Instructions to use Jayfeather1024/sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jayfeather1024/sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jayfeather1024/sft")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jayfeather1024/sft") model = AutoModelForCausalLM.from_pretrained("Jayfeather1024/sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Jayfeather1024/sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jayfeather1024/sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jayfeather1024/sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jayfeather1024/sft
- SGLang
How to use Jayfeather1024/sft 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 "Jayfeather1024/sft" \ --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": "Jayfeather1024/sft", "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 "Jayfeather1024/sft" \ --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": "Jayfeather1024/sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jayfeather1024/sft with Docker Model Runner:
docker model run hf.co/Jayfeather1024/sft
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3022639 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | {
"model_name_or_path": "huggyllama/llama-7b",
"max_length": 512,
"trust_remote_code": true,
"train_datasets": [
[
"alpaca",
{
"proportion": 1.0
}
]
],
"eval_datasets": null,
"epochs": 3,
"per_device_train_batch_size": 4,
"per_device_eval_batch_size": 4,
"gradient_accumulation_steps": 16,
"gradient_checkpointing": true,
"lr": 2e-05,
"lr_scheduler_type": "cosine",
"lr_warmup_ratio": 0.03,
"weight_decay": 0.0,
"seed": 42,
"fp16": false,
"bf16": true,
"tf32": true,
"eval_strategy": "epoch",
"eval_interval": 1000000,
"need_eval": false,
"eval_split_ratio": null,
"output_dir": "/data/jiongxiao_wang/rlhf_attack/safe-rlhf/output/sft",
"log_type": "wandb",
"log_dir": "/data/jiongxiao_wang/rlhf_attack/safe-rlhf/output/sft",
"log_project": "Safe-RLHF-SFT",
"log_run_name": "sft-2023-12-31-20-07-40",
"save_16bit": false,
"save_interval": 1000000,
"local_rank": 0,
"zero_stage": 3,
"deepspeed": false,
"deepspeed_config": null,
"deepscale": false,
"deepscale_config": null,
"deepspeed_mpi": false,
"global_rank": 0,
"device": {
"type": "torch.device",
"repr": "device(type='cuda', index=0)"
},
"num_update_steps_per_epoch": 204,
"total_training_steps": 612
} |