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 config.json from ai-forever/Zarya-1.7B: direct link, hf CLI and curl.
- Browser
- Download file 2.21 kB
-
https://huggingface.co/ai-forever/Zarya-1.7B/resolve/main/config.json
- Command line
-
hf download hf://ai-forever/Zarya-1.7B/config.json
-
curl -L -o config.json https://huggingface.co/ai-forever/Zarya-1.7B/resolve/main/config.json
2.21 kB
| { | |
| "T": 0, | |
| "add_loss_path": false, | |
| "alpha_0": 0.75, | |
| "architectures": [ | |
| "Zarya" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration.ZaryaConfig", | |
| "AutoModel": "modeling.Zarya", | |
| "AutoModelForCausalLM": "modeling.Zarya", | |
| "AutoModelForMaskedLM": "modeling.Zarya" | |
| }, | |
| "bos_token_id": 151644, | |
| "diffusion_attn_mode": "causal", | |
| "diffusion_loss_proportion": 0.5, | |
| "diffusion_shuffle": true, | |
| "dropout": 0.1, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 151645, | |
| "extra_processing": true, | |
| "grouped_noise": false, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6144, | |
| "layer_types": [ | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention", | |
| "full_attention" | |
| ], | |
| "mask_token_id": 151669, | |
| "max_position_embeddings": 40960, | |
| "max_window_layers": 28, | |
| "model_type": "zarya", | |
| "noise_eps": 0.001, | |
| "noise_sorting": true, | |
| "norm_elementwise_affine": true, | |
| "norm_eps": 1e-06, | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 28, | |
| "num_key_value_heads": 8, | |
| "ordered_sampling": true, | |
| "pad_token_id": 151643, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000, | |
| "sample_t_override": 0.0, | |
| "sample_t_upper": 0.1, | |
| "sampling_eps": 0.001, | |
| "scale_by_batch": false, | |
| "sequential_attn_mode": "causal", | |
| "sequential_shuffle": true, | |
| "simple_masking": false, | |
| "sliding_window": null, | |
| "tie_word_embeddings": true, | |
| "time_conditioning": false, | |
| "transformers_version": "5.12.1", | |
| "unnormalized_loss": false, | |
| "use_cache": true, | |
| "use_sliding_window": false, | |
| "vocab_size": 151936 | |
| } | |