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_result.json from Angshul/TransWave: direct link, hf CLI and curl.
- Browser
- Download file 746 Bytes
-
https://huggingface.co/Angshul/TransWave/resolve/main/training_result.json
- Command line
-
hf download hf://Angshul/TransWave/training_result.json
-
curl -L -o training_result.json https://huggingface.co/Angshul/TransWave/resolve/main/training_result.json
746 Bytes
| { | |
| "release": "TransWave-Meyer-96M-C4", | |
| "architecture": "causal_transwave_blocklifting_meyer_byte_lm_g4_v2", | |
| "source_commit": "5cae31b81e15160379ded79f32bcbb443d7db9f7", | |
| "parameters": 96130040, | |
| "dataset": "allenai/c4:en", | |
| "context_tokens": 20480, | |
| "physical_batch": 280, | |
| "tokens_per_full_step": 5734400, | |
| "usable_training_tokens": 4499988480, | |
| "tokens_seen": 4499988480, | |
| "steps": 785, | |
| "target_ppl": 3.0, | |
| "best_full_validation_nll": 2.228725892720205, | |
| "best_full_validation_ppl": 9.288024597671594, | |
| "final_validation": { | |
| "nll": 2.228725892720205, | |
| "ppl": 9.288024597671594, | |
| "tokens": 16773120, | |
| "contexts": 819 | |
| }, | |
| "stop_reason": "maximum_4p5B_token_budget_exhausted", | |
| "wall_hours": 2.553788356317414 | |
| } |