Instructions to use MobiusGaian/gpt_FT_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MobiusGaian/gpt_FT_adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gpt2") model = PeftModel.from_pretrained(base_model, "MobiusGaian/gpt_FT_adapter") - Transformers
How to use MobiusGaian/gpt_FT_adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MobiusGaian/gpt_FT_adapter")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MobiusGaian/gpt_FT_adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MobiusGaian/gpt_FT_adapter with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MobiusGaian/gpt_FT_adapter" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MobiusGaian/gpt_FT_adapter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MobiusGaian/gpt_FT_adapter
- SGLang
How to use MobiusGaian/gpt_FT_adapter 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 "MobiusGaian/gpt_FT_adapter" \ --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": "MobiusGaian/gpt_FT_adapter", "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 "MobiusGaian/gpt_FT_adapter" \ --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": "MobiusGaian/gpt_FT_adapter", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MobiusGaian/gpt_FT_adapter with Docker Model Runner:
docker model run hf.co/MobiusGaian/gpt_FT_adapter
| { | |
| "model_name": "gpt2", | |
| "model_type": "causal_lm", | |
| "tokenizer_name_or_path": "gpt2", | |
| "training_strategy": "lora", | |
| "adaptation_type": "task", | |
| "epochs": 1, | |
| "batch_size": 4, | |
| "learning_rate": 0.0002, | |
| "grad_accum_steps": 1, | |
| "precision": "fp32", | |
| "max_seq_length": 128, | |
| "quantization_bits": 0, | |
| "lora_r": 8, | |
| "lora_alpha": 16, | |
| "lora_dropout": 0.05, | |
| "target_modules": "auto", | |
| "dpo_beta": 0.1, | |
| "dpo_max_prompt_length": 64, | |
| "optimizer": "adamw_torch", | |
| "lr_scheduler_type": "cosine", | |
| "warmup_ratio": 0.03, | |
| "weight_decay": 0.01, | |
| "seed": 42, | |
| "num_train_samples": 190, | |
| "num_val_samples": 10, | |
| "model_family": "default", | |
| "vocab_size": 50257, | |
| "label2id": null, | |
| "id2label": null, | |
| "num_labels": null, | |
| "special_tokens_added": [], | |
| "original_vocab_size": null, | |
| "custom_chat_template": false, | |
| "used_unsloth": false, | |
| "vlm_model_class": null, | |
| "model_class": null | |
| } |