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

Libraries

How to use juinron/Hummingbird-V1 with Transformers:

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

pipe = pipeline("text-generation", model="juinron/Hummingbird-V1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("juinron/Hummingbird-V1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use juinron/Hummingbird-V1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/juinron/Hummingbird-V1

SGLang

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

Docker Model Runner
How to use juinron/Hummingbird-V1 with Docker Model Runner:
```
docker model run hf.co/juinron/Hummingbird-V1
```

Hummingbird-V1

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{
  "activation": "swiglu",
  "architecture": "MicroLoopForDiffusionLM",
  "architectures": [
    "MicroLoopForDiffusionLM"
  ],
  "attention_implementation": "sdpa",
  "attention_output_gate": false,
  "attn_res_block_size": null,
  "auto_map": {
    "AutoConfig": "configuration_microloop.MicroLoopConfig",
    "AutoModelForCausalLM": "modeling_microloop.MicroLoopForDiffusionLM"
  },
  "bos_token_id": 1,
  "diffusion": {
    "fallback_block_size": 16,
    "objective": "absorbing_mask_mdlm",
    "primary_block_size": 32,
    "training_block_sizes": {
      "16": 0.35,
      "32": 0.6,
      "64": 0.05
    }
  },
  "dropout": 0.0,
  "dtype": "float32",
  "eos_token_id": 2,
  "head_dimension": 40,
  "hidden_size": 240,
  "intermediate_size": 640,
  "is_decoder": true,
  "looping": {
    "layers": [
      4,
      5,
      6
    ],
    "maximum_serving_loops": 3,
    "training_loop_counts": [
      1
    ]
  },
  "max_position_embeddings": 2048,
  "model_type": "microloop_diffusion",
  "mtp_enabled": false,
  "normalization": "rmsnorm",
  "num_attention_heads": 6,
  "num_hidden_layers": 14,
  "num_key_value_heads": 2,
  "pad_token_id": 0,
  "positional_encoding": "rope",
  "qk_norm": "per_head",
  "rms_norm_eps": 1e-05,
  "rope_theta": 10000.0,
  "swiglu_clamp": {
    "enabled": true,
    "gate_max": 10.0,
    "linear_max": 10.0,
    "linear_min": -10.0
  },
  "target_parameters": 10000000,
  "tie_word_embeddings": true,
  "tokenizer": {
    "required_special_tokens": [
      "<pad>",
      "<bos>",
      "<eos>",
      "<mask>",
      "<system>",
      "<user>",
      "<assistant>",
      "<turn_end>",
      "<doc_end>"
    ],
    "type": "byte_level_bpe",
    "vocabulary_size": 4096
  },
  "transformers_version": "5.14.1",
  "use_cache": false,
  "vocab_size": 4096
}