Text Generation
Transformers
PyTorch
English
chatglm
conversational
text2text-generation
custom_code
Instructions to use NewBreaker/chatglm-6b-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NewBreaker/chatglm-6b-int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NewBreaker/chatglm-6b-int4", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NewBreaker/chatglm-6b-int4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NewBreaker/chatglm-6b-int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NewBreaker/chatglm-6b-int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NewBreaker/chatglm-6b-int4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NewBreaker/chatglm-6b-int4
- SGLang
How to use NewBreaker/chatglm-6b-int4 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 "NewBreaker/chatglm-6b-int4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NewBreaker/chatglm-6b-int4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "NewBreaker/chatglm-6b-int4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NewBreaker/chatglm-6b-int4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NewBreaker/chatglm-6b-int4 with Docker Model Runner:
docker model run hf.co/NewBreaker/chatglm-6b-int4
Download load_model.py from NewBreaker/chatglm-6b-int4: direct link, hf CLI and curl.
- Browser
- Download file 419 Bytes
-
https://huggingface.co/NewBreaker/chatglm-6b-int4/resolve/main/load_model.py
- Command line
-
hf download hf://NewBreaker/chatglm-6b-int4/load_model.py
-
curl -L -o load_model.py https://huggingface.co/NewBreaker/chatglm-6b-int4/resolve/main/load_model.py
419 Bytes
| from transformers import AutoTokenizer, AutoModel,pipeline | |
| # | |
| # tokenizer = AutoTokenizer.from_pretrained(".\\", trust_remote_code=True) | |
| # # model = AutoModel.from_pretrained(".\\", trust_remote_code=True).float() | |
| # model = AutoModel.from_pretrained(".\\", trust_remote_code=True) | |
| # model = model.eval() | |
| # response, history = model.chat(tokenizer, "你好", history=[]) | |
| # print("response:", response) | |
| npl = pipeline('') |