Text Generation
Transformers
Safetensors
PyTorch
German
llama
german
deutsch
llama2
meta
facebook
leolm
custom_code
text-generation-inference
Instructions to use jphme/em_german_7b_leo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jphme/em_german_7b_leo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jphme/em_german_7b_leo", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jphme/em_german_7b_leo", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("jphme/em_german_7b_leo", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jphme/em_german_7b_leo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jphme/em_german_7b_leo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jphme/em_german_7b_leo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jphme/em_german_7b_leo
- SGLang
How to use jphme/em_german_7b_leo 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 "jphme/em_german_7b_leo" \ --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": "jphme/em_german_7b_leo", "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 "jphme/em_german_7b_leo" \ --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": "jphme/em_german_7b_leo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jphme/em_german_7b_leo with Docker Model Runner:
docker model run hf.co/jphme/em_german_7b_leo
merged model upload
Browse files- config.json +1 -1
- model-00001-of-00002.safetensors +1 -1
- model-00002-of-00002.safetensors +1 -1
- tokenizer.json +0 -0
- tokenizer_config.json +3 -6
config.json
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"_name_or_path": "jphme/
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"architectures": [
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"LlamaForCausalLM"
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{
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"_name_or_path": "jphme/em_german_7b_leo",
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"architectures": [
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"LlamaForCausalLM"
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model-00001-of-00002.safetensors
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model-00002-of-00002.safetensors
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tokenizer.json
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"added_tokens_decoder": {
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"normalized": false,
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"1": {
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"additional_special_tokens": [],
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"tokenizer_file": "/root/.cache/huggingface/hub/models--LeoLM--leo-hessianai-7b/snapshots/88c5ac07006ea8f1b5d10aa4f03f0d624dd27e56/tokenizer.json",
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"unk_token": "<unk>",
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"normalized": false,
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"2": {
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"normalized": false,
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"special": true
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"additional_special_tokens": [],
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": true
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}
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