Instructions to use ZeroWw/Mistral-Nemo-Instruct-2407-SILLY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZeroWw/Mistral-Nemo-Instruct-2407-SILLY with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZeroWw/Mistral-Nemo-Instruct-2407-SILLY",
	filename="Mistral-Nemo-Instruct-2407.fq8.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 ZeroWw/Mistral-Nemo-Instruct-2407-SILLY with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ZeroWw/Mistral-Nemo-Instruct-2407-SILLY
# Run inference directly in the terminal:
llama-cli -hf ZeroWw/Mistral-Nemo-Instruct-2407-SILLY

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ZeroWw/Mistral-Nemo-Instruct-2407-SILLY
# Run inference directly in the terminal:
llama-cli -hf ZeroWw/Mistral-Nemo-Instruct-2407-SILLY

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 ZeroWw/Mistral-Nemo-Instruct-2407-SILLY
# Run inference directly in the terminal:
./llama-cli -hf ZeroWw/Mistral-Nemo-Instruct-2407-SILLY

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 ZeroWw/Mistral-Nemo-Instruct-2407-SILLY
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZeroWw/Mistral-Nemo-Instruct-2407-SILLY

Use Docker

docker model run hf.co/ZeroWw/Mistral-Nemo-Instruct-2407-SILLY

LM Studio
Jan

vLLM

How to use ZeroWw/Mistral-Nemo-Instruct-2407-SILLY with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeroWw/Mistral-Nemo-Instruct-2407-SILLY"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeroWw/Mistral-Nemo-Instruct-2407-SILLY",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZeroWw/Mistral-Nemo-Instruct-2407-SILLY

Ollama
How to use ZeroWw/Mistral-Nemo-Instruct-2407-SILLY with Ollama:
```
ollama run hf.co/ZeroWw/Mistral-Nemo-Instruct-2407-SILLY
```

Unsloth Studio new

How to use ZeroWw/Mistral-Nemo-Instruct-2407-SILLY 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 ZeroWw/Mistral-Nemo-Instruct-2407-SILLY 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 ZeroWw/Mistral-Nemo-Instruct-2407-SILLY to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZeroWw/Mistral-Nemo-Instruct-2407-SILLY to start chatting

Docker Model Runner
How to use ZeroWw/Mistral-Nemo-Instruct-2407-SILLY with Docker Model Runner:
```
docker model run hf.co/ZeroWw/Mistral-Nemo-Instruct-2407-SILLY
```

Lemonade

How to use ZeroWw/Mistral-Nemo-Instruct-2407-SILLY with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZeroWw/Mistral-Nemo-Instruct-2407-SILLY

Run and chat with the model

lemonade run user.Mistral-Nemo-Instruct-2407-SILLY-{{QUANT_TAG}}

List all available models

lemonade list

ZeroWw 'SILLY' version. The original model has been quantized (fq8 version) and a percentage of it's tensors have been modified adding some noise.

Full colab: https://colab.research.google.com/drive/1a7seagBzu5l3k3FL4SFk0YJocl7nsDJw?usp=sharing

Fast colab: https://colab.research.google.com/drive/1SDD7ox21di_82Y9v68AUoy0PhkxwBVvN?usp=sharing

Original reddit post: https://www.reddit.com/r/LocalLLaMA/comments/1ec0s8p/i_made_a_silly_test/

I created a program to randomize the weights of a model. The program has 2 parameters: the percentage of weights to modify and the percentage of the original value to randmly apply to each weight.

At the end I check the resulting GGUF file for binary differences. In this example I set to modify 100% of the weights of the original model by a maximum of 15% deviation.

Since the deviation is calculated on the F32 weights, when quantized to Q8_0 this changes. So, in the end I got a file that compared to the original has:

Bytes Difference percentage: 73.04%

Average value divergence: 2.98%

The cool thing is that chatting with the model I see no apparent difference and the model still works nicely as the original.

Since I am running everything on CPU, I could not run perplexity scores or anything computing intensive.

As a small test, I asked the model a few questions (like the history of the roman empire) and then fact check its answer using a big model. No errors were detected.

Update: all procedure tested and created on COLAB.

Created on: Sat Jul 27, 00:18:42

Downloads last month: 2

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants