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 / TRAINING_DATA.md

juinron

Release Hummingbird-V1 500M natural continuation checkpoint

d03200e verified 4 days ago

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Hummingbird-V1 training-data notices

This file documents the data lineage for the released Hummingbird-V1 weights. Apache-2.0 applies to the project code and released model materials; it does not replace the licenses or notices attached to third-party source datasets.

Training lineage

The selected checkpoint has 2,000,170,752 cumulative token presentations:

1,500,000,000 presentations in the original Muon causal-pretraining phase;
500,170,752 presentations in the natural-corpus continuation phase.

The continuation used a 1,091,660,174-token packed training split. Its complete prepared-corpus totals across train, validation and held-out splits were:

Source	Frozen revision	Prepared tokens	License
FineWeb-Edu (`sample-100BT`)	`87f09149ef4734204d70ed1d046ddc9ca3f2b8f9`	570,339,212	ODC-By 1.0
DCLM baseline 1.0	`a3b142c183aebe5af344955ae20836eb34dcf69b`	211,970,663	CC-BY 4.0
FineWeb-HQ	`e58199cdd52438d94405df1a4d8630cc5f13bf84`	109,226,534	ODC-By 1.0
SmolLM-Corpus / Cosmopedia v2	`3ba9d605774198c5868892d7a8deda78031a781f`	168,663,969	ODC-By 1.0
FineMath (`finemath-4plus`)	`e92b25a616738fe95dc186b64dfb19f9c8525594`	53,640,449	ODC-By 1.0

The original base phase also used FineWeb-Edu, Cosmopedia v2, TinyStories, and project-generated procedural arithmetic, tutorial and short-choice-rationale text. TinyStories revision f54c09fd23315a6f9c86f9dc80f725de7d8f9c64 is made available under CDLA-Sharing-1.0.

Filtering, deduplication and evaluation protection

The natural corpus used quality admission filters, canonical exact-document deduplication, and 32-permutation MinHash/8-band LSH near-duplicate removal confirmed by exact Jaccard similarity at a 0.80 threshold. Documents were split deterministically by SHA-256 before packing.

Rendered Open SLM and ArithMark-3 prompts plus choices were held in a protection index and excluded from training. Public benchmark results were nevertheless evaluated at the 250M, 500M, 750M and 1B continuation checkpoints and used to select the released 500M checkpoint. This checkpoint-selection bias is disclosed in the model card; benchmark records and answers were not training data.

Exact quotas, filtering, source fields, split policy and prepared token counts are included in training/corpus_contract.yaml, training/packed_metadata.json and training/provenance.json in the release package.