Instructions to use zlsl/m_cosmos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zlsl/m_cosmos with Transformers:

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

pipe = pipeline("text-generation", model="zlsl/m_cosmos")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zlsl/m_cosmos")
model = AutoModelForCausalLM.from_pretrained("zlsl/m_cosmos", device_map="auto")

Cosmos

How to use zlsl/m_cosmos with Cosmos:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zlsl/m_cosmos with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zlsl/m_cosmos

SGLang

How to use zlsl/m_cosmos 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 "zlsl/m_cosmos" \
    --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": "zlsl/m_cosmos",
		"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 "zlsl/m_cosmos" \
        --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": "zlsl/m_cosmos",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zlsl/m_cosmos with Docker Model Runner:
```
docker model run hf.co/zlsl/m_cosmos
```

Модель для генерации текстов по космосу, астрофизике, космонавтике

Для пользователей text-generation-webui

В инструменте поломана работа с GPT-2, GPTJ, GPT-NEO и аналогичными модлями, неверно загружается токенизер. Ошибка такая:

eos_token_id = eos_token_id[0] IndexError: list index out of range

Исправляется легко, в файл modules/models.py в функцию load_tokenizer() надо добавить строчку
tokenizer.eos_token_id = 2
перед
return tokenizer

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Safetensors

Model size

0.4B params

Tensor type

F32