Instructions to use TigerResearch/tigerbot-13b-chat-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TigerResearch/tigerbot-13b-chat-8bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TigerResearch/tigerbot-13b-chat-8bit")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TigerResearch/tigerbot-13b-chat-8bit") model = AutoModelForCausalLM.from_pretrained("TigerResearch/tigerbot-13b-chat-8bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TigerResearch/tigerbot-13b-chat-8bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TigerResearch/tigerbot-13b-chat-8bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TigerResearch/tigerbot-13b-chat-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TigerResearch/tigerbot-13b-chat-8bit
- SGLang
How to use TigerResearch/tigerbot-13b-chat-8bit 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 "TigerResearch/tigerbot-13b-chat-8bit" \ --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": "TigerResearch/tigerbot-13b-chat-8bit", "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 "TigerResearch/tigerbot-13b-chat-8bit" \ --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": "TigerResearch/tigerbot-13b-chat-8bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TigerResearch/tigerbot-13b-chat-8bit with Docker Model Runner:
docker model run hf.co/TigerResearch/tigerbot-13b-chat-8bit
Commit History
-
Update README.md
e9e1b28 -
Update README.md
3320b3e -
Update README.md
0a3f109 -
Update README.md
6db8dc7 -
Update README.md
7ca4f1a
-
Rename tigerbot_13b_chat_8bit_128g.safetensors to tigerbot_13b_chat_8bit_c4_128g_no_act.safetensors
b3de569 -
Update quantize_config.json
f1ec5d8
-
Delete tigerbot-13b-8bit-128g.pt
e81f157 -
Upload 8 files
6d7197b
-
Update generation_config.json
2499306
-
Update README.md
bdf369e -
Upload 6 files
a9f02b8 -
Upload tigerbot-13b-8bit-128g.pt
c9641d9 -
initial commit
4b9e925