Text Generation
Transformers
English
finetuning
lora
qlora
unsloth
gpu
distributed
Eval Results (legacy)
Instructions to use lilbablo/humigencev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilbablo/humigencev2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lilbablo/humigencev2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lilbablo/humigencev2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lilbablo/humigencev2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lilbablo/humigencev2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lilbablo/humigencev2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lilbablo/humigencev2
- SGLang
How to use lilbablo/humigencev2 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 "lilbablo/humigencev2" \ --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": "lilbablo/humigencev2", "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 "lilbablo/humigencev2" \ --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": "lilbablo/humigencev2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use lilbablo/humigencev2 with Docker Model Runner:
docker model run hf.co/lilbablo/humigencev2
Download utils/device.py from lilbablo/humigencev2: direct link, hf CLI and curl.
- Browser
- Download file 897 Bytes
-
https://huggingface.co/lilbablo/humigencev2/resolve/main/utils/device.py
- Command line
-
hf download hf://lilbablo/humigencev2/utils/device.py
-
curl -L -o device.py https://huggingface.co/lilbablo/humigencev2/resolve/main/utils/device.py
897 Bytes
| import platform | |
| import psutil | |
| import torch | |
| import subprocess | |
| def get_system_info(): | |
| info = { | |
| "Platform": platform.system(), | |
| "Python Version": platform.python_version(), | |
| "Torch Version": torch.__version__, | |
| "CUDA Available": torch.cuda.is_available(), | |
| "CUDA Version": torch.version.cuda, | |
| "RAM": f"{round(psutil.virtual_memory().total / (1024**3), 2)} GB", | |
| "CPUs": psutil.cpu_count(logical=True), | |
| } | |
| if torch.cuda.is_available(): | |
| info["GPU Count"] = torch.cuda.device_count() | |
| info["GPUs"] = [ | |
| { | |
| "name": torch.cuda.get_device_name(i), | |
| "memory": f"{round(torch.cuda.get_device_properties(i).total_memory / (1024**3), 2)} GB" | |
| } for i in range(torch.cuda.device_count()) | |
| ] | |
| else: | |
| info["GPU Count"] = 0 | |
| info["GPUs"] = [] | |
| return info |