Instructions to use QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF",
	filename="Mistral-Nemo-Japanese-Instruct-2408.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-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 QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-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 QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-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 QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-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": "QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-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 QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-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 QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Nemo-Japanese-Instruct-2408-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF

This is quantized version of cyberagent/Mistral-Nemo-Japanese-Instruct-2408 created using llama.cpp

Original Model Card

Mistral-Nemo-Japanese-Instruct-2408

Model Description

This is a Japanese continually pre-trained model based on mistralai/Mistral-Nemo-Instruct-2407.

Usage

Make sure to update your transformers installation via pip install --upgrade transformers.

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model = AutoModelForCausalLM.from_pretrained("cyberagent/Mistral-Nemo-Japanese-Instruct-2408", device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("cyberagent/Mistral-Nemo-Japanese-Instruct-2408")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

messages = [
    {"role": "system", "content": "あなたは親切なAIアシスタントです。"},
    {"role": "user", "content": "AIによって私たちの暮らしはどのように変わりますか？"}
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
output_ids = model.generate(input_ids,
                            max_new_tokens=1024,
                            temperature=0.5,
                            streamer=streamer)

Prompt Format

ChatML Format

<s><|im_start|>system
あなたは親切なAIアシスタントです。<|im_end|>
<|im_start|>user
AIによって私たちの暮らしはどのように変わりますか？<|im_end|>
<|im_start|>assistant

License

Apache-2.0

Author

Ryosuke Ishigami

How to cite

@misc{cyberagent-mistral-nemo-japanese-instruct-2408,
      title={Mistral-Nemo-Japanese-Instruct-2408},
      url={https://huggingface.co/cyberagent/Mistral-Nemo-Japanese-Instruct-2408},
      author={Ryosuke Ishigami},
      year={2024},
}

Downloads last month: 408

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Mistral-Nemo-Japanese-Instruct-2408-GGUF

Base model

mistralai/Mistral-Nemo-Base-2407

Quantized

(76)

this model