Instructions to use robertthecreator/deepseek-coder-secure-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use robertthecreator/deepseek-coder-secure-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base") model = PeftModel.from_pretrained(base_model, "robertthecreator/deepseek-coder-secure-lora") - Transformers
How to use robertthecreator/deepseek-coder-secure-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="robertthecreator/deepseek-coder-secure-lora")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("robertthecreator/deepseek-coder-secure-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use robertthecreator/deepseek-coder-secure-lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "robertthecreator/deepseek-coder-secure-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "robertthecreator/deepseek-coder-secure-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/robertthecreator/deepseek-coder-secure-lora
- SGLang
How to use robertthecreator/deepseek-coder-secure-lora 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 "robertthecreator/deepseek-coder-secure-lora" \ --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": "robertthecreator/deepseek-coder-secure-lora", "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 "robertthecreator/deepseek-coder-secure-lora" \ --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": "robertthecreator/deepseek-coder-secure-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use robertthecreator/deepseek-coder-secure-lora with Docker Model Runner:
docker model run hf.co/robertthecreator/deepseek-coder-secure-lora
| { | |
| "best_metric": 1.6711045503616333, | |
| "best_model_checkpoint": "./best_model/checkpoint-524", | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 524, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.9541984732824428, | |
| "grad_norm": 0.08830071985721588, | |
| "learning_rate": 8.091603053435115e-06, | |
| "loss": 1.9773, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_loss": 1.6711045503616333, | |
| "eval_runtime": 48.1017, | |
| "eval_samples_per_second": 9.688, | |
| "eval_steps_per_second": 1.227, | |
| "step": 524 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 2620, | |
| "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": 1.680497543479296e+16, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |