Text Generation
Transformers
Safetensors
PyTorch
German
llama
german
deutsch
llama2
meta
facebook
conversational
text-generation-inference
Instructions to use jphme/em_german_7b_v01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jphme/em_german_7b_v01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jphme/em_german_7b_v01") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jphme/em_german_7b_v01") model = AutoModelForCausalLM.from_pretrained("jphme/em_german_7b_v01") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jphme/em_german_7b_v01 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_v01" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jphme/em_german_7b_v01", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jphme/em_german_7b_v01
- SGLang
How to use jphme/em_german_7b_v01 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_v01" \ --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": "jphme/em_german_7b_v01", "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 "jphme/em_german_7b_v01" \ --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": "jphme/em_german_7b_v01", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jphme/em_german_7b_v01 with Docker Model Runner:
docker model run hf.co/jphme/em_german_7b_v01
Commit History
update readme a3f7893
update readme 13a0031
update readme 439e3e1
update readme 787e9f2
Update README.md 9f8e269
Update README.md 9babbbe
Update README.md 7246423
Create README.md ca611ff
Add Llama2 Lincense 036c7f5
merged model upload 512d208
remove old model versions 48d00b3
Jan Philipp Harries commited on