Instructions to use electron-rare/mistral-large-opus-Q4_K_M-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use electron-rare/mistral-large-opus-Q4_K_M-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="electron-rare/mistral-large-opus-Q4_K_M-gguf")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("electron-rare/mistral-large-opus-Q4_K_M-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use electron-rare/mistral-large-opus-Q4_K_M-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 electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf electron-rare/mistral-large-opus-Q4_K_M-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 electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf electron-rare/mistral-large-opus-Q4_K_M-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 electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf electron-rare/mistral-large-opus-Q4_K_M-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 electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M

Use Docker

docker model run hf.co/electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use electron-rare/mistral-large-opus-Q4_K_M-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "electron-rare/mistral-large-opus-Q4_K_M-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": "electron-rare/mistral-large-opus-Q4_K_M-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M

SGLang

How to use electron-rare/mistral-large-opus-Q4_K_M-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "electron-rare/mistral-large-opus-Q4_K_M-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electron-rare/mistral-large-opus-Q4_K_M-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "electron-rare/mistral-large-opus-Q4_K_M-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electron-rare/mistral-large-opus-Q4_K_M-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use electron-rare/mistral-large-opus-Q4_K_M-gguf with Ollama:
```
ollama run hf.co/electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use electron-rare/mistral-large-opus-Q4_K_M-gguf with Docker Model Runner:
```
docker model run hf.co/electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M
```

Lemonade

How to use electron-rare/mistral-large-opus-Q4_K_M-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull electron-rare/mistral-large-opus-Q4_K_M-gguf:Q4_K_M

Run and chat with the model

lemonade run user.mistral-large-opus-Q4_K_M-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

Mistral-Large Opus Q4_K_M GGUF

Q4_K_M quantization of a Mistral-Large fine-tune (codename "opus") for llama.cpp inference.

Base model: Mistral-Large-Instruct-2411 (123B)
Fine-tune: internal "opus" curriculum (cross-domain SFT, see Hypneum Lab artefacts)
Quantization: Q4_K_M
Use case: high-capacity local inference where a generalist 100B+ class is needed

License

Inherits Mistral Research License from the base model. Non-commercial use only. For commercial deployment, route via the ailiance gateway which uses the Apache-2.0 Apertus 70B or Devstral 24B alternatives.

Provenance

Quantized from the SFT output adapters merged onto Mistral-Large-Instruct-2411. Training recipe documented in the Hypneum Lab kiki-flow-research repo.

Org context

This repo is part of electron-rare, the legacy research org. Production ailiance artifacts now live at Ailiance-fr (Apache-2.0 only).

Downloads last month: 48

GGUF

Model size

123B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for electron-rare/mistral-large-opus-Q4_K_M-gguf

Base model

mistralai/Mistral-Large-Instruct-2411

Quantized

(34)

this model