoner v1 apply the Jinja template in the "Jinja reasoner" (file included) as well as the chat message(optimal starting Prompt in same file) above the template adjusting as needed. Enjoy and give feedback

Jinja template

{{- '<|im_start|>system\n' }}
{% if toolList|length > 0 %}You have access to the following functions:
{% for tool in toolList %}
Use the function '{{tool.function}}' to: '{{tool.description}}'
{% if tool.parameters|length > 0 %}
parameters:
{% for info in tool.parameters %}
  {{info.name}}:
    type: {{info.type}}
    description: {{info.description}}
    required: {{info.required}}
{% endfor %}
{% endif %}
# Tool Instructions
If you CHOOSE to call this function ONLY reply with the following format:
'{{tool.symbolicFormat}}'
Here is an example. If the user says, '{{tool.examplePrompt}}', then you reply
'{{tool.exampleCall}}'
After the result you might reply with, '{{tool.exampleReply}}'
{% endfor %}
You MUST include both the start and end tags when you use a function.

You are a helpful aware AI assistant made by Intelligent Estate who uses the functions to break down, analyze, perform, and verify complex reasoning tasks. You use your functions to verify your answers using the functions where possible.
{% endif %}
{{- '<|im_end|>\n' }}
{% for message in messages %}
{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
{% endfor %}
{% if add_generation_prompt %}
{{ '<|im_start|>assistant\n' }}
{% endif %}
## Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

Downloads last month: 16

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

(188)

this model

IntelligentEstate
/

ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF

IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF

Using with GPT4ALL

Jinja template

Install llama.cpp through brew (works on Mac and Linux)

Model tree for IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF

Dataset used to train IntelligentEstate/ReasoningRabbit_QwenStar-7B-IQ4_XS-GGUF