Text Generation
Transformers
Safetensors
English
cloverlm
causal-lm
quartet-ii
nvfp4
low-precision-training
pretrained
custom_code
Instructions to use daslab-testing/CloverLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daslab-testing/CloverLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="daslab-testing/CloverLM", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("daslab-testing/CloverLM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use daslab-testing/CloverLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "daslab-testing/CloverLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "daslab-testing/CloverLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/daslab-testing/CloverLM
- SGLang
How to use daslab-testing/CloverLM 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 "daslab-testing/CloverLM" \ --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": "daslab-testing/CloverLM", "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 "daslab-testing/CloverLM" \ --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": "daslab-testing/CloverLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use daslab-testing/CloverLM with Docker Model Runner:
docker model run hf.co/daslab-testing/CloverLM
Download lm_eval/pyproject.toml from daslab-testing/CloverLM: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/daslab-testing/CloverLM/resolve/main/lm_eval/pyproject.toml
- Command line
-
hf download hf://daslab-testing/CloverLM/lm_eval/pyproject.toml
-
curl -L -o pyproject.toml https://huggingface.co/daslab-testing/CloverLM/resolve/main/lm_eval/pyproject.toml
1.78 kB
| [project] | |
| name = "cloverlm-eval" | |
| version = "0.1.0" | |
| description = "Add your description here" | |
| readme = "README.md" | |
| requires-python = ">=3.11" | |
| dependencies = [ | |
| "accelerate>=1.13.0", | |
| "apache-tvm-ffi==0.1.9", | |
| "certifi==2026.2.25", | |
| "charset-normalizer==3.4.6", | |
| "click==8.3.1", | |
| "cuda-bindings==13.0.3", | |
| "cuda-pathfinder==1.4.3", | |
| "cuda-python==13.0.3", | |
| "einops==0.8.2", | |
| "filelock==3.25.2", | |
| "flashinfer-python==0.6.6", | |
| "fsspec==2026.2.0", | |
| "idna==3.11", | |
| "jinja2==3.1.6", | |
| "lm-eval>=0.4.11", | |
| "markupsafe==3.0.3", | |
| "mpmath==1.3.0", | |
| "networkx==3.6.1", | |
| "ninja==1.13.0", | |
| "numpy==2.4.3", | |
| "nvidia-cublas==13.1.0.3", | |
| "nvidia-cuda-cupti==13.0.85", | |
| "nvidia-cuda-nvrtc==13.0.88", | |
| "nvidia-cuda-runtime==13.0.96", | |
| "nvidia-cudnn-cu13==9.15.1.9", | |
| "nvidia-cudnn-frontend==1.19.0", | |
| "nvidia-cufft==12.0.0.61", | |
| "nvidia-cufile==1.15.1.6", | |
| "nvidia-curand==10.4.0.35", | |
| "nvidia-cusolver==12.0.4.66", | |
| "nvidia-cusparse==12.6.3.3", | |
| "nvidia-cusparselt-cu13==0.8.0", | |
| "nvidia-cutlass-dsl==4.4.2", | |
| "nvidia-cutlass-dsl-libs-base==4.4.2", | |
| "nvidia-ml-py==13.590.48", | |
| "nvidia-nccl-cu13==2.28.9", | |
| "nvidia-nvjitlink==13.0.88", | |
| "nvidia-nvshmem-cu13==3.4.5", | |
| "nvidia-nvtx==13.0.85", | |
| "nvtx==0.2.15", | |
| "packaging==26.0", | |
| "quartet2", | |
| "requests==2.32.5", | |
| "scipy==1.17.1", | |
| "sympy==1.14.0", | |
| "tabulate==0.10.0", | |
| "tokenmonster>=1.1.12", | |
| "torch==2.10.0+cu130", | |
| "tqdm==4.67.3", | |
| "transformers>=5.3.0", | |
| "triton==3.6.0", | |
| "typing-extensions==4.15.0", | |
| "urllib3==2.6.3", | |
| ] | |
| [tool.uv.sources] | |
| quartet2 = { git = "https://github.com/IST-DASLab/Quartet-II.git", subdirectory = "kernels", rev = "23e8856671fad8b488ba3cfd218a13e0494654b4" } | |