Instructions to use dwhite2003/dwdefense-case-analysis-lora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dwhite2003/dwdefense-case-analysis-lora-v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-Instruct") model = PeftModel.from_pretrained(base_model, "dwhite2003/dwdefense-case-analysis-lora-v2") - Transformers
How to use dwhite2003/dwdefense-case-analysis-lora-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dwhite2003/dwdefense-case-analysis-lora-v2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dwhite2003/dwdefense-case-analysis-lora-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dwhite2003/dwdefense-case-analysis-lora-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dwhite2003/dwdefense-case-analysis-lora-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dwhite2003/dwdefense-case-analysis-lora-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dwhite2003/dwdefense-case-analysis-lora-v2
- SGLang
How to use dwhite2003/dwdefense-case-analysis-lora-v2 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 "dwhite2003/dwdefense-case-analysis-lora-v2" \ --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": "dwhite2003/dwdefense-case-analysis-lora-v2", "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 "dwhite2003/dwdefense-case-analysis-lora-v2" \ --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": "dwhite2003/dwdefense-case-analysis-lora-v2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use dwhite2003/dwdefense-case-analysis-lora-v2 with Docker Model Runner:
docker model run hf.co/dwhite2003/dwdefense-case-analysis-lora-v2
| { | |
| "model": "DWDefense Case Analysis LoRA v2", | |
| "base_model": "meta-llama/Llama-3.3-70B-Instruct", | |
| "existing_lora": "dwhite2003/dwdefense-llama33-70b-lora", | |
| "training_data": "training_data_v2.jsonl", | |
| "training_examples": 2000, | |
| "train_split": 1800, | |
| "eval_split": 200, | |
| "epochs": 3, | |
| "batch_size": 1, | |
| "gradient_accumulation": 16, | |
| "effective_batch_size": 16, | |
| "learning_rate": 0.0002, | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "max_seq_length": 2048, | |
| "train_loss": 0.26428862377605605, | |
| "eval_loss": 0.18975503742694855, | |
| "train_runtime_seconds": 18905.9025, | |
| "gpu": "NVIDIA A100-SXM4-80GB", | |
| "completed_at": "2026-04-13T05:38:22.751458" | |
| } |