Instructions to use ELBennett/Weevil-10M-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ELBennett/Weevil-10M-QA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ELBennett/Weevil-10M-QA", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ELBennett/Weevil-10M-QA", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ELBennett/Weevil-10M-QA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ELBennett/Weevil-10M-QA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ELBennett/Weevil-10M-QA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ELBennett/Weevil-10M-QA

SGLang

How to use ELBennett/Weevil-10M-QA 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 "ELBennett/Weevil-10M-QA" \
    --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": "ELBennett/Weevil-10M-QA",
		"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 "ELBennett/Weevil-10M-QA" \
        --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": "ELBennett/Weevil-10M-QA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ELBennett/Weevil-10M-QA with Docker Model Runner:
```
docker model run hf.co/ELBennett/Weevil-10M-QA
```

Weevil-10M-QA / export_metadata.json

ELBennett

Add Weevil Transformers model, tokenizer and weights

91cb9ef verified 4 days ago

Raw History Blame Contribute Delete

310 Bytes

{
  "variant": "Weevil-10M-QA",
  "source_checkpoint": "reports/qa-long-stable-decay-8gpu-20261022/step-00015625",
  "source_model_sha256": "789b1d4c7c2ee2cc3fc5c0a46b915e3739149896a58f553c3eedcaa68acb9ffd",
  "source_step": 15625,
  "parameters": 9894464,
  "model_spec": null,
  "weights_dtype": "float32"
}

	{
	"variant": "Weevil-10M-QA",
	"source_checkpoint": "reports/qa-long-stable-decay-8gpu-20261022/step-00015625",
	"source_model_sha256": "789b1d4c7c2ee2cc3fc5c0a46b915e3739149896a58f553c3eedcaa68acb9ffd",
	"source_step": 15625,
	"parameters": 9894464,
	"model_spec": null,
	"weights_dtype": "float32"
	}