Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
gpt_bigcode
Generated from Trainer
text-generation-inference
Instructions to use HuggingFaceH4/starchat-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceH4/starchat-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/starchat-beta")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/starchat-beta") model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/starchat-beta") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HuggingFaceH4/starchat-beta with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceH4/starchat-beta" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceH4/starchat-beta", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceH4/starchat-beta
- SGLang
How to use HuggingFaceH4/starchat-beta 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 "HuggingFaceH4/starchat-beta" \ --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": "HuggingFaceH4/starchat-beta", "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 "HuggingFaceH4/starchat-beta" \ --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": "HuggingFaceH4/starchat-beta", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceH4/starchat-beta with Docker Model Runner:
docker model run hf.co/HuggingFaceH4/starchat-beta
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.901639344262295, | |
| "global_step": 90, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.07, | |
| "learning_rate": 0.0, | |
| "loss": 1.8309, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.52, | |
| "learning_rate": 2e-05, | |
| "loss": 1.5321, | |
| "step": 8 | |
| }, | |
| { | |
| "epoch": 0.98, | |
| "eval_loss": 1.2855817079544067, | |
| "eval_runtime": 6.6367, | |
| "eval_samples_per_second": 30.437, | |
| "eval_steps_per_second": 1.055, | |
| "step": 15 | |
| }, | |
| { | |
| "epoch": 1.05, | |
| "learning_rate": 2e-05, | |
| "loss": 1.35, | |
| "step": 16 | |
| }, | |
| { | |
| "epoch": 1.57, | |
| "learning_rate": 2e-05, | |
| "loss": 1.2071, | |
| "step": 24 | |
| }, | |
| { | |
| "epoch": 1.97, | |
| "eval_loss": 1.2619894742965698, | |
| "eval_runtime": 5.1554, | |
| "eval_samples_per_second": 39.183, | |
| "eval_steps_per_second": 1.358, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 2.1, | |
| "learning_rate": 2e-05, | |
| "loss": 1.1502, | |
| "step": 32 | |
| }, | |
| { | |
| "epoch": 2.62, | |
| "learning_rate": 2e-05, | |
| "loss": 1.0162, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 2.95, | |
| "eval_loss": 1.285272240638733, | |
| "eval_runtime": 5.1992, | |
| "eval_samples_per_second": 38.852, | |
| "eval_steps_per_second": 1.346, | |
| "step": 45 | |
| }, | |
| { | |
| "epoch": 3.15, | |
| "learning_rate": 2e-05, | |
| "loss": 0.9511, | |
| "step": 48 | |
| }, | |
| { | |
| "epoch": 3.67, | |
| "learning_rate": 2e-05, | |
| "loss": 0.8484, | |
| "step": 56 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "eval_loss": 1.3274288177490234, | |
| "eval_runtime": 5.1899, | |
| "eval_samples_per_second": 38.922, | |
| "eval_steps_per_second": 1.349, | |
| "step": 61 | |
| }, | |
| { | |
| "epoch": 4.2, | |
| "learning_rate": 2e-05, | |
| "loss": 0.7971, | |
| "step": 64 | |
| }, | |
| { | |
| "epoch": 4.72, | |
| "learning_rate": 2e-05, | |
| "loss": 0.6981, | |
| "step": 72 | |
| }, | |
| { | |
| "epoch": 4.98, | |
| "eval_loss": 1.3993656635284424, | |
| "eval_runtime": 5.213, | |
| "eval_samples_per_second": 38.749, | |
| "eval_steps_per_second": 1.343, | |
| "step": 76 | |
| }, | |
| { | |
| "epoch": 5.25, | |
| "learning_rate": 2e-05, | |
| "loss": 0.6462, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 5.77, | |
| "learning_rate": 2e-05, | |
| "loss": 0.5668, | |
| "step": 88 | |
| }, | |
| { | |
| "epoch": 5.9, | |
| "eval_loss": 1.4719988107681274, | |
| "eval_runtime": 5.1996, | |
| "eval_samples_per_second": 38.849, | |
| "eval_steps_per_second": 1.346, | |
| "step": 90 | |
| }, | |
| { | |
| "epoch": 5.9, | |
| "step": 90, | |
| "total_flos": 383994839433216.0, | |
| "train_loss": 0.9728257921006944, | |
| "train_runtime": 2307.7787, | |
| "train_samples_per_second": 10.108, | |
| "train_steps_per_second": 0.039 | |
| } | |
| ], | |
| "max_steps": 90, | |
| "num_train_epochs": 6, | |
| "total_flos": 383994839433216.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |