Text Generation
Transformers
Safetensors
PyTorch
English
notoken_gen46
byte-level
tokenizer-free
character-level
small-language-model
transformer
rope
rmsnorm
swiglu
muon
from-scratch
custom_code
Instructions to use VeroLM/NoTokenLM-Gen-4.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VeroLM/NoTokenLM-Gen-4.6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VeroLM/NoTokenLM-Gen-4.6", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("VeroLM/NoTokenLM-Gen-4.6", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use VeroLM/NoTokenLM-Gen-4.6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VeroLM/NoTokenLM-Gen-4.6" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VeroLM/NoTokenLM-Gen-4.6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VeroLM/NoTokenLM-Gen-4.6
- SGLang
How to use VeroLM/NoTokenLM-Gen-4.6 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 "VeroLM/NoTokenLM-Gen-4.6" \ --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": "VeroLM/NoTokenLM-Gen-4.6", "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 "VeroLM/NoTokenLM-Gen-4.6" \ --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": "VeroLM/NoTokenLM-Gen-4.6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use VeroLM/NoTokenLM-Gen-4.6 with Docker Model Runner:
docker model run hf.co/VeroLM/NoTokenLM-Gen-4.6
Download gen46_eval_code.zip from VeroLM/NoTokenLM-Gen-4.6: direct link, hf CLI and curl.
- Browser
- Download file 7.48 kB
-
https://huggingface.co/VeroLM/NoTokenLM-Gen-4.6/resolve/main/gen46_eval_code.zip
- Command line
-
hf download hf://VeroLM/NoTokenLM-Gen-4.6/gen46_eval_code.zip
-
curl -L -o gen46_eval_code.zip https://huggingface.co/VeroLM/NoTokenLM-Gen-4.6/resolve/main/gen46_eval_code.zip
7.48 kB
- Xet hash:
- 6156e9472bc0b996767a82497ee7b3eef9d32aedbc434cdc38f0b20c5e8cd28f
- Size of remote file:
- 7.48 kB
- SHA256:
- 95e3e6c4a3fbd3a6d1918a733cacdeb0c5f453c8ed5967c015c89ca45e4ae6d7
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