Instructions to use InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF",
	filename="mistral-7B-v0.2-iMat-IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use InferenceIllusionist/Mistral-7B-v0.2-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 InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf InferenceIllusionist/Mistral-7B-v0.2-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 InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf InferenceIllusionist/Mistral-7B-v0.2-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-7B-v0.2-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf InferenceIllusionist/Mistral-7B-v0.2-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-7B-v0.2-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF with Ollama:
```
ollama run hf.co/InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF:Q4_K_M
```

Unsloth Studio new

How to use InferenceIllusionist/Mistral-7B-v0.2-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 InferenceIllusionist/Mistral-7B-v0.2-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 InferenceIllusionist/Mistral-7B-v0.2-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 InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF to start chatting

Docker Model Runner
How to use InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF:Q4_K_M
```

Lemonade

How to use InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-v0.2-iMat-GGUF-Q4_K_M

List all available models

lemonade list

Mistral 7B v0.2 iMat GGUF

Not to be confused with Mistral 7B Instruct v0.2 (this is the latest release from 3/23)

Mistral 7B v0.2 iMat GGUF quantized from fp16 with love.

iMat dat file created using groups_merged.txt
Not sure what to expect from this model by itself but uploading to repo in case anyone is curious like me

Legacy quants (i.e. Q8, Q5_K_M) in this repo have all been enhanced with importance matrix calculation. These quants show improved KL-Divergence over their static counterparts.

All files have been tested for your safety and convenience. No need to clone the entire repo, just pick the quant that's right for you.

For more information on latest iMatrix quants see this PR - https://github.com/ggerganov/llama.cpp/pull/5747

Downloads last month: 40

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Collection including InferenceIllusionist/Mistral-7B-v0.2-iMat-GGUF

GGUFs

Collection

I take requests, feel free to drop me a line in the community posts • 31 items • Updated Apr 28 • 3