Instructions to use valteu/qa_math_qa_lora_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use valteu/qa_math_qa_lora_v3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "valteu/qa_math_qa_lora_v3") - Transformers
How to use valteu/qa_math_qa_lora_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="valteu/qa_math_qa_lora_v3")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("valteu/qa_math_qa_lora_v3", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use valteu/qa_math_qa_lora_v3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "valteu/qa_math_qa_lora_v3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "valteu/qa_math_qa_lora_v3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/valteu/qa_math_qa_lora_v3
- SGLang
How to use valteu/qa_math_qa_lora_v3 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 "valteu/qa_math_qa_lora_v3" \ --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": "valteu/qa_math_qa_lora_v3", "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 "valteu/qa_math_qa_lora_v3" \ --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": "valteu/qa_math_qa_lora_v3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use valteu/qa_math_qa_lora_v3 with Docker Model Runner:
docker model run hf.co/valteu/qa_math_qa_lora_v3
| { | |
| "results": { | |
| "commonsense_qa_commonsense_qa_acc_score": 0.5290745290745291, | |
| "commonsense_qa_commonsense_qa_acc_sem": 0.014290736187689666, | |
| "mathqa_mathqa_acc_score": 0.46733668341708545, | |
| "mathqa_mathqa_acc_sem": 0.0091159076442573, | |
| "mathqa_mathqa_acc_norm_score": 0.45494137353433833, | |
| "logiqa_logiqa_acc_score": 0.2780337941628264, | |
| "logiqa_logiqa_acc_sem": 0.018018696598158766, | |
| "logiqa_logiqa_acc_norm_score": 0.30261136712749614 | |
| }, | |
| "energy": { | |
| "total": 547114.6368800001, | |
| "train": 338581.11309, | |
| "eval": 208533.52379 | |
| }, | |
| "train_energy": 338581.11309, | |
| "eval_energy": 208533.52379 | |
| } |