Text Generation
Transformers
Safetensors
qwen3
feature-extraction
dflash
speculative-decoding
diffusion
efficiency
flash-decoding
qwen
diffusion-language-model
custom_code
text-generation-inference
Instructions to use HyzeAI/Hyze1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HyzeAI/Hyze1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HyzeAI/Hyze1B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HyzeAI/Hyze1B", trust_remote_code=True) model = AutoModel.from_pretrained("HyzeAI/Hyze1B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HyzeAI/Hyze1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HyzeAI/Hyze1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HyzeAI/Hyze1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HyzeAI/Hyze1B
- SGLang
How to use HyzeAI/Hyze1B 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 "HyzeAI/Hyze1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HyzeAI/Hyze1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "HyzeAI/Hyze1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HyzeAI/Hyze1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HyzeAI/Hyze1B with Docker Model Runner:
docker model run hf.co/HyzeAI/Hyze1B
File size: 1,102 Bytes
bb46b83 | 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 42 43 44 45 46 47 48 49 50 51 52 53 | {
"architectures": [
"DFlashDraftModel"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoModel": "dflash.DFlashDraftModel"
},
"block_size": 16,
"dflash_config": {
"mask_token_id": 248070,
"target_layer_ids": [
1,
16,
31,
46,
61
]
},
"dtype": "bfloat16",
"eos_token_id": 248044,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 17408,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 262144,
"max_window_layers": 5,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 5,
"num_key_value_heads": 8,
"num_target_layers": 64,
"pad_token_id": 248044,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000000,
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 248320
}
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