Instructions to use danish-foundation-models/DFM-Mimir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danish-foundation-models/DFM-Mimir with Transformers:

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

pipe = pipeline("text-generation", model="danish-foundation-models/DFM-Mimir")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("danish-foundation-models/DFM-Mimir")
model = AutoModelForCausalLM.from_pretrained("danish-foundation-models/DFM-Mimir", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use danish-foundation-models/DFM-Mimir with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danish-foundation-models/DFM-Mimir"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danish-foundation-models/DFM-Mimir",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/danish-foundation-models/DFM-Mimir

SGLang

How to use danish-foundation-models/DFM-Mimir 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/DFM-Mimir" \
    --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": "danish-foundation-models/DFM-Mimir",
		"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 "danish-foundation-models/DFM-Mimir" \
        --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": "danish-foundation-models/DFM-Mimir",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use danish-foundation-models/DFM-Mimir with Docker Model Runner:
```
docker model run hf.co/danish-foundation-models/DFM-Mimir
```

Thanks for the model, how to serve?

by nimishchaudhari - opened 8 days ago

Discussion

nimishchaudhari

8 days ago

Hello

Thanks a lot for the model, always keen on testing <2b models for cpu inference. Can you recommend a serving engine for this? For testing I suppose we need to use hf transformers library?

Thanks for yet another European entry to the AI land.

peter-sk

Danish Foundation Models org 5 days ago

HF transformers or llama cpp with patch (see: https://huggingface.co/sinimiini/HRM-Text-1B-GGUF)

M-CP

4 days ago

Consider oMLX downloaders as first class users, #imjustsaying

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