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
File size: 1,060 Bytes
ea51a3d | 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 | {
"model_type": "transwave_meyer",
"architectures": ["TransWaveForCausalLM"],
"auto_map": {
"AutoConfig": "configuration_transwave.TransWaveConfig",
"AutoModelForCausalLM": "modeling_transwave.TransWaveForCausalLM"
},
"vocab_size": 256,
"byte_dim": 96,
"bytes_per_block": 8,
"num_scales": 8,
"hidden_size": 768,
"max_position_embeddings": 32768,
"block_context": 4096,
"num_hidden_layers": 12,
"num_groups": 12,
"meyer_bands": 12,
"meyer_controls_per_band": 8,
"meyer_kernel_blocks": 512,
"local_kernel_blocks": 7,
"intermediate_size": 2816,
"decoder_hidden_size": 256,
"trained_context_length": 20480,
"architecture_id": "causal_transwave_blocklifting_meyer_byte_lm_g4_v2",
"training_dataset": "allenai/c4:en",
"training_tokens": 4499988480,
"training_steps": 785,
"training_physical_batch": 280,
"validation_nll": 2.228725892720205,
"validation_ppl": 9.288024597671594,
"source_commit": "5cae31b81e15160379ded79f32bcbb443d7db9f7",
"tie_word_embeddings": true
} |