Text Generation
Transformers
English
transwave_meyer
causal-lm
byte-level
raw-byte
attention-free
meyer
wavelet
custom_code
Instructions to use Angshul/TransWave with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Angshul/TransWave with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Angshul/TransWave", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Angshul/TransWave", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Angshul/TransWave with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Angshul/TransWave" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Angshul/TransWave", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Angshul/TransWave
- SGLang
How to use Angshul/TransWave 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 "Angshul/TransWave" \ --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": "Angshul/TransWave", "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 "Angshul/TransWave" \ --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": "Angshul/TransWave", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Angshul/TransWave with Docker Model Runner:
docker model run hf.co/Angshul/TransWave
Download training_protocol.json from Angshul/TransWave: direct link, hf CLI and curl.
- Browser
- Download file 727 Bytes
-
https://huggingface.co/Angshul/TransWave/resolve/main/training_protocol.json
- Command line
-
hf download hf://Angshul/TransWave/training_protocol.json
-
curl -L -o training_protocol.json https://huggingface.co/Angshul/TransWave/resolve/main/training_protocol.json
727 Bytes
| { | |
| "architecture": "causal_transwave_blocklifting_meyer_byte_lm_g4_v2", | |
| "source_commit": "5cae31b81e15160379ded79f32bcbb443d7db9f7", | |
| "parameters": 96130040, | |
| "dataset": "allenai/c4:en", | |
| "objective": "exact causal next-byte NLL", | |
| "context_tokens": 20480, | |
| "physical_batch": 280, | |
| "tokens_per_full_step": 5734400, | |
| "maximum_training_tokens": 4499996672, | |
| "usable_training_tokens": 4499988480, | |
| "target_ppl": 3.0, | |
| "base_lr": 0.0001, | |
| "min_lr": 1e-05, | |
| "warmup_fraction": 0.01, | |
| "grad_clip": 1.0, | |
| "model_seed": 20261010, | |
| "order_seed": 20261011, | |
| "allocator": "expandable_segments:True,garbage_collection_threshold:0.80", | |
| "fingerprint": "370d05a6d2672fc7838726e78e8fe267b4b9c480b4f3708b64e7136beea128fd" | |
| } |