Instructions to use tiny-random/zaya1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/zaya1 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/zaya1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiny-random/zaya1")
model = AutoModelForCausalLM.from_pretrained("tiny-random/zaya1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/zaya1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/zaya1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/zaya1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/zaya1

SGLang

How to use tiny-random/zaya1 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 "tiny-random/zaya1" \
    --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": "tiny-random/zaya1",
		"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 "tiny-random/zaya1" \
        --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": "tiny-random/zaya1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/zaya1 with Docker Model Runner:
```
docker model run hf.co/tiny-random/zaya1
```

zaya1

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{
  "activation_func": "swiglu",
  "activation_func_fp8_input_store": false,
  "add_bias_linear": false,
  "architectures": [
    "ZayaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bias_activation_fusion": true,
  "bos_token_id": 2,
  "cca": true,
  "cca_num_q_heads": [
    2,
    0
  ],
  "dtype": "bfloat16",
  "eos_token_id": 1,
  "ffn_hidden_size_list": [
    0,
    32
  ],
  "gated_linear_unit": true,
  "hidden_size": 512,
  "kv_channels": 128,
  "lm_head_bias": false,
  "max_position_embeddings": 32768,
  "model_type": "zaya",
  "moe_router_topk": 1,
  "norm_epsilon": 1e-05,
  "num_attention_heads": 4,
  "num_hidden_layers": 2,
  "num_key_value_heads": 1,
  "num_query_groups_list": [
    1,
    0
  ],
  "pad_token_id": 0,
  "partial_rotary_factor": 0.5,
  "residual_in_fp32": false,
  "rope_scaling": false,
  "rope_theta": 1000000,
  "scale_residual_merge": true,
  "sliding_window": null,
  "tie_word_embeddings": true,
  "transformers_version": "4.57.1",
  "use_cache": true,
  "vocab_size": 262272,
  "zaya_layers": [
    "a",
    16
  ],
  "zaya_mlp_expansion": [
    0,
    8
  ],
  "zaya_use_eda": true,
  "zaya_use_mod": true
}