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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NLPoetic/Mistral-NeMo-Instruct-2407-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_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 NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_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 NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M

Use Docker

docker model run hf.co/NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M

LM Studio
Jan
Ollama
How to use NLPoetic/Mistral-NeMo-Instruct-2407-GGUF with Ollama:
```
ollama run hf.co/NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M
```

Unsloth Studio

How to use NLPoetic/Mistral-NeMo-Instruct-2407-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 NLPoetic/Mistral-NeMo-Instruct-2407-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 NLPoetic/Mistral-NeMo-Instruct-2407-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use NLPoetic/Mistral-NeMo-Instruct-2407-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use NLPoetic/Mistral-NeMo-Instruct-2407-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use NLPoetic/Mistral-NeMo-Instruct-2407-GGUF with Docker Model Runner:
```
docker model run hf.co/NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M
```

Lemonade

How to use NLPoetic/Mistral-NeMo-Instruct-2407-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.Mistral-NeMo-Instruct-2407-GGUF-Q5_K_M

List all available models

lemonade list

Hermes Agent

How to use NLPoetic/Mistral-NeMo-Instruct-2407-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default NLPoetic/Mistral-NeMo-Instruct-2407-GGUF:Q5_K_M

Run Hermes

hermes

Atomic Chat

Quantized Mistral-NeMo-Instruct-2407 versions for Prompt Sensitivity Blog

This repository contains four quantized versions of Mistral-NeMo-Instruct-2407, created using llama.cpp. The goal was to examine how different quantization methods affect prompt sensitivity with sentiment classification tasks.

Quantization Details

Models were quantized using llama.cpp (release b3922). The imatrix versions used an imatrix.dat file created from Bartowski's calibration dataset, mentioned here.

Models

Filename	Size	Description
Mistral-NeMo-12B-Instruct-2407-Q8_0.gguf	13 GB	8-bit default quantization
Mistral-NeMo-12B-Instruct-2407-Q5_0.gguf	8.73 GB	5-bit default quantization
Mistral-NeMo-12B-Instruct-2407-imatrix-Q8_0.gguf	13 GB	8-bit with imatrix quantization
Mistral-NeMo-12B-Instruct-2407-imatrix-Q5_0.gguf	8.73 GB	5-bit with imatrix quantization

I've also included the imatrix.dat (7.05 MB) file used to create the imatrix-quantized versions.

Findings

Prompt sensitivity was seen specifically in 5-bit models using imatrix quantization, but not with default llama.cpp quantization settings. Prompt sensitivity was not observed in 8-bit models with either quantization method.

For further discussion please see my accompanying blog post.

Author

Simon Barnes

Downloads last month: 87

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

5-bit

8-bit

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Model tree for NLPoetic/Mistral-NeMo-Instruct-2407-GGUF

Base model

mistralai/Mistral-Nemo-Base-2407

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mistralai/Mistral-Nemo-Instruct-2407

Quantized

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