Text Generation
Transformers
Safetensors
English
llama
gpt
llm
large language model
h2o-llmstudio
text-generation-inference
Instructions to use Saurabh16100/MedLLM-1-1-New with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saurabh16100/MedLLM-1-1-New with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Saurabh16100/MedLLM-1-1-New")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Saurabh16100/MedLLM-1-1-New") model = AutoModelForCausalLM.from_pretrained("Saurabh16100/MedLLM-1-1-New") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Saurabh16100/MedLLM-1-1-New with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Saurabh16100/MedLLM-1-1-New" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Saurabh16100/MedLLM-1-1-New", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Saurabh16100/MedLLM-1-1-New
- SGLang
How to use Saurabh16100/MedLLM-1-1-New 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 "Saurabh16100/MedLLM-1-1-New" \ --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": "Saurabh16100/MedLLM-1-1-New", "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 "Saurabh16100/MedLLM-1-1-New" \ --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": "Saurabh16100/MedLLM-1-1-New", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Saurabh16100/MedLLM-1-1-New with Docker Model Runner:
docker model run hf.co/Saurabh16100/MedLLM-1-1-New
| { | |
| "_name_or_path": "h2oai/h2ogpt-4096-llama2-7b-chat", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "bos_token_id": 1, | |
| "custom_pipelines": { | |
| "text-generation": { | |
| "impl": "h2oai_pipeline.H2OTextGenerationPipeline", | |
| "pt": "AutoModelForCausalLM" | |
| } | |
| }, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 4096, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "pad_token_id": 0, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.31.0", | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |