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 / lm_eval /README.md

BlackSamorez

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	### Environment Setup

	Download this directory to a local machine and set up [`uv`](https://docs.astral.sh/uv/).

	1. Install `uv` (if you haven't already):
	```bash
	curl -LsSf [https://astral.sh/uv/install.sh](https://astral.sh/uv/install.sh) \| sh
	```

	2. Sync the environment:
	```bash
	uv sync
	```
	(This automatically creates a virtual environment at `.venv` and strictly installs the dependencies locked in `uv.lock`.)

	3. Activate the environment:
	```bash
	source .venv/bin/activate
	```

	### Evaluation Script

	Run:

	```bash
	accelerate launch eval.py \
	--model cloverlm \
	--model_args "pretrained=daslab-testing/CloverLM,dtype=bfloat16,quartet_2_impl=quartet2,attn_backend=pytorch" \
	--tasks "arc_easy_mi,arc_challenge_mi,hellaswag,piqa" \
	--num_fewshot 0 \
	--include_path ./ \
	--trust_remote_code \
	--confirm_run_unsafe_code \
	--batch_size auto
	```

	### Expected Evaluation Results

	```
	\| Tasks \|Version\|Filter\|n-shot\| Metric \| \|Value \| \|Stderr\|
	\|----------------\|------:\|------\|-----:\|---------------\|---\|-----:\|---\|-----:\|
	\|arc_challenge_mi\| 1\|none \| 0\|acc \|↑ \|0.4625\|± \|0.0146\|
	\| \| \|none \| 0\|acc_mutual_info\|↑ \|0.5094\|± \|0.0146\|
	\| \| \|none \| 0\|acc_norm \|↑ \|0.4923\|± \|0.0146\|
	\|arc_easy_mi \| 1\|none \| 0\|acc \|↑ \|0.7997\|± \|0.0082\|
	\| \| \|none \| 0\|acc_mutual_info\|↑ \|0.7239\|± \|0.0092\|
	\| \| \|none \| 0\|acc_norm \|↑ \|0.7731\|± \|0.0086\|
	\|hellaswag \| 1\|none \| 0\|acc \|↑ \|0.5392\|± \|0.0050\|
	\| \| \|none \| 0\|acc_norm \|↑ \|0.7167\|± \|0.0045\|
	\|piqa \| 1\|none \| 0\|acc \|↑ \|0.7922\|± \|0.0095\|
	\| \| \|none \| 0\|acc_norm \|↑ \|0.8058\|± \|0.0092\|
	```

	### Alternative Backends

	Replace `quartet_2_impl=quartet2` with `quartet_2_impl=pseudoquant` on non-Blackwell GPUs.
	You can try `attn_backend=pytorch/flash2/flash3/flash4` if you have the corresponding libs installed.