LTX-Test

model-name

Deploy Dedicated

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Get help setting up a custom Dedicated Endpoints.

Talk with our engineer to get a quote for reserved GPU instances with discounts.

README

License: mit

Specs

Table
Field	Value
Architecture	`GPT2LMHeadModel`
Params	~43.9K
Hidden size	32
Layers	2
Heads	4
Vocab	256
Context	64
dtype	float32
Weights file	`model.safetensors` (~174 KiB)

Usage

python
from transformers import AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained(".")
out = model(torch.zeros(1, 8, dtype=torch.long))
print(out.logits.shape)  # torch.Size([1, 8, 256])

Or load the raw tensors directly:

python
from safetensors.torch import load_file
state = load_file("model.safetensors")
print(len(state), "tensors")

Notes

Weights are random (torch.manual_seed(0), init range 0.02); LayerNorm/biases use the conventional ones/zeros init.
Intended for CI, smoke tests, and verifying upload/download plumbing — do not use for inference quality.

Model provider

LTX-Test

Model tree

Base

this model

Modalities

Input

Text

Output

Text

Pricing

Dedicated Endpoints