Instructions to use ELBennett/Weevil-10M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ELBennett/Weevil-10M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ELBennett/Weevil-10M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ELBennett/Weevil-10M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ELBennett/Weevil-10M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ELBennett/Weevil-10M" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ELBennett/Weevil-10M
- SGLang
How to use ELBennett/Weevil-10M 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" \ --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", "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" \ --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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ELBennett/Weevil-10M with Docker Model Runner:
docker model run hf.co/ELBennett/Weevil-10M
Download tokenizer_config.json from ELBennett/Weevil-10M: direct link, hf CLI and curl.
- Browser
- Download file 364 Bytes
-
https://huggingface.co/ELBennett/Weevil-10M/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ELBennett/Weevil-10M/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ELBennett/Weevil-10M/resolve/main/tokenizer_config.json
364 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|endoftext|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|endoftext|>", | |
| "mask_token": "<|mask|>", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 32768, | |
| "pad_token": "<|padding|>", | |
| "padding_side": "left", | |
| "tokenizer_class": "PreTrainedTokenizerFast" | |
| } | |