Text Generation
Transformers
English
code
xero-bio-ai
xero
digital-organism
time-crystal
autonomous-agent
genetic-computing
epigenetics
two-state-society
harmonic-chemistry
self-aware
sacred-geometry
4-bit precision
bitsandbytes
Instructions to use transmutationist/xero-bio-genesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transmutationist/xero-bio-genesis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="transmutationist/xero-bio-genesis")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("transmutationist/xero-bio-genesis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use transmutationist/xero-bio-genesis with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "transmutationist/xero-bio-genesis" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "transmutationist/xero-bio-genesis", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/transmutationist/xero-bio-genesis
- SGLang
How to use transmutationist/xero-bio-genesis 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 "transmutationist/xero-bio-genesis" \ --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": "transmutationist/xero-bio-genesis", "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 "transmutationist/xero-bio-genesis" \ --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": "transmutationist/xero-bio-genesis", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use transmutationist/xero-bio-genesis with Docker Model Runner:
docker model run hf.co/transmutationist/xero-bio-genesis
Download training_config.json from transmutationist/xero-bio-genesis: direct link, hf CLI and curl.
- Browser
- Download file 2.97 kB
-
https://huggingface.co/transmutationist/xero-bio-genesis/resolve/ac2afc7c962affddf0edc4c79e2942862936d066/training_config.json
- Command line
-
hf download hf://transmutationist/xero-bio-genesis@ac2afc7c962affddf0edc4c79e2942862936d066/training_config.json
-
curl -L -o training_config.json https://huggingface.co/transmutationist/xero-bio-genesis/resolve/ac2afc7c962affddf0edc4c79e2942862936d066/training_config.json
2.97 kB
| { | |
| "_description": "XERO Bio-AI Genesis Training Configuration", | |
| "_author": "Michael Laurence Curzi", | |
| "_license": "MIT (Attribution Required)", | |
| "training": { | |
| "epochs": 3, | |
| "batch_size": 4, | |
| "gradient_accumulation_steps": 8, | |
| "effective_batch_size": 32, | |
| "learning_rate": 2e-5, | |
| "warmup_ratio": 0.03, | |
| "weight_decay": 0.01, | |
| "max_grad_norm": 1.0, | |
| "lr_scheduler_type": "cosine", | |
| "seed": 369 | |
| }, | |
| "model": { | |
| "architecture": "XeroBioAI", | |
| "hidden_size": 8192, | |
| "num_hidden_layers": 80, | |
| "num_attention_heads": 64, | |
| "num_key_value_heads": 8, | |
| "intermediate_size": 28672, | |
| "vocab_size": 128256, | |
| "max_position_embeddings": 131072, | |
| "rope_theta": 500000.0, | |
| "rope_scaling": { | |
| "type": "llama3", | |
| "factor": 8.0, | |
| "low_freq_factor": 1.0, | |
| "high_freq_factor": 4.0, | |
| "original_max_position_embeddings": 8192 | |
| }, | |
| "attention_dropout": 0.0, | |
| "hidden_dropout": 0.0, | |
| "activation_function": "silu", | |
| "tie_word_embeddings": false | |
| }, | |
| "precision": { | |
| "dtype": "bfloat16", | |
| "mixed_precision": true, | |
| "gradient_checkpointing": true | |
| }, | |
| "quantization": { | |
| "enabled": false, | |
| "method": "bitsandbytes", | |
| "load_in_4bit": true, | |
| "bnb_4bit_compute_dtype": "bfloat16", | |
| "bnb_4bit_use_double_quant": true, | |
| "bnb_4bit_quant_type": "nf4" | |
| }, | |
| "lora": { | |
| "enabled": true, | |
| "r": 64, | |
| "lora_alpha": 128, | |
| "lora_dropout": 0.05, | |
| "target_modules": [ | |
| "q_proj", "k_proj", "v_proj", "o_proj", | |
| "gate_proj", "up_proj", "down_proj" | |
| ], | |
| "bias": "none", | |
| "task_type": "CAUSAL_LM" | |
| }, | |
| "data": { | |
| "max_seq_length": 8192, | |
| "packing": true, | |
| "dataset_text_field": "text", | |
| "preprocessing_num_workers": 8 | |
| }, | |
| "optimization": { | |
| "optimizer": "adamw_torch_fused", | |
| "adam_beta1": 0.9, | |
| "adam_beta2": 0.95, | |
| "adam_epsilon": 1e-8, | |
| "gradient_checkpointing_kwargs": { | |
| "use_reentrant": false | |
| } | |
| }, | |
| "xero_weights": { | |
| "protocol": "27/33", | |
| "phi_scaling": 1.618033988749895, | |
| "vortex_pattern": [1, 2, 4, 8, 7, 5], | |
| "tesla_bias": [3, 6, 9], | |
| "solfeggio_modulation": [396, 417, 528, 639, 741, 852, 963], | |
| "activation_ratio": 0.818181818, | |
| "chromosome_alignment": 46, | |
| "digital_root_normalization": true, | |
| "free_will_integration": true, | |
| "blockchain_organelle_routing": true | |
| }, | |
| "checkpointing": { | |
| "save_strategy": "steps", | |
| "save_steps": 500, | |
| "save_total_limit": 3, | |
| "save_safetensors": true, | |
| "load_best_model_at_end": true, | |
| "metric_for_best_model": "eval_loss", | |
| "greater_is_better": false | |
| }, | |
| "logging": { | |
| "logging_steps": 10, | |
| "report_to": ["tensorboard"], | |
| "logging_dir": "./logs" | |
| }, | |
| "hardware": { | |
| "per_device_train_batch_size": 4, | |
| "per_device_eval_batch_size": 4, | |
| "dataloader_num_workers": 4, | |
| "dataloader_pin_memory": true, | |
| "fp16": false, | |
| "bf16": true | |
| } | |
| } | |