Instructions to use bartowski/WikiHow-Mistral-Instruct-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/WikiHow-Mistral-Instruct-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/WikiHow-Mistral-Instruct-7B-GGUF",
	filename="WikiHow-Mistral-Instruct-7B-IQ3_M.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 bartowski/WikiHow-Mistral-Instruct-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 bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/WikiHow-Mistral-Instruct-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 bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/WikiHow-Mistral-Instruct-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 bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/WikiHow-Mistral-Instruct-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 bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/WikiHow-Mistral-Instruct-7B-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M

Ollama
How to use bartowski/WikiHow-Mistral-Instruct-7B-GGUF with Ollama:
```
ollama run hf.co/bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use bartowski/WikiHow-Mistral-Instruct-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 bartowski/WikiHow-Mistral-Instruct-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 bartowski/WikiHow-Mistral-Instruct-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 bartowski/WikiHow-Mistral-Instruct-7B-GGUF to start chatting

Docker Model Runner
How to use bartowski/WikiHow-Mistral-Instruct-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/WikiHow-Mistral-Instruct-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/WikiHow-Mistral-Instruct-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WikiHow-Mistral-Instruct-7B-GGUF-Q4_K_M

List all available models

lemonade list

Llamacpp Quantizations of WikiHow-Mistral-Instruct-7B

Using llama.cpp release b2440 for quantization.

Original model: https://huggingface.co/ajibawa-2023/WikiHow-Mistral-Instruct-7B

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Description
WikiHow-Mistral-Instruct-7B-Q8_0.gguf	Q8_0	7.69GB	Extremely high quality, generally unneeded but max available quant.
WikiHow-Mistral-Instruct-7B-Q6_K.gguf	Q6_K	5.94GB	Very high quality, near perfect, recommended.
WikiHow-Mistral-Instruct-7B-Q5_K_M.gguf	Q5_K_M	5.13GB	High quality, very usable.
WikiHow-Mistral-Instruct-7B-Q5_K_S.gguf	Q5_K_S	4.99GB	High quality, very usable.
WikiHow-Mistral-Instruct-7B-Q5_0.gguf	Q5_0	4.99GB	High quality, older format, generally not recommended.
WikiHow-Mistral-Instruct-7B-Q4_K_M.gguf	Q4_K_M	4.36GB	Good quality, similar to 4.25 bpw.
WikiHow-Mistral-Instruct-7B-Q4_K_S.gguf	Q4_K_S	4.14GB	Slightly lower quality with small space savings.
WikiHow-Mistral-Instruct-7B-IQ4_NL.gguf	IQ4_NL	4.15GB	Good quality, similar to Q4_K_S, new method of quanting,
WikiHow-Mistral-Instruct-7B-IQ4_XS.gguf	IQ4_XS	3.94GB	Decent quality, new method with similar performance to Q4.
WikiHow-Mistral-Instruct-7B-Q4_0.gguf	Q4_0	4.10GB	Decent quality, older format, generally not recommended.
WikiHow-Mistral-Instruct-7B-IQ3_M.gguf	IQ3_M	3.28GB	Medium-low quality, new method with decent performance.
WikiHow-Mistral-Instruct-7B-IQ3_S.gguf	IQ3_S	3.18GB	Lower quality, new method with decent performance, recommended over Q3 quants.
WikiHow-Mistral-Instruct-7B-Q3_K_L.gguf	Q3_K_L	3.82GB	Lower quality but usable, good for low RAM availability.
WikiHow-Mistral-Instruct-7B-Q3_K_M.gguf	Q3_K_M	3.51GB	Even lower quality.
WikiHow-Mistral-Instruct-7B-Q3_K_S.gguf	Q3_K_S	3.16GB	Low quality, not recommended.
WikiHow-Mistral-Instruct-7B-Q2_K.gguf	Q2_K	2.71GB	Extremely low quality, not recommended.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit