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

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# Hummingbird-V2 training data

Hummingbird-V2 was trained from scratch on the 10,000,000,000-token packed
FlightMix Balanced training split. Separate 25,000,000-token validation and
25,000,000-token held-out splits were not used as training records. The selected
checkpoint is the 10B-token checkpoint at step 31,250.

| Source | Hub repository | Training tokens | Declared source license |
|---|---|---:|---|
| FineWeb-Edu | `HuggingFaceFW/fineweb-edu` | 2,050,000,000 | ODC-By 1.0 |
| FineWeb-Edu-Dedup | `HuggingFaceTB/smollm-corpus` | 3,450,000,000 | ODC-By 1.0 |
| Cosmopedia v2 | `HuggingFaceTB/smollm-corpus` | 1,500,000,000 | ODC-By 1.0 |
| FineMath 4+ | `HuggingFaceTB/finemath` | 1,000,000,000 | ODC-By 1.0 |
| DCLM baseline 1.0 | `mlfoundations/dclm-baseline-1.0` | 1,000,000,000 | CC-BY 4.0 |
| Science exposition | `allenai/dolma3_dolmino_mix-100B-1025` | 400,000,000 | ODC-By 1.0 |
| Grounded QA | `allenai/dolma3_dolmino_mix-100B-1025` | 200,000,000 | ODC-By 1.0 |
| FinePDFs-Edu | `HuggingFaceFW/finepdfs-edu` | 300,000,000 | ODC-By 1.0 |
| Educational code | `allenai/dolma3_dolmino_mix-10B-1025` | 100,000,000 | ODC-By 1.0 |

The bundled `training/corpus_contract.yaml` records pinned upstream revisions,
source selections, quality filters, deduplication, and split assignment. The
bundled `training/packed_metadata.json` records the realized packed token and
document totals. The pipeline used a benchmark protection index for rendered
HellaSwag, ARC-Easy, ARC-Challenge, PIQA, and ArithMark-3 prompts and choices.
Protected matching reduces direct overlap but cannot prove complete absence
of benchmark-related content in upstream data.

Public task scores across checkpoints and comparisons with other candidate
runs informed the release choice. This selection bias is disclosed in README.md.
Dataset licenses and notices remain applicable to the upstream data; the
Apache-2.0 license for this model package does not replace them.