Instructions to use qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF",
	filename="mistral-nemo-instruct-2407-bf16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-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 qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-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 qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-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 qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-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": "qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M

Ollama
How to use qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF with Ollama:
```
ollama run hf.co/qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M
```

Unsloth Studio

How to use qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-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 qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-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 qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M
```

Lemonade

How to use qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Nemo-Instruct-2407-iMat-GGUF-Q4_K_M

List all available models

lemonade list

Quant Infos

needs #8604 & latest master with tekken tokenizer fixes applied
quants done with an importance matrix for improved quantization loss
Quantized ggufs & imatrix from hf bf16, through bf16. safetensors bf16 -> gguf bf16 -> quant for optimal quant loss.
Wide coverage of different gguf quant types from Q_8_0 down to IQ1_S
- experimental custom quant types
  - _L with --output-tensor-type f16 --token-embedding-type f16 (same as bartowski's)

Imatrix generated with this multi-purpose dataset by bartowski.

./imatrix -m $model_name-bf16.gguf -f calibration_datav3.txt -o $model_name.imatrix

Original Model Card:

TODO

Downloads last month: 187

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

View +2 variants

Model tree for qwp4w3hyb/Mistral-Nemo-Instruct-2407-iMat-GGUF

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

mistralai/Mistral-Nemo-Instruct-2407

Quantized

(174)

this model