Instructions to use grimjim/madwind-wizard-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/madwind-wizard-7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/madwind-wizard-7B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("grimjim/madwind-wizard-7B-GGUF", dtype="auto")

llama-cpp-python

How to use grimjim/madwind-wizard-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="grimjim/madwind-wizard-7B-GGUF",
	filename="madwind-wizard-7B.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use grimjim/madwind-wizard-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf grimjim/madwind-wizard-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf grimjim/madwind-wizard-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf grimjim/madwind-wizard-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf grimjim/madwind-wizard-7B-GGUF: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 grimjim/madwind-wizard-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grimjim/madwind-wizard-7B-GGUF: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 grimjim/madwind-wizard-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grimjim/madwind-wizard-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grimjim/madwind-wizard-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use grimjim/madwind-wizard-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/madwind-wizard-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/madwind-wizard-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/madwind-wizard-7B-GGUF:Q4_K_M

SGLang

How to use grimjim/madwind-wizard-7B-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 "grimjim/madwind-wizard-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/madwind-wizard-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "grimjim/madwind-wizard-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/madwind-wizard-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use grimjim/madwind-wizard-7B-GGUF with Ollama:
```
ollama run hf.co/grimjim/madwind-wizard-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use grimjim/madwind-wizard-7B-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 grimjim/madwind-wizard-7B-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 grimjim/madwind-wizard-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use grimjim/madwind-wizard-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/grimjim/madwind-wizard-7B-GGUF:Q4_K_M
```

Lemonade

How to use grimjim/madwind-wizard-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grimjim/madwind-wizard-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.madwind-wizard-7B-GGUF-Q4_K_M

List all available models

lemonade list

madwind-wizard-7B-GGUF

This is a merge of pre-trained 7B language models created using mergekit.

The intended goal of this merge was to combine the 32K context window of Mistral v0.2 base with the richness and strength of the Zephyr Beta and WizardLM 2 models. This was a mixed-precision merge, promoting Mistral v0.2 base from fp16 to bf16.

The result can be used for text generation. Note that Zephyr Beta training removed in-built alignment from datasets, resulting in a model more likely to generate problematic text when prompted. This merge appears to have inherited that feature.

Full weights: grimjim/madwind-wizard-7B
GGUF quants: grimjim/madwind-wizard-7B-GGUF

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
    - model: alpindale/Mistral-7B-v0.2-hf
      layer_range: [0,32]
    - model: grimjim/zephyr-beta-wizardLM-2-merge-7B
      layer_range: [0,32]
merge_method: slerp
base_model: alpindale/Mistral-7B-v0.2-hf
parameters:
  t:
    - value: 0.5
dtype: bfloat16

Downloads last month: 20

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for grimjim/madwind-wizard-7B-GGUF

Base model

grimjim/madwind-wizard-7B

Quantized

(1)

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Collection including grimjim/madwind-wizard-7B-GGUF

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Collection

Select models helpfully quantized by others as well as myself • 60 items • Updated Mar 2 • 2