Instructions to use juinron/Hummingbird-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use juinron/Hummingbird-V2 with Transformers:

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

pipe = pipeline("text-generation", model="juinron/Hummingbird-V2", 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-V2", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use juinron/Hummingbird-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "juinron/Hummingbird-V2"
# 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-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/juinron/Hummingbird-V2

SGLang

How to use juinron/Hummingbird-V2 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-V2" \
    --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-V2",
		"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-V2" \
        --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-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use juinron/Hummingbird-V2 with Docker Model Runner:
```
docker model run hf.co/juinron/Hummingbird-V2
```

Hummingbird-V2

Hummingbird-V2 is a 9.6M-parameter English base language model trained from scratch on 10 billion tokens. It is designed for text completion and continuation scoring.

Model series

Hummingbird-V2 follows Hummingbird-V1 in the same small language model series. V2 was trained from a fresh initialization.

Architecture

Property	Value
Parameters	9,592,720
Layers / hidden size	14 / 240
Vocabulary	4,096-token digit-aware byte-level BPE
Maximum context	2,048 tokens
Architecture	Decoder-only Transformer with grouped-query attention and SwiGLU

Training and data

The model was trained on this balanced mix:

Source	Share
FineWeb-Edu sources	55%
Cosmopedia v2	15%
FineMath 4+	10%
DCLM baseline	10%
Dolma 3 science, question answering, and code	7%
FinePDFs-Edu	3%

Training used Muon and AdamW with a 512-token context. See training data for source details and training provenance for the full recipe.

Zero-shot evaluation

Author-run zero-shot results for the 10B-token checkpoint. Task scores are normalized continuation accuracy in percent; the Intelligence Index is a chance-normalized composite.

Benchmark	Score
HellaSwag	27.63
ARC-Easy	39.39
ARC-Challenge	21.16
PIQA	57.40
ArithMark-3	36.00
Chance-normalized Intelligence Index	9.556

These evaluations helped select the released checkpoint; results have not been independently verified.

Use

Load juinron/Hummingbird-V2 with Transformers and the packaged custom code:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "juinron/Hummingbird-V2"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval()

inputs = tokenizer("The color of the sky is", return_tensors="pt")
with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=32, do_sample=False, use_cache=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))

trust_remote_code=True loads the packaged model code. Review it before use.

Limitations

Hummingbird-V2 is small and English-focused. It can make factual or reasoning errors and is not instruction-tuned or safety-aligned. Do not rely on it for consequential decisions.

License

The model package is released under Apache-2.0. Third-party dataset notices are included in NOTICE and TRAINING_DATA.md.

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Safetensors

Model size

9.76M params

Tensor type

F32

juinron
/

Hummingbird-V2