Instructions to use norallm/norbloom-7b-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use norallm/norbloom-7b-scratch with Transformers:

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

pipe = pipeline("text-generation", model="norallm/norbloom-7b-scratch")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("norallm/norbloom-7b-scratch")
model = AutoModel.from_pretrained("norallm/norbloom-7b-scratch", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use norallm/norbloom-7b-scratch 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 norallm/norbloom-7b-scratch:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf norallm/norbloom-7b-scratch:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf norallm/norbloom-7b-scratch:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf norallm/norbloom-7b-scratch: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 norallm/norbloom-7b-scratch:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf norallm/norbloom-7b-scratch: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 norallm/norbloom-7b-scratch:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf norallm/norbloom-7b-scratch:Q4_K_M

Use Docker

docker model run hf.co/norallm/norbloom-7b-scratch:Q4_K_M

LM Studio
Jan

vLLM

How to use norallm/norbloom-7b-scratch with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/norallm/norbloom-7b-scratch:Q4_K_M

SGLang

How to use norallm/norbloom-7b-scratch 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 "norallm/norbloom-7b-scratch" \
    --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": "norallm/norbloom-7b-scratch",
		"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 "norallm/norbloom-7b-scratch" \
        --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": "norallm/norbloom-7b-scratch",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use norallm/norbloom-7b-scratch with Ollama:
```
ollama run hf.co/norallm/norbloom-7b-scratch:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use norallm/norbloom-7b-scratch with Docker Model Runner:
```
docker model run hf.co/norallm/norbloom-7b-scratch:Q4_K_M
```

Lemonade

How to use norallm/norbloom-7b-scratch with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull norallm/norbloom-7b-scratch:Q4_K_M

Run and chat with the model

lemonade run user.norbloom-7b-scratch-Q4_K_M

List all available models

lemonade list

Atomic Chat

oepen commited on Feb 11, 2024

Commit

48e5e27

verified ·

1 Parent(s): 02f80e8

typo

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README.md CHANGED Viewed

@@ -22,11 +22,11 @@ datasets:
 NorBLOOM-7b-scratch is a large Norwegian language model pretrained from scratch on a total of 260 billion subword tokens (using six repetitions of open Norwegian texts).
-This model is a part of the NORA-LLM family developed in collaboration between [the Language Technology Group at the University of Oslo](https://huggingface.co/ltg), [the High Performance Language Technologies (HPLT) project](https://hplt-project.org/), [the National Library of Norway](https://huggingface.co/NbAiLab), and [the University of Turku](https://huggingface.co/TurkuNLP).
 All the models are pre-trained on the same dataset and with the same tokenizer.
 NorBLOOM-7b-scratch has around 7 billion parameters and is based on [the BLOOM architecture](https://arxiv.org/abs/2211.05100).
-The NORA-LLM language model family includes (as of now):
 - [**NorMistral-7b-warm**](https://huggingface.co/norallm/normistral-7b-warm) -- an LLM initialized from [Mistral-7b-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and continuously pretrained on Norwegian data;
 - [**NorMistral-7b-scratch**](https://huggingface.co/norallm/normistral-7b-scratch) -- a Mistral-based LLM pretrained from scratch on Norwegian data;
 - [**NorBLOOM-7b-scratch**](https://huggingface.co/norallm/NorBLOOM-7b-scratch) -- a BLOOM-based LLM pretrained from scratch on Norwegian data.

 NorBLOOM-7b-scratch is a large Norwegian language model pretrained from scratch on a total of 260 billion subword tokens (using six repetitions of open Norwegian texts).
+This model is a part of the NORA.LLM family developed in collaboration between [the Language Technology Group at the University of Oslo](https://huggingface.co/ltg), [the High Performance Language Technologies (HPLT) project](https://hplt-project.org/), [the National Library of Norway](https://huggingface.co/NbAiLab), and [the University of Turku](https://huggingface.co/TurkuNLP).
 All the models are pre-trained on the same dataset and with the same tokenizer.
 NorBLOOM-7b-scratch has around 7 billion parameters and is based on [the BLOOM architecture](https://arxiv.org/abs/2211.05100).
+The NORA.LLM language model family includes (as of now):
 - [**NorMistral-7b-warm**](https://huggingface.co/norallm/normistral-7b-warm) -- an LLM initialized from [Mistral-7b-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and continuously pretrained on Norwegian data;
 - [**NorMistral-7b-scratch**](https://huggingface.co/norallm/normistral-7b-scratch) -- a Mistral-based LLM pretrained from scratch on Norwegian data;
 - [**NorBLOOM-7b-scratch**](https://huggingface.co/norallm/NorBLOOM-7b-scratch) -- a BLOOM-based LLM pretrained from scratch on Norwegian data.