Instructions to use tencent/Hy3-preview-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Hy3-preview-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tencent/Hy3-preview-Base") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tencent/Hy3-preview-Base") model = AutoModelForCausalLM.from_pretrained("tencent/Hy3-preview-Base") 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 tencent/Hy3-preview-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tencent/Hy3-preview-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tencent/Hy3-preview-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tencent/Hy3-preview-Base
- SGLang
How to use tencent/Hy3-preview-Base 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 "tencent/Hy3-preview-Base" \ --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": "tencent/Hy3-preview-Base", "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 "tencent/Hy3-preview-Base" \ --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": "tencent/Hy3-preview-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tencent/Hy3-preview-Base with Docker Model Runner:
docker model run hf.co/tencent/Hy3-preview-Base
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f71230b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"architectures": [
"HYV3ForCausalLM"
],
"enable_attention_fp32_softmax": false,
"enable_lm_head_fp32": true,
"enable_moe_fp32_combine": false,
"expert_hidden_dim": 1536,
"moe_intermediate_size": 1536,
"first_k_dense_replace": 1,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.006,
"intermediate_size": 13312,
"max_position_embeddings": 262144,
"model_type": "hy_v3",
"moe_router_enable_expert_bias": true,
"moe_router_use_sigmoid": true,
"num_attention_heads": 64,
"num_experts": 192,
"num_experts_per_tok": 8,
"num_hidden_layers": 80,
"num_key_value_heads": 8,
"num_shared_experts": 1,
"output_router_logits": true,
"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,
"tie_word_embeddings": false,
"transformers_version": "5.6.0",
"use_cache": true,
"use_grouped_mm": false,
"vocab_size": 120832,
"num_nextn_predict_layers": 1
} |