[](https://discord.com/servers/agora-999382051935506503)
# Chai-1
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
An free and open source community implementation of Chai-1 in PyTorch. [Paper is here](https://chaiassets.com/chai-1/paper/technical_report_v1.pdf)
Join our discord to help us implement this paper!
## Installation
```bash
pip3 install chai-one
```
## Usage
```python
######### example.py
import torch
from loguru import logger
from chai_one.model import ChaiOne
# Set up model parameters
dim_single = 128
dim_pairwise = 128
dim_msa = 128
dim_msa_input = 134 # Adjusted to match the expected input dimension
dim_additional_msa_feats = 2
window_size = 25
# Initialize the model
logger.info("Initializing ChaiOne model")
model = ChaiOne(
dim_single=dim_single,
dim_pairwise=dim_pairwise,
msa_depth=4,
dim_msa=dim_msa,
dim_msa_input=dim_msa_input, # Set to 134
dim_additional_msa_feats=0,
msa_pwa_heads=8,
msa_pwa_dim_head=32,
layerscale_output=False,
heads=8,
window_size=window_size,
num_memory_kv=0,
attn_layers=48,
)
# Create dummy input tensors
batch_size = 1
seq_length = 100
num_msa = 4
logger.info(
f"Creating input tensors with shape: batch_size={batch_size}, seq_length={seq_length}, num_msa={num_msa}"
)
single_repr = torch.randn(batch_size, seq_length, dim_single)
pairwise_repr = torch.randn(
batch_size, seq_length, seq_length, dim_pairwise
)
# Create msa tensor with matching input size for msa_init_proj (134 features)
msa = torch.randn(
batch_size, num_msa, seq_length, dim_msa_input
) # Adjusted to 134
# Forward pass
logger.info("Performing forward pass")
output = model(
single_repr=single_repr,
pairwise_repr=pairwise_repr,
msa=msa,
)
logger.info(f"Output shape: {output.shape}")
```
# License
MIT
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"description": "[](https://discord.com/servers/agora-999382051935506503)\n\n# Chai-1\n\n[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)\n\nAn free and open source community implementation of Chai-1 in PyTorch. [Paper is here](https://chaiassets.com/chai-1/paper/technical_report_v1.pdf)\n\nJoin our discord to help us implement this paper!\n\n\n## Installation\n\n```bash\npip3 install chai-one\n```\n\n## Usage\n\n```python\n\n######### example.py\nimport torch\nfrom loguru import logger\nfrom chai_one.model import ChaiOne\n\n# Set up model parameters\ndim_single = 128\ndim_pairwise = 128\ndim_msa = 128\ndim_msa_input = 134 # Adjusted to match the expected input dimension\ndim_additional_msa_feats = 2\nwindow_size = 25\n\n# Initialize the model\nlogger.info(\"Initializing ChaiOne model\")\nmodel = ChaiOne(\n dim_single=dim_single,\n dim_pairwise=dim_pairwise,\n msa_depth=4,\n dim_msa=dim_msa,\n dim_msa_input=dim_msa_input, # Set to 134\n dim_additional_msa_feats=0,\n msa_pwa_heads=8,\n msa_pwa_dim_head=32,\n layerscale_output=False,\n heads=8,\n window_size=window_size,\n num_memory_kv=0,\n attn_layers=48,\n)\n\n# Create dummy input tensors\nbatch_size = 1\nseq_length = 100\nnum_msa = 4\n\nlogger.info(\n f\"Creating input tensors with shape: batch_size={batch_size}, seq_length={seq_length}, num_msa={num_msa}\"\n)\nsingle_repr = torch.randn(batch_size, seq_length, dim_single)\npairwise_repr = torch.randn(\n batch_size, seq_length, seq_length, dim_pairwise\n)\n\n# Create msa tensor with matching input size for msa_init_proj (134 features)\nmsa = torch.randn(\n batch_size, num_msa, seq_length, dim_msa_input\n) # Adjusted to 134\n\n# Forward pass\nlogger.info(\"Performing forward pass\")\noutput = model(\n single_repr=single_repr,\n pairwise_repr=pairwise_repr,\n msa=msa,\n)\n\nlogger.info(f\"Output shape: {output.shape}\")\n\n```\n\n\n\n# License\nMIT\n",
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