Instructions to use wop/Cosmos-T2-Accelerate-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wop/Cosmos-T2-Accelerate-Preview with Transformers:

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

pipe = pipeline("text-generation", model="wop/Cosmos-T2-Accelerate-Preview")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("wop/Cosmos-T2-Accelerate-Preview", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wop/Cosmos-T2-Accelerate-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wop/Cosmos-T2-Accelerate-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wop/Cosmos-T2-Accelerate-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wop/Cosmos-T2-Accelerate-Preview

SGLang

How to use wop/Cosmos-T2-Accelerate-Preview 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 "wop/Cosmos-T2-Accelerate-Preview" \
    --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": "wop/Cosmos-T2-Accelerate-Preview",
		"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 "wop/Cosmos-T2-Accelerate-Preview" \
        --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": "wop/Cosmos-T2-Accelerate-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use wop/Cosmos-T2-Accelerate-Preview with Docker Model Runner:
```
docker model run hf.co/wop/Cosmos-T2-Accelerate-Preview
```

Cosmos-T2-Accelerate-Preview / model_config.json

wop

Initial preview release: model checkpoints, history, config, README

7a10347 verified 1 day ago

raw

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	{
	"model_family": "Cosmos-T2-Accelerate-Preview",
	"model_name": "Cosmos-T2-Accelerate-Preview",
	"model_class_name": "CosmosT2_Accelerate_LLM",
	"hf_repo_id": "wop/Cosmos-T2-Accelerate-Preview",
	"tokenizer_name": "Qwen/Qwen2.5-0.5B",
	"dataset_name": "wop/XXXXXL-chain-of-thought",
	"dataset_split": "train",
	"dataset_row_limit": 10000,
	"train_val_fraction": 0.1,
	"seed": 42,
	"block_size": 1028,
	"max_len": 1028,
	"d_model": 64,
	"n_layers": 4,
	"n_heads": 4,
	"n_kv_heads": 1,
	"d_ff": 256,
	"rope_base": 10000,
	"dropout": 0.05,
	"use_engram": true,
	"engram_every": 2,
	"engram_buckets": 128,
	"engram_dim": 16,
	"engram_order": 3,
	"epochs": 50,
	"batch_size": 6,
	"lr": 0.0003,
	"weight_decay": 0.1,
	"warmup_steps": 50,
	"grad_clip": 1.0,
	"log_every_steps": 10,
	"eval_every_steps": 500,
	"plot_every_epochs": 20,
	"val_max_batches": 50
	}