Instructions to use merge-crew/da-sv-task-arithmetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use merge-crew/da-sv-task-arithmetic with Transformers:

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

pipe = pipeline("text-generation", model="merge-crew/da-sv-task-arithmetic")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("merge-crew/da-sv-task-arithmetic")
model = AutoModelForCausalLM.from_pretrained("merge-crew/da-sv-task-arithmetic")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use merge-crew/da-sv-task-arithmetic with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/merge-crew/da-sv-task-arithmetic

SGLang

How to use merge-crew/da-sv-task-arithmetic 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 "merge-crew/da-sv-task-arithmetic" \
    --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": "merge-crew/da-sv-task-arithmetic",
		"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 "merge-crew/da-sv-task-arithmetic" \
        --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": "merge-crew/da-sv-task-arithmetic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use merge-crew/da-sv-task-arithmetic with Docker Model Runner:
```
docker model run hf.co/merge-crew/da-sv-task-arithmetic
```

Danish-Swedish Merged Model

This is a merge of the following models, all based on mistralai/Mistral-7B-v0.1:

danish-foundation-models/munin-7b-alpha, continued pretraining on Danish data;
timpal0l/Mistral-7B-v0.1-flashback-v2, continued pretraining on Swedish data.

Model Details

Merged by: Dan Saattrup Nielsen
Model type: Decoder model, based on mistralai/Mistral-7B-v0.1
Language(s): Danish and Swedish
License: CC-BY-4.0

Merge configuration:

dict(
    models=[
        dict(
            model="danish-foundation-models/munin-7b-alpha",
            parameters=dict(
                weight=1.0,
            ),
        ),
        dict(
            model="timpal0l/Mistral-7B-v0.1-flashback-v2",
            parameters=dict(
                weight=1.0,
            ),
        ),
    ],
    merge_method="task_arithmetic",
    base_model="mistralai/Mistral-7B-v0.1",
    parameters=dict(
        int8_mask=True,
        normalize=True,
    ),
    dtype="bfloat16",
)

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

F32

Model tree for merge-crew/da-sv-task-arithmetic

danish-foundation-models/munin-7b-alpha

timpal0l/Mistral-7B-v0.1-flashback-v2

Merge model

this model