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: 2,166 Bytes

### 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.