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/validators.py from lilbablo/humigencev2: direct link, hf CLI and curl.
- Browser
- Download file 842 Bytes
-
https://huggingface.co/lilbablo/humigencev2/resolve/main/utils/validators.py
- Command line
-
hf download hf://lilbablo/humigencev2/utils/validators.py
-
curl -L -o validators.py https://huggingface.co/lilbablo/humigencev2/resolve/main/utils/validators.py
842 Bytes
| import os | |
| import json | |
| def detect_datasets(base_path="~/humigence_data"): | |
| base_path = os.path.expanduser(base_path) | |
| choices = [] | |
| for root, dirs, files in os.walk(base_path): | |
| for file in files: | |
| if file.endswith(".jsonl") or file.endswith(".json"): | |
| full_path = os.path.join(root, file) | |
| try: | |
| with open(full_path, "r") as f: | |
| if file.endswith(".jsonl"): | |
| count = sum(1 for _ in f) | |
| else: | |
| data = json.load(f) | |
| count = len(data) | |
| display_name = f"{file} ({count} samples)" | |
| choices.append((display_name, full_path)) | |
| except Exception: | |
| continue | |
| return choices |