Text Generation
Transformers
Safetensors
PyTorch
German
llama
german
deutsch
llama2
meta
facebook
text-generation-inference
4-bit precision
gptq
Instructions to use jphme/em_german_7b_v01_gptq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jphme/em_german_7b_v01_gptq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jphme/em_german_7b_v01_gptq")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("jphme/em_german_7b_v01_gptq") model = AutoModelForMultimodalLM.from_pretrained("jphme/em_german_7b_v01_gptq") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jphme/em_german_7b_v01_gptq 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_gptq" # 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_v01_gptq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jphme/em_german_7b_v01_gptq
- SGLang
How to use jphme/em_german_7b_v01_gptq 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_gptq" \ --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_v01_gptq", "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_v01_gptq" \ --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_v01_gptq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jphme/em_german_7b_v01_gptq with Docker Model Runner:
docker model run hf.co/jphme/em_german_7b_v01_gptq
| { | |
| "_name_or_path": "jphme/em_german_7b_v01", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_length": 4096, | |
| "max_position_embeddings": 4096, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "quantization_config": { | |
| "bits": 4, | |
| "damp_percent": 0.1, | |
| "desc_act": 1, | |
| "group_size": 32, | |
| "quant_method": "gptq", | |
| "sym": true, | |
| "true_sequential": true, | |
| "model_file_base_name": "model" | |
| }, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.34.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } |