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VertexResearch
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Model Details
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VertexResearch
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Input Modalities
Output Modalities
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| Architecture | Qwen3 (Qwen3ForCausalLM) |
| Parameters | 33,924,224 (≈34M), tied embeddings |
| Hidden size | 384 |
| Layers | 10 |
| Attention heads | 6 (GQA, 2 KV heads, head_dim 64) |
| Intermediate size | 1536 |
| Context length | 1024 |
| RoPE theta | 10000 |
| Vocab | 32000, byte-level BPE |
Independent byte-level BPE (tokenizer.json), vocab 32000, trained on a
≈480K-document sample of the pretraining mix (≈85% web / ≈15% code).
Special tokens: <|endoftext|> (bos/eos), <|pad|>. No chat/instruct
special tokens — this is a raw base model.
≈1.50B unique tokens, trained for 2 epochs (≈3.0B tokens seen, ≈88 tokens/param):
Documents were concatenated with <|endoftext|> separators and packed into
fixed 1024-token blocks, shuffled independently each epoch.
python
from huggingface_hub import hf_hub_downloadfrom transformers import AutoModelForCausalLM, PreTrainedTokenizerFastmodel = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-35M-Base")tok = PreTrainedTokenizerFast(tokenizer_file=hf_hub_download("VertexResearch/Vertex-0.6-35M-Base", "tokenizer.json"),bos_token="<|endoftext|>", eos_token="<|endoftext|>", pad_token="<|pad|>",)out = model.generate(tok("The", return_tensors="pt").input_ids, max_new_tokens=50)print(tok.decode(out[0]))
These models are not the most coherent yet and need more tuning: expect rambling, repetition, and inconsistent answers, especially over longer generations.