Text Generation
Transformers
Safetensors
llama
mergekit
Merge
conversational
text-generation-inference
Instructions to use Sumail/Eurus10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sumail/Eurus10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sumail/Eurus10") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Sumail/Eurus10") model = AutoModelForCausalLM.from_pretrained("Sumail/Eurus10") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Sumail/Eurus10 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sumail/Eurus10" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sumail/Eurus10", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Sumail/Eurus10
- SGLang
How to use Sumail/Eurus10 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 "Sumail/Eurus10" \ --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": "Sumail/Eurus10", "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 "Sumail/Eurus10" \ --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": "Sumail/Eurus10", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Sumail/Eurus10 with Docker Model Runner:
docker model run hf.co/Sumail/Eurus10
Ctrl+K
- 4.54 kB
- 213 MB xet
- 1.05 kB
- 105 MB xet
- 107 MB xet
- 158 MB xet
- 157 MB xet
- 138 MB xet
- 158 MB xet
- 730 Bytes
- 2.2 GB xet
- 179 MB xet
- 22 Bytes xet
- 101 MB xet
- 99 MB xet
- 102 MB xet
- 98.7 MB xet
- 148 MB xet
- 384 Bytes
- 1.8 MB xet
- 4.31 MB
- 4.32 MB
- 4.95 GB xet
- 4.93 GB xet
- 4.97 GB xet
- 2.81 GB xet
- 34.1 kB
- 152 MB xet
- 149 MB xet
- 137 MB xet
- 212 MB xet
- 164 MB xet
- 200 MB xet
- 206 MB xet
- 215 MB xet
- 205 MB xet
- 244 MB xet
- 139 MB xet
- 242 MB xet
- 148 MB xet
- 1.8 MB xet
- 78 kB
- 629 kB
- 1.8 MB xet
- 45.2 kB
- 341 kB
- 1.79 MB xet
- 118 kB
- 1.09 MB xet
- 420 kB