Text Generation
Transformers
Safetensors
Russian
English
zarya
feature-extraction
dllm
diffusion
diffusion-language-modeling
instruct
conversational
custom_code
Instructions to use ai-forever/Zarya-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ai-forever/Zarya-1.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ai-forever/Zarya-1.7B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ai-forever/Zarya-1.7B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ai-forever/Zarya-1.7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ai-forever/Zarya-1.7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ai-forever/Zarya-1.7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ai-forever/Zarya-1.7B
- SGLang
How to use ai-forever/Zarya-1.7B 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 "ai-forever/Zarya-1.7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ai-forever/Zarya-1.7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "ai-forever/Zarya-1.7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ai-forever/Zarya-1.7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ai-forever/Zarya-1.7B with Docker Model Runner:
docker model run hf.co/ai-forever/Zarya-1.7B
Download tokenizer.json from ai-forever/Zarya-1.7B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/ai-forever/Zarya-1.7B/resolve/main/tokenizer.json
- Command line
-
hf download hf://ai-forever/Zarya-1.7B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ai-forever/Zarya-1.7B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 47ccea43c6b77eaa5765a1efd0a89a90138fd338307f1aca38c103135a8ca53b
- Size of remote file:
- 11.4 MB
- SHA256:
- 4c8165c070bd7b1eff751f9cd82acf52c4636e997f2df755d01f4eac442a3108
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