Instructions to use ZichenAI/BaiHu-V1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZichenAI/BaiHu-V1-Flash with Transformers:

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

pipe = pipeline("text-generation", model="ZichenAI/BaiHu-V1-Flash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ZichenAI/BaiHu-V1-Flash", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ZichenAI/BaiHu-V1-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZichenAI/BaiHu-V1-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZichenAI/BaiHu-V1-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZichenAI/BaiHu-V1-Flash

SGLang

How to use ZichenAI/BaiHu-V1-Flash 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 "ZichenAI/BaiHu-V1-Flash" \
    --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": "ZichenAI/BaiHu-V1-Flash",
		"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 "ZichenAI/BaiHu-V1-Flash" \
        --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": "ZichenAI/BaiHu-V1-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ZichenAI/BaiHu-V1-Flash with Docker Model Runner:
```
docker model run hf.co/ZichenAI/BaiHu-V1-Flash
```

BaiHu-V1-Flash

File size: 1,966 Bytes

b418a7a

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  "architectures": [
    "BaiHuSSAForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "dtype": "bfloat16",
  "eos_token_id": 151643,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 1024,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_types": [
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    "full_attention",
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    "full_attention"
  ],
  "max_position_embeddings": 32768,
  "max_window_layers": 28,
  "model_type": "baihu_ssa",
  "num_attention_heads": 16,
  "num_hidden_layers": 28,
  "num_key_value_heads": 8,
  "pad_token_id": null,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "rope_theta": 1000000,
    "rope_type": "default"
  },
  "sliding_window": null,
  "ssa_block_size": 64,
  "ssa_component_init": 0.01,
  "ssa_component_seed": 1234,
  "ssa_compress_dim": 128,
  "ssa_donor_model": "Qwen3-0.6B-Base",
  "ssa_donor_revision": null,
  "ssa_force_full_window": false,
  "ssa_local_blocks": 2,
  "ssa_num_subq_heads": 4,
  "ssa_router_bias_scale": 0.1,
  "ssa_router_dim": 128,
  "ssa_router_init_scale": 0.01,
  "ssa_shared_init_scale": 0.01,
  "ssa_shared_kv": true,
  "ssa_top_k": 8,
  "ssa_use_router_key": true,
  "tie_word_embeddings": true,
  "transformers_version": "5.17.0",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 151936
}