Instructions to use LTX-Test-Renamed/model-name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LTX-Test-Renamed/model-name with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LTX-Test-Renamed/model-name")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LTX-Test-Renamed/model-name")
model = AutoModelForCausalLM.from_pretrained("LTX-Test-Renamed/model-name")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LTX-Test-Renamed/model-name with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LTX-Test-Renamed/model-name"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LTX-Test-Renamed/model-name",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LTX-Test-Renamed/model-name

SGLang

How to use LTX-Test-Renamed/model-name 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 "LTX-Test-Renamed/model-name" \
    --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": "LTX-Test-Renamed/model-name",
		"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 "LTX-Test-Renamed/model-name" \
        --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": "LTX-Test-Renamed/model-name",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LTX-Test-Renamed/model-name with Docker Model Runner:
```
docker model run hf.co/LTX-Test-Renamed/model-name
```

model-name / README.md

barakyoresh

Upload folder using huggingface_hub

f7e7356 verified 30 days ago

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metadata

license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
  - gpt2
  - tiny
  - testing
  - dummy

model-name

A tiny, randomly-initialized GPT-2-style model for testing tooling and pipelines. It is not trained — outputs are meaningless. The point is a small, valid Hugging Face layout (config.json + model.safetensors) that real loaders accept.

Specs

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

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:

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.