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)

# pip install -U transformers accelerate
# 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

File size: 3,275 Bytes

---

language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- causal-lm
- base-model
- small-language-model
- custom_code
- muon
- hummingbird
- hummingbird-v2
datasets:
- HuggingFaceFW/fineweb-edu
- HuggingFaceTB/smollm-corpus
- HuggingFaceTB/finemath
- mlfoundations/dclm-baseline-1.0
- allenai/dolma3_dolmino_mix-100B-1025
- allenai/dolma3_dolmino_mix-10B-1025
- HuggingFaceFW/finepdfs-edu
---

![Hummingbird-V2 banner with daisies](hummingbird-v2-banner.jpg)

# 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](https://huggingface.co/juinron/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](TRAINING_DATA.md) for source details and
[training provenance](training/provenance.json) 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:

```python

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](LICENSE). Third-party dataset notices
are included in [NOTICE](NOTICE) and [TRAINING_DATA.md](TRAINING_DATA.md).