Instructions to use AlarKolk/abc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlarKolk/abc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AlarKolk/abc")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AlarKolk/abc") model = AutoModelForCausalLM.from_pretrained("AlarKolk/abc") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AlarKolk/abc with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AlarKolk/abc" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AlarKolk/abc", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AlarKolk/abc
- SGLang
How to use AlarKolk/abc 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 "AlarKolk/abc" \ --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": "AlarKolk/abc", "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 "AlarKolk/abc" \ --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": "AlarKolk/abc", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AlarKolk/abc with Docker Model Runner:
docker model run hf.co/AlarKolk/abc
** Converted model for GPTQ from https://huggingface.co/lmsys/vicuna-13b-delta-v0. This is the best local model I've ever tried. I hope someone makes a version based on the uncensored dataset...**
GPTQ conversion command (on CUDA branch): CUDA_VISIBLE_DEVICES=0 python llama.py ../lmsys/vicuna-13b-v0 c4 --wbits 4 --true-sequential --groupsize 128 --save vicuna-13b-4bit-128g.pt
Added 1 token to the tokenizer model: python llama-tools/add_tokens.py lmsys/vicuna-13b-v0/tokenizer.model /content/tokenizer.model llama-tools/test_list.txt
Use of Oobabooga with these tags: --wbits 4 --groupsize 128
Enjoy