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.