Instructions to use danish-foundation-models/munin-7b-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danish-foundation-models/munin-7b-alpha with Transformers:

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

pipe = pipeline("text-generation", model="danish-foundation-models/munin-7b-alpha")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("danish-foundation-models/munin-7b-alpha")
model = AutoModelForMultimodalLM.from_pretrained("danish-foundation-models/munin-7b-alpha")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use danish-foundation-models/munin-7b-alpha with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/danish-foundation-models/munin-7b-alpha

SGLang

How to use danish-foundation-models/munin-7b-alpha 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 "danish-foundation-models/munin-7b-alpha" \
    --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": "danish-foundation-models/munin-7b-alpha",
		"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 "danish-foundation-models/munin-7b-alpha" \
        --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": "danish-foundation-models/munin-7b-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use danish-foundation-models/munin-7b-alpha with Docker Model Runner:
```
docker model run hf.co/danish-foundation-models/munin-7b-alpha
```

synquid commited on Jan 15, 2024

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@@ -19,9 +19,10 @@ The Munin 7B Alpha Large Language Model (LLM) is a pretrained generative text mo
 It has been trained on [Danish Gigaword](https://gigaword.dk/) using [continual pretraining](https://doi.org/10.48550/arXiv.2308.04014).
-For full details of this model please read our [release blog post](https://foundationmodels.dk/blog/2024/01/11/releasing-munin-7b-alpha---a-danish-llm/). The code-base can be found on the [danish-foundation-models](https://github.com/centre-for-humanities-computing/danish-foundation-models).
-**Note:** This model is an Alpha model, thus we don't recommend using this model in production. If you do find cases where this models outperforms competing models do let us know.
 ## Notice

 It has been trained on [Danish Gigaword](https://gigaword.dk/) using [continual pretraining](https://doi.org/10.48550/arXiv.2308.04014).
+For full details of this model please read our [release blog post](https://foundationmodels.dk/blog/2024/01/11/releasing-munin-7b-alpha---a-danish-llm/).
+The code-base can be found on the [our Git repo](https://github.com/centre-for-humanities-computing/danish-foundation-models).
+**Note:** This model is an Alpha model. We don't recommend using this model in production. If you do use the model, please let us know.
 ## Notice