Instructions to use Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-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 Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-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 Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L
# Run inference directly in the terminal:
llama cli -hf Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L
# Run inference directly in the terminal:
llama cli -hf Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L

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 Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L
# Run inference directly in the terminal:
./llama-cli -hf Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L

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 Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L

Use Docker

docker model run hf.co/Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L

LM Studio
Jan
Ollama
How to use Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF with Ollama:
```
ollama run hf.co/Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L
```
Unsloth Desktop
Docker Model Runner
How to use Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF with Docker Model Runner:
```
docker model run hf.co/Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L
```

Lemonade

How to use Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF:Q6_K_L

Run and chat with the model

lemonade run user.Mistral-Large-Instruct-2411-Q6_K_L-GGUF-Q6_K_L

List all available models

lemonade list

Atomic Chat

Static quantization of Mistral-Large-Instruct-2411

File	Notes
PART 1 PART 2 PART 3	Q6_K with token embedding, output, and some other tensors quantized to Q8_0 6.65 bpw ~1.3% increase in size relative to Q6_K Quantized from F32

Downloads last month: 13

GGUF

Model size

123B params

Architecture

llama

Hardware compatibility

6-bit

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Model tree for Valeciela/Mistral-Large-Instruct-2411-Q6_K_L-GGUF

Base model

mistralai/Mistral-Large-Instruct-2411

Quantized

(34)

this model