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)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("daslab-testing/CloverLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
Upload folder using huggingface_hub
Browse files- lm_eval/README.md +1 -1
- lm_eval/pyproject.toml +1 -1
- lm_eval/uv.lock +2 -2
lm_eval/README.md
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3. **Activate the environment:**
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```bash
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source .venv/bin/activate
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```
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### Evaluation Script
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3. **Activate the environment:**
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```bash
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source .venv/bin/activate
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```
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### Evaluation Script
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lm_eval/pyproject.toml
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[tool.uv.sources]
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quartet2 = { git = "https://github.com/IST-DASLab/Quartet-II.git", subdirectory = "kernels", rev = "
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]
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[tool.uv.sources]
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quartet2 = { git = "https://github.com/IST-DASLab/Quartet-II.git", subdirectory = "kernels", rev = "23e8856671fad8b488ba3cfd218a13e0494654b4" }
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{ name = "nvidia-nvtx", specifier = "==13.0.85" },
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{ name = "nvtx", specifier = "==0.2.15" },
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{ name = "packaging", specifier = "==26.0" },
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{ name = "quartet2", git = "https://github.com/IST-DASLab/Quartet-II.git?subdirectory=kernels&rev=
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{ name = "requests", specifier = "==2.32.5" },
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{ name = "scipy", specifier = "==1.17.1" },
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{ name = "sympy", specifier = "==1.14.0" },
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name = "quartet2"
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version = "0.0.2"
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source = { git = "https://github.com/IST-DASLab/Quartet-II.git?subdirectory=kernels&rev=
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dependencies = [
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{ name = "flashinfer-python" },
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{ name = "nvtx" },
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{ name = "nvidia-nvtx", specifier = "==13.0.85" },
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{ name = "nvtx", specifier = "==0.2.15" },
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{ name = "packaging", specifier = "==26.0" },
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{ name = "quartet2", git = "https://github.com/IST-DASLab/Quartet-II.git?subdirectory=kernels&rev=23e8856671fad8b488ba3cfd218a13e0494654b4" },
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{ name = "requests", specifier = "==2.32.5" },
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{ name = "scipy", specifier = "==1.17.1" },
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{ name = "sympy", specifier = "==1.14.0" },
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[[package]]
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name = "quartet2"
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version = "0.0.2"
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source = { git = "https://github.com/IST-DASLab/Quartet-II.git?subdirectory=kernels&rev=23e8856671fad8b488ba3cfd218a13e0494654b4#23e8856671fad8b488ba3cfd218a13e0494654b4" }
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dependencies = [
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{ name = "flashinfer-python" },
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{ name = "nvtx" },
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