Instructions to use Taykhoom/RiNALMo-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/RiNALMo-micro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/RiNALMo-micro", trust_remote_code=True)# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/RiNALMo-micro", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -26,6 +26,7 @@ pre-trained on 36 million non-coding RNA sequences.
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| Vocabulary size | 22 |
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| Positional encoding | RoPE (base=10000, non-interleaved) |
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| Architecture | Pre-LN Transformer with SwiGLU FFN |
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**Vocabulary** (index order): `<cls>` (0), `<pad>` (1), `<eos>` (2), `<unk>` (3),
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`<mask>` (4), A (5), C (6), G (7), T (8), I (9), R (10), Y (11), K (12), M (13),
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| Vocabulary size | 22 |
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| Positional encoding | RoPE (base=10000, non-interleaved) |
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| Architecture | Pre-LN Transformer with SwiGLU FFN |
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| Max sequence length | ~8192 (practical; RoPE has no hard limit) |
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**Vocabulary** (index order): `<cls>` (0), `<pad>` (1), `<eos>` (2), `<unk>` (3),
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`<mask>` (4), A (5), C (6), G (7), T (8), I (9), R (10), Y (11), K (12), M (13),
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