Instructions to use daslab-testing/CloverLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

CloverLM

File size: 1,782 Bytes

[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" }