tx-model-standalone

TXModel - Hub-Ready Version

Zero-hassle deployment! Requires ONLY:

pip install transformers torch safetensors

🚀 Quick Start

from transformers import AutoModel
import torch

# Load from Hub (one command!)
model = AutoModel.from_pretrained(
    "your-username/model-name",
    trust_remote_code=True
)

# Use immediately
genes = torch.randint(0, 100, (2, 10))
values = torch.rand(2, 10)
masks = torch.ones(2, 10).bool()

model.eval()
with torch.no_grad():
    output = model(genes=genes, values=values, gen_masks=masks)
    
print(output.last_hidden_state.shape)  # [2, 10, d_model]

✨ Features

• ✅ Single file - all code in modeling.py
• ✅ Zero dependencies (except transformers + torch)
• ✅ Works with AutoModel out of the box
• ✅ No import errors - everything self-contained

📦 Installation

pip install transformers torch safetensors

That's it!

🎯 Usage

Basic Inference

from transformers import AutoModel

model = AutoModel.from_pretrained(
    "your-username/model-name",
    trust_remote_code=True
)

# Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

Batch Processing

# Your data
batch = {
    'genes': torch.randint(0, 1000, (32, 100)),
    'values': torch.rand(32, 100),
    'masks': torch.ones(32, 100).bool()
}

# Process
model.eval()
with torch.no_grad():
    output = model(**batch)

📊 Model Details

• Parameters: ~70M
• Architecture: Transformer Encoder
• Hidden Size: 512
• Layers: 12
• Heads: 8

📝 Citation

@article{tahoe2024,
  title={Tahoe-x1},
  author={...},
  year={2024}
}