Instructions to use saber135/ada_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use saber135/ada_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/functiongemma-270m-it") model = PeftModel.from_pretrained(base_model, "saber135/ada_model") - Transformers
How to use saber135/ada_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="saber135/ada_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("saber135/ada_model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use saber135/ada_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "saber135/ada_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "saber135/ada_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/saber135/ada_model
- SGLang
How to use saber135/ada_model 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 "saber135/ada_model" \ --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": "saber135/ada_model", "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 "saber135/ada_model" \ --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": "saber135/ada_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use saber135/ada_model with Docker Model Runner:
docker model run hf.co/saber135/ada_model
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 21, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "entropy": 0.5876428335905075, | |
| "epoch": 0.8, | |
| "grad_norm": 26.097728729248047, | |
| "learning_rate": 0.0001, | |
| "loss": 6.2434, | |
| "mean_token_accuracy": 0.48119242638349535, | |
| "num_tokens": 15116.0, | |
| "step": 5 | |
| }, | |
| { | |
| "entropy": 1.4853135066873886, | |
| "epoch": 1.48, | |
| "grad_norm": 5.605739593505859, | |
| "learning_rate": 9.371733080722911e-05, | |
| "loss": 2.4627, | |
| "mean_token_accuracy": 0.6227525683010325, | |
| "num_tokens": 27973.0, | |
| "step": 10 | |
| }, | |
| { | |
| "entropy": 1.726570823613335, | |
| "epoch": 2.16, | |
| "grad_norm": 3.4330334663391113, | |
| "learning_rate": 7.644820051634812e-05, | |
| "loss": 1.4966, | |
| "mean_token_accuracy": 0.7552376985549927, | |
| "num_tokens": 40761.0, | |
| "step": 15 | |
| }, | |
| { | |
| "entropy": 1.1423663020133972, | |
| "epoch": 2.96, | |
| "grad_norm": 2.336728811264038, | |
| "learning_rate": 5.2532458441935636e-05, | |
| "loss": 0.9415, | |
| "mean_token_accuracy": 0.8482233583927155, | |
| "num_tokens": 55844.0, | |
| "step": 20 | |
| } | |
| ], | |
| "logging_steps": 5, | |
| "max_steps": 35, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 5, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 35657548827648.0, | |
| "train_batch_size": 4, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |