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 / config.json

juinron

Release Hummingbird-V1 500M natural continuation checkpoint

d03200e verified 4 days ago

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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
	}