Instructions to use tiny-random/hy-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/hy-v3 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/hy-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiny-random/hy-v3")
model = AutoModelForCausalLM.from_pretrained("tiny-random/hy-v3")
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

vLLM

How to use tiny-random/hy-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/hy-v3"
# 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/hy-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/hy-v3

SGLang

How to use tiny-random/hy-v3 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/hy-v3" \
    --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/hy-v3",
		"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/hy-v3" \
        --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/hy-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/hy-v3 with Docker Model Runner:
```
docker model run hf.co/tiny-random/hy-v3
```

hy-v3

File size: 1,368 Bytes

803c34b

{
  "architectures": [
    "HYV3ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 120000,
  "dtype": "bfloat16",
  "enable_attention_fp32_softmax": false,
  "enable_lm_head_fp32": true,
  "enable_moe_fp32_combine": false,
  "eod_token_id": 120026,
  "eos_token_id": 120025,
  "expert_hidden_dim": 32,
  "first_k_dense_replace": 1,
  "head_dim": 32,
  "hidden_act": "silu",
  "hidden_size": 8,
  "initializer_range": 0.006,
  "intermediate_size": 32,
  "max_position_embeddings": 262144,
  "mlp_bias": false,
  "mlp_layer_types": [
    "dense",
    "sparse",
    "sparse",
    "sparse"
  ],
  "model_type": "hy_v3",
  "moe_intermediate_size": 32,
  "moe_router_enable_expert_bias": true,
  "moe_router_use_sigmoid": true,
  "num_attention_heads": 8,
  "num_experts": 192,
  "num_experts_per_tok": 8,
  "num_hidden_layers": 4,
  "num_key_value_heads": 4,
  "num_nextn_predict_layers": 1,
  "num_shared_experts": 1,
  "output_router_logits": true,
  "pad_token_id": 120002,
  "qk_norm": true,
  "rms_norm_eps": 1e-05,
  "rope_parameters": {
    "rope_theta": 11158840.0,
    "rope_type": "default"
  },
  "route_norm": true,
  "router_scaling_factor": 2.826,
  "sep_token_id": 120007,
  "tie_word_embeddings": false,
  "transformers_version": "5.7.0.dev0",
  "use_cache": true,
  "use_grouped_mm": false,
  "vocab_size": 120832
}