Instructions to use ELBennett/Weevil-3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ELBennett/Weevil-3M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ELBennett/Weevil-3M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ELBennett/Weevil-3M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ELBennett/Weevil-3M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ELBennett/Weevil-3M" # 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-3M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ELBennett/Weevil-3M
- SGLang
How to use ELBennett/Weevil-3M 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-3M" \ --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-3M", "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-3M" \ --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-3M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ELBennett/Weevil-3M with Docker Model Runner:
docker model run hf.co/ELBennett/Weevil-3M
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0f70a95 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"variant": "Weevil-3M",
"source_checkpoint": "reports/latent-gdn2-sub3m-tst-8gpu-10b-20261023/step-00038147",
"source_model_sha256": "17715c39cc5fb56fd37bd655e98082991440cdc7d462160bfc2678bd0cd8263b",
"source_step": 38147,
"parameters": 2993576,
"model_spec": {
"name": "weevil_latent_gdn2_sub3m_v1",
"model_class": "WeevilSimpleParallel",
"layers_per_stack": 8,
"constructor": {
"vocab_size": 4096,
"embedding_dim": 128,
"intermediate_size": 432
},
"gdn2_config": {
"hidden_size": 144,
"num_heads": 3,
"key_head_dim": 48,
"value_head_dim": 48,
"qkv_rank": 48,
"conv_kernel": 4,
"norm_eps": 1e-06,
"compute_dtype": "bfloat16",
"mixer": "chunk",
"chunk_size": null,
"prefix_block_size": null
},
"parameter_budget": {
"exclusive_upper_limit": 3000000,
"expected_total": 2993576,
"embedding": 542720,
"mlp": 1492992,
"mixers": 956568,
"normalization": 1296,
"qkv_subset_of_mixers": 221184
}
},
"weights_dtype": "float32"
}
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