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

Libraries

How to use second-state/Mistral-Large-Instruct-2407-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="second-state/Mistral-Large-Instruct-2407-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("second-state/Mistral-Large-Instruct-2407-GGUF")
model = AutoModelForCausalLM.from_pretrained("second-state/Mistral-Large-Instruct-2407-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

docker model run hf.co/second-state/Mistral-Large-Instruct-2407-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/Mistral-Large-Instruct-2407-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/Mistral-Large-Instruct-2407-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": "second-state/Mistral-Large-Instruct-2407-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/Mistral-Large-Instruct-2407-GGUF:Q4_K_M

SGLang

How to use second-state/Mistral-Large-Instruct-2407-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 "second-state/Mistral-Large-Instruct-2407-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": "second-state/Mistral-Large-Instruct-2407-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 "second-state/Mistral-Large-Instruct-2407-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": "second-state/Mistral-Large-Instruct-2407-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use second-state/Mistral-Large-Instruct-2407-GGUF with Ollama:
```
ollama run hf.co/second-state/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/Mistral-Large-Instruct-2407-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 second-state/Mistral-Large-Instruct-2407-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 second-state/Mistral-Large-Instruct-2407-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/Mistral-Large-Instruct-2407-GGUF to start chatting

Docker Model Runner
How to use second-state/Mistral-Large-Instruct-2407-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/Mistral-Large-Instruct-2407-GGUF:Q4_K_M
```

Lemonade

How to use second-state/Mistral-Large-Instruct-2407-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

apepkuss79 commited on Aug 2, 2024

Commit

570d2f8

verified ·

1 Parent(s): bdba70b

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +22 -7

README.md CHANGED Viewed

@@ -82,13 +82,28 @@ language:
 | [Mistral-Large-Instruct-2407-Q3_K_M-00002-of-00002.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q3_K_M-00002-of-00002.gguf) | Q3_K_M | 3 | 29.2 GB| very small, high quality loss |
 | [Mistral-Large-Instruct-2407-Q3_K_S-00001-of-00002.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q3_K_S-00001-of-00002.gguf) | Q3_K_S | 3 | 29.9 GB| very small, high quality loss |
 | [Mistral-Large-Instruct-2407-Q3_K_S-00002-of-00002.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q3_K_S-00002-of-00002.gguf) | Q3_K_S | 3 | 29.2 GB| very small, high quality loss |
-| [Mistral-Large-Instruct-2407-Q4_0.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_0.gguf)     | Q4_0   | 4 | 4.11 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
-| [Mistral-Large-Instruct-2407-Q4_K_M.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| medium, balanced quality - recommended |
-| [Mistral-Large-Instruct-2407-Q4_K_S.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| small, greater quality loss |
-| [Mistral-Large-Instruct-2407-Q5_0.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_0.gguf)     | Q5_0   | 5 | 5 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
-| [Mistral-Large-Instruct-2407-Q5_K_M.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_K_M.gguf) | Q5_K_M | 5 | 5.14 GB| large, very low quality loss - recommended |
-| [Mistral-Large-Instruct-2407-Q5_K_S.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_K_S.gguf) | Q5_K_S | 5 | 5 GB| large, low quality loss - recommended |
-| [Mistral-Large-Instruct-2407-Q6_K.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q6_K.gguf)     | Q6_K   | 6 | 5.95 GB| very large, extremely low quality loss |
 | [Mistral-Large-Instruct-2407-Q8_0.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q8_0.gguf)     | Q8_0   | 8 | 7.7 GB| very large, extremely low quality loss - not recommended |
 | [Mistral-Large-Instruct-2407-f16.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-f16.gguf)     | f16   | 16 | 14.5 GB|  |

 | [Mistral-Large-Instruct-2407-Q3_K_M-00002-of-00002.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q3_K_M-00002-of-00002.gguf) | Q3_K_M | 3 | 29.2 GB| very small, high quality loss |
 | [Mistral-Large-Instruct-2407-Q3_K_S-00001-of-00002.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q3_K_S-00001-of-00002.gguf) | Q3_K_S | 3 | 29.9 GB| very small, high quality loss |
 | [Mistral-Large-Instruct-2407-Q3_K_S-00002-of-00002.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q3_K_S-00002-of-00002.gguf) | Q3_K_S | 3 | 29.2 GB| very small, high quality loss |
+| [Mistral-Large-Instruct-2407-Q4_0-00001-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_0-00001-of-00003.gguf)     | Q4_0   | 4 | 30.0 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Mistral-Large-Instruct-2407-Q4_0-00002-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_0-00002-of-00003.gguf)     | Q4_0   | 4 | 30.0 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Mistral-Large-Instruct-2407-Q4_0-00003-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_0-00003-of-00003.gguf)     | Q4_0   | 4 | 9.09 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Mistral-Large-Instruct-2407-Q4_K_M-00001-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_K_M-00001-of-00003.gguf) | Q4_K_M | 4 | 30.0 GB| medium, balanced quality - recommended |
+| [Mistral-Large-Instruct-2407-Q4_K_M-00002-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_K_M-00002-of-00003.gguf) | Q4_K_M | 4 | 29.9 GB| medium, balanced quality - recommended |
+| [Mistral-Large-Instruct-2407-Q4_K_M-00003-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_K_M-00003-of-00003.gguf) | Q4_K_M | 4 | 13.3 GB| medium, balanced quality - recommended |
+| [Mistral-Large-Instruct-2407-Q4_K_S-00001-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_K_S-00001-of-00003.gguf) | Q4_K_S | 4 | 29.9 GB| small, greater quality loss |
+| [Mistral-Large-Instruct-2407-Q4_K_S-00002-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_K_S-00002-of-00003.gguf) | Q4_K_S | 4 | 30.0 GB| small, greater quality loss |
+| [Mistral-Large-Instruct-2407-Q4_K_S-00003-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q4_K_S-00003-of-00003.gguf) | Q4_K_S | 4 | 9.67 GB| small, greater quality loss |
+| [Mistral-Large-Instruct-2407-Q5_0-00001-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_0-00001-of-00003.gguf)     | Q5_0   | 5 | 30.0 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
+| [Mistral-Large-Instruct-2407-Q5_0-00002-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_0-00002-of-00003.gguf)     | Q5_0   | 5 | 30.0 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
+| [Mistral-Large-Instruct-2407-Q5_0-00003-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_0-00003-of-00003.gguf)     | Q5_0   | 5 | 24.4 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
+| [Mistral-Large-Instruct-2407-Q5_K_M-00001-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_K_M-00001-of-00003.gguf) | Q5_K_M | 5 | 29.9 GB| large, very low quality loss - recommended |
+| [Mistral-Large-Instruct-2407-Q5_K_M-00002-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_K_M-00002-of-00003.gguf) | Q5_K_M | 5 | 29.7 GB| large, very low quality loss - recommended |
+| [Mistral-Large-Instruct-2407-Q5_K_M-00003-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_K_M-00003-of-00003.gguf) | Q5_K_M | 5 | 26.8 GB| large, very low quality loss - recommended |
+| [Mistral-Large-Instruct-2407-Q5_K_S-00001-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_K_S-00001-of-00003.gguf) | Q5_K_S | 5 | 30.0 GB| large, low quality loss - recommended |
+| [Mistral-Large-Instruct-2407-Q5_K_S-00002-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_K_S-00002-of-00003.gguf) | Q5_K_S | 5 | 30.0 GB| large, low quality loss - recommended |
+| [Mistral-Large-Instruct-2407-Q5_K_S-00003-of-00003.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q5_K_S-00003-of-00003.gguf) | Q5_K_S | 5 | 24.4 GB| large, low quality loss - recommended |
+| [Mistral-Large-Instruct-2407-Q6_K-00001-of-00004.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q6_K-00001-of-00004.gguf)     | Q6_K   | 6 | 29.9 GB| very large, extremely low quality loss |
+| [Mistral-Large-Instruct-2407-Q6_K-00002-of-00004.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q6_K-00002-of-00004.gguf)     | Q6_K   | 6 | 29.8 GB| very large, extremely low quality loss |
+| [Mistral-Large-Instruct-2407-Q6_K-00003-of-00004.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q6_K-00003-of-00004.gguf)     | Q6_K   | 6 | 29.8 GB| very large, extremely low quality loss |
+| [Mistral-Large-Instruct-2407-Q6_K-00004-of-00004.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q6_K-00004-of-00004.gguf)     | Q6_K   | 6 | 11.1 GB| very large, extremely low quality loss |
 | [Mistral-Large-Instruct-2407-Q8_0.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q8_0.gguf)     | Q8_0   | 8 | 7.7 GB| very large, extremely low quality loss - not recommended |
 | [Mistral-Large-Instruct-2407-f16.gguf](https://huggingface.co/second-state/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-f16.gguf)     | f16   | 16 | 14.5 GB|  |