Instructions to use ZeroAgency/Zero-Mistral-24B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZeroAgency/Zero-Mistral-24B-gguf with Transformers:

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

pipe = pipeline("text-generation", model="ZeroAgency/Zero-Mistral-24B-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ZeroAgency/Zero-Mistral-24B-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ZeroAgency/Zero-Mistral-24B-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 ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZeroAgency/Zero-Mistral-24B-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 ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZeroAgency/Zero-Mistral-24B-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 ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZeroAgency/Zero-Mistral-24B-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 ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M

Use Docker

docker model run hf.co/ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use ZeroAgency/Zero-Mistral-24B-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeroAgency/Zero-Mistral-24B-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": "ZeroAgency/Zero-Mistral-24B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M

SGLang

How to use ZeroAgency/Zero-Mistral-24B-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 "ZeroAgency/Zero-Mistral-24B-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": "ZeroAgency/Zero-Mistral-24B-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 "ZeroAgency/Zero-Mistral-24B-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": "ZeroAgency/Zero-Mistral-24B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ZeroAgency/Zero-Mistral-24B-gguf with Ollama:
```
ollama run hf.co/ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M
```

Unsloth Studio

How to use ZeroAgency/Zero-Mistral-24B-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 ZeroAgency/Zero-Mistral-24B-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 ZeroAgency/Zero-Mistral-24B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZeroAgency/Zero-Mistral-24B-gguf to start chatting

Docker Model Runner
How to use ZeroAgency/Zero-Mistral-24B-gguf with Docker Model Runner:
```
docker model run hf.co/ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M
```

Lemonade

How to use ZeroAgency/Zero-Mistral-24B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZeroAgency/Zero-Mistral-24B-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Zero-Mistral-24B-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

Model Card for Zero-Mistral

This is a GGUF version of ZeroAgency/Zero-Mistral-24B.

All quants made with llama.cpp version b5083.

Quants available:

BF16
F16
IQ4_NL
IQ4_NL_L - same as above but with --leave-output-tensors
IQ4_XS
IQ4_XS_L - same as above but with --leave-output-tensors
Q4_K_M
Q4_K_M_L - same as above but with --leave-output-tensors
Q6_K
Q6_K_L - same as above but with --leave-output-tensors
Q8_0 - quantized from bf16 gguf
Q8_0-direct - direct convertation from hf
Q8_0_L - quantized from bf16 but with --leave-output-tensors

Downloads last month: 310

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

Model tree for ZeroAgency/Zero-Mistral-24B-gguf

Base model

mistralai/Mistral-Small-3.1-24B-Base-2503

Finetuned

mistralai/Mistral-Small-3.1-24B-Instruct-2503

Finetuned

ZeroAgency/Zero-Mistral-24B

Quantized

(3)

this model

Dataset used to train ZeroAgency/Zero-Mistral-24B-gguf

Collection including ZeroAgency/Zero-Mistral-24B-gguf

Zero-Mistral 24B

Collection

Zero-Mistral-24B is our latest Russian language adapted version of Mistral-Small-3.1 • 2 items • Updated Apr 24, 2025 • 1