Instructions to use ssbuild/chatglm2-6b-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ssbuild/chatglm2-6b-int4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ssbuild/chatglm2-6b-int4", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("ssbuild/chatglm2-6b-int4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ssbuild/chatglm2-6b-int4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ssbuild/chatglm2-6b-int4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ssbuild/chatglm2-6b-int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ssbuild/chatglm2-6b-int4
- SGLang
How to use ssbuild/chatglm2-6b-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 "ssbuild/chatglm2-6b-int4" \ --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": "ssbuild/chatglm2-6b-int4", "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 "ssbuild/chatglm2-6b-int4" \ --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": "ssbuild/chatglm2-6b-int4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ssbuild/chatglm2-6b-int4 with Docker Model Runner:
docker model run hf.co/ssbuild/chatglm2-6b-int4
Download tokenizer.model from ssbuild/chatglm2-6b-int4: direct link, hf CLI and curl.
- Browser
- Download file 1.02 MB
-
https://huggingface.co/ssbuild/chatglm2-6b-int4/resolve/main/tokenizer.model
- Command line
-
hf download hf://ssbuild/chatglm2-6b-int4/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/ssbuild/chatglm2-6b-int4/resolve/main/tokenizer.model
1.02 MB
- Xet hash:
- 3352c1e6f2520c6c47b1d30d3804127e6e179244b7e45cbdbdb4844ac50f23d2
- Size of remote file:
- 1.02 MB
- SHA256:
- e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2
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