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

Libraries

How to use InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-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 InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf InferenceIllusionist/Mistral-Large-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 serve -hf InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf InferenceIllusionist/Mistral-Large-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 InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf InferenceIllusionist/Mistral-Large-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 InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF with Ollama:
```
ollama run hf.co/InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF:Q4_K_M
```

Lemonade

How to use InferenceIllusionist/Mistral-Large-Instruct-2407-iMat-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

Mistral-Large-Instruct-2407-iMat-GGUF

Important Note: Inferencing in llama.cpp has now been merged in PR #8604. Please ensure you are on release b3438 or newer. Text-generation-web-ui (Ooba) is also working as of 7/23. Official support for Kobold.cpp is still pending.

Quantized from Mistral-Large-Instruct-2407 123B fp16

Weighted quantizations were creating using fp16 GGUF and groups_merged.txt in 105 chunks and n_ctx=512
For a brief rundown of iMatrix quant performance please see this PR
All quants are verified working prior to uploading to repo for your safety and convenience

KL-Divergence Reference Chart (Click on image to view in full size)

Quant-specific Tips:

If you are getting a cudaMalloc failed: out of memory error, try passing an argument for lower context in llama.cpp, e.g. for 8k: -c 8192

If you have all ampere generation or newer cards, you can use flash attention like so: -fa

Provided Flash Attention is enabled you can also use quantized cache to save on VRAM e.g. for 8-bit: -ctk q8_0 -ctv q8_0

Files split with llama.cpp's gguf-split. No need to manually combine files - just download all files for a specific quant size and load the first file (labeled "00001-")

Original model card can be found here

Downloads last month: 719

GGUF

Model size

123B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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