Instructions to use NorwAI/NorwAI-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NorwAI/NorwAI-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="NorwAI/NorwAI-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NorwAI/NorwAI-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("NorwAI/NorwAI-Mistral-7B")

llama-cpp-python

How to use NorwAI/NorwAI-Mistral-7B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NorwAI/NorwAI-Mistral-7B",
	filename="normistral-7b.Q3_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use NorwAI/NorwAI-Mistral-7B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NorwAI/NorwAI-Mistral-7B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NorwAI/NorwAI-Mistral-7B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NorwAI/NorwAI-Mistral-7B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf NorwAI/NorwAI-Mistral-7B: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 NorwAI/NorwAI-Mistral-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NorwAI/NorwAI-Mistral-7B: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 NorwAI/NorwAI-Mistral-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NorwAI/NorwAI-Mistral-7B:Q4_K_M

Use Docker

docker model run hf.co/NorwAI/NorwAI-Mistral-7B:Q4_K_M

LM Studio
Jan

vLLM

How to use NorwAI/NorwAI-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NorwAI/NorwAI-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NorwAI/NorwAI-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NorwAI/NorwAI-Mistral-7B:Q4_K_M

SGLang

How to use NorwAI/NorwAI-Mistral-7B 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 "NorwAI/NorwAI-Mistral-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NorwAI/NorwAI-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "NorwAI/NorwAI-Mistral-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NorwAI/NorwAI-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use NorwAI/NorwAI-Mistral-7B with Ollama:
```
ollama run hf.co/NorwAI/NorwAI-Mistral-7B:Q4_K_M
```

Unsloth Studio new

How to use NorwAI/NorwAI-Mistral-7B 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 NorwAI/NorwAI-Mistral-7B 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 NorwAI/NorwAI-Mistral-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NorwAI/NorwAI-Mistral-7B to start chatting

Docker Model Runner
How to use NorwAI/NorwAI-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/NorwAI/NorwAI-Mistral-7B:Q4_K_M
```

Lemonade

How to use NorwAI/NorwAI-Mistral-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NorwAI/NorwAI-Mistral-7B:Q4_K_M

Run and chat with the model

lemonade run user.NorwAI-Mistral-7B-Q4_K_M

List all available models

lemonade list

How did you preprocess the datasets to remove Copyrighted material?

by Stealcase - opened Jul 7, 2024

Discussion

Stealcase

Jul 7, 2024

Hey, this line is mentioned in the Training Data section:
"The publicly available datasets were preprocessed to filter out texts with copyright issues, and all datasets were preprocessed to remove sensitive information"

I have a couple of questions.

How did you go about doing this?

What metric/filter did you use to determine something with a copyright issue?
Do you have a paper on the training process or the filtering process?
Do you have documentation on exactly what data was used to train this model? VG, NRK and Schibsted are mentioned, which indicates that articles published through these might have been used to train this LLM. Could you clarify what that means?

Before using models such as NorwAI-Mistral-7B it's essential that the associated Copyright and personal data/sensitive data risks are known. The documentation is very vague on these subjects

jlu

Jul 17, 2024

(Disclaimer: Not affiliated with the project)

Do you have documentation on exactly what data was used to train this model? VG, NRK and Schibsted are mentioned, which indicates that articles published through these might have been used to train this LLM. Could you clarify what that means?

The launch announcement (6:15) clarifies that the models are mainly based on the Norwegian Colossal Corpus from the National Library. VG, NRK and Schibsted are listed as collaborators, which I would take to mean they've granted NTNU license to use their archives for the purpose of model training, similar to what the National Library is describing for their Mimir Extended dataset (8:00).

Stealcase

Jul 26, 2024

Hey jlu, I believe you have done a classic mistake of mixing NorwAI and NORA LLM. (Don't worry, you're not the only one)

Though believe me when I say that NORA LLM has their own Copyright problems

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