Instructions to use jingyaogong/minimind-3o with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jingyaogong/minimind-3o with Transformers:

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

pipe = pipeline("text-generation", model="jingyaogong/minimind-3o", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jingyaogong/minimind-3o", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jingyaogong/minimind-3o with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jingyaogong/minimind-3o"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jingyaogong/minimind-3o",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jingyaogong/minimind-3o

SGLang

How to use jingyaogong/minimind-3o 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 "jingyaogong/minimind-3o" \
    --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": "jingyaogong/minimind-3o",
		"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 "jingyaogong/minimind-3o" \
        --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": "jingyaogong/minimind-3o",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jingyaogong/minimind-3o with Docker Model Runner:
```
docker model run hf.co/jingyaogong/minimind-3o
```

minimind-3o

File size: 1,332 Bytes

e623a8a

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  "architectures": [
    "MiniMindOmni"
  ],
  "audio_hidden_size": 512,
  "audio_ids": [
    16
  ],
  "audio_pad_token": 2049,
  "audio_special_token": "<|audio_pad|>",
  "audio_spk_token": 2051,
  "audio_stop_token": 2050,
  "audio_vocab_size": 2112,
  "auto_map": {
    "AutoConfig": "model_omni.OmniConfig",
    "AutoModelForCausalLM": "model_omni.MiniMindOmni"
  },
  "bos_token_id": 1,
  "bridge_layer": 3,
  "dropout": 0.0,
  "dtype": "bfloat16",
  "eos_token_id": 2,
  "flash_attn": true,
  "head_dim": 96,
  "hidden_act": "silu",
  "hidden_size": 768,
  "image_hidden_size": 768,
  "image_ids": [
    12
  ],
  "image_special_token": "<|image_pad|>",
  "image_token_len": 64,
  "inference_rope_scaling": false,
  "intermediate_size": 2432,
  "max_position_embeddings": 32768,
  "model_type": "minimind-o",
  "moe_intermediate_size": 2432,
  "norm_topk_prob": true,
  "num_attention_heads": 8,
  "num_experts": 4,
  "num_experts_per_tok": 1,
  "num_hidden_layers": 8,
  "num_key_value_heads": 4,
  "num_talker_hidden_layers": 4,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 1000000.0,
  "router_aux_loss_coef": 0.0005,
  "spk_emb_size": 192,
  "talker_hidden_size": 768,
  "think_end_ids": [
    26,
    234,
    234
  ],
  "transformers_version": "4.57.6",
  "use_moe": false,
  "vocab_size": 6400
}