Text Generation
Transformers
Safetensors
Danish
Swedish
mistral
Merge
mergekit
text-generation-inference
Instructions to use merge-crew/da-sv-ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use merge-crew/da-sv-ties with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="merge-crew/da-sv-ties")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("merge-crew/da-sv-ties") model = AutoModelForCausalLM.from_pretrained("merge-crew/da-sv-ties") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use merge-crew/da-sv-ties 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-ties" # 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-ties", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/merge-crew/da-sv-ties
- SGLang
How to use merge-crew/da-sv-ties 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-ties" \ --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-ties", "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-ties" \ --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-ties", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use merge-crew/da-sv-ties with Docker Model Runner:
docker model run hf.co/merge-crew/da-sv-ties
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="ties", base_model="mistralai/Mistral-7B-v0.1", parameters=dict( int8_mask=True, normalize=True, ), dtype="bfloat16", )
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