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VertexResearch
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Model Details
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VertexResearch
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| Parameters | 96.75M (tied embeddings) |
| Architecture | Qwen3-based transformer |
| Hidden size / layers | 576 / 16 |
| Attention | 9 heads, 3 KV heads (GQA), head_dim 64 |
| FFN size | 2304 (SwiGLU) |
| Context length | 8192 |
| RoPE theta | 1,000,000 |
| Vocab | 32,768 (Supra2-100M tokenizer, digit-split) |
| Precision | trained fp32 compute-graph via torch.compile |
python
from transformers import AutoTokenizer, AutoModelForCausalLMtok = AutoTokenizer.from_pretrained("VertexResearch/Vertex-0.6-100M-8192-ctx-Base")model = AutoModelForCausalLM.from_pretrained("VertexResearch/Vertex-0.6-100M-8192-ctx-Base")ids = tok("The capital of France is", return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=30)print(tok.decode(out[0]))
This is a completion model — prompt it with text to continue, not with chat messages.
These models are not the most coherent yet and need more tuning: expect rambling, repetition, and inconsistent answers, especially over longer generations.
97M parameters and 10B tokens: expect fluent English and basic factual/code completion, frequent factual errors, and repetition under greedy decoding. Knowledge cutoff of the web corpus is approximately April 2024.