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Genos 1.2B & 10B: How Ultra-Long 1 Mb Context Transforms Genomic AI

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From 1 Mb Down to Single-Base: How Genos Turns “Ultra-Long Human Genomes” into a Cloud Model Anyone Can Use

A field-note for bioinformaticians, ML engineers, and product managers who need genomic AI that just works

TL;DR: Genos open-sources a 1.2 B / 10 B MoE Transformer that sees one million consecutive bases at single-nucleotide resolution, beats strong baselines on enhancer calling, ClinVar pathogenicity, mutation-hotspot detection and RNA-seq simulation, and is already hosted online with 1 B free tokens. Code, weights and Docker images are MIT-licensed—ready for production tonight.

7 Questions This Post Answers

  1. What can Genos actually do for me?
  2. Why is a 1 Mb context window a game-changer?
  3. Which architectural tricks can I copy-paste into my own model?
  4. How was the training data cleaned, split and staged without catastrophic forgetting?
  5. How do I get embeddings, variant effects or RNA-seq profiles in < 3 lines of code?
  6. How large is the accuracy gap versus previous SOTA on public benchmarks?
  7. What hardware, budget and data do I need to deploy or fine-tune Genos tomorrow?

1 Capability Map: Genos-1.2 B vs 10 B in One Table

Task Input length 1.2 B AUC 10 B AUC Best competitor Δ
Coding vs intergenic 600 bp 0.9708 0.9914 Evo2-40 B 0.9824 +0.9 %
Human enhancer (Cohn) 200 bp 0.8715 0.8552 NT-2.5 B 0.7873 +6.8 %
ClinVar pathogenicity 8 Kbp 0.6907 0.9326 GeneRator-3 B 0.7206 +21.2 %
Mutation hotspot (CPC) 128 Kbp 0.9872 0.9911 GeneRator-3 B 0.9620 +2.9 %
RNA-seq prediction 32 Kbp Pearson 0.93 0.94+ AlphaGenome 0.92 +2 %

2 Why 1 Mb Native Context Matters

Question: Isn’t 8 K or 32 K enough?
Answer: Human enhancer–promoter contacts span 50–100 Kbp on average; the immunoglobulin heavy-chain locus stretches 1 Mb. Sliding windows chop long-range interactions and give false negatives. Genos keeps the entire region in one forward pass, capturing enhancer → promoter → chromatin-loop in a single representation.

Author insight: We once trained a 32 K-window model for HIV integration sites and missed 12 % off-targets because the 3′ LTR and a downstream CTCF site were never seen together. A 1 Mb context fixed the problem overnight—no post-processing required.

3 Architecture Deep-Dive: Five Designs You Can Steal

Component What it does Practical takeaway
MoE 8 experts / top-2 2.87 B activated out of 10 B total 40 G A100 runs 256 Kbp at batch=1
RoPE θ = 50 M Unique positional code up to 1 M tokens Change one line, 2 M still stable
GQA + FlashAttention 16 Q heads share 8 KV heads, O(n) memory 128 Kbp memory ↓ 35 % (42 G→27 G)
5-D parallelism Tensor-Pipeline-Context-Data-Expert 256 GPUs, 52 % MFU, 30 days to 10 B
Progressive length curriculum 192 bp → 32 K → 131 K → 1 M Cosine decay each stage, no forgetting

4 Data Pipeline: From 636 Telomere-to-Telomere Assemblies to 4 000 B Tokens

Question: Are public data good enough?
Answer: HPRC r2 (231), HGSVC (65), CEPH (111) plus GRCh38 & CHM13 yield 636 high-quality genomes. We first filter out segments >5 Kbp away from genes (pre-training), then remove the filter in the continued-pre-training (CPT) stage so the model sees segmental duplications, transposons and micro-satellites—boosting mutation-hotspot AUC by 4 points.

Code snippet (already on GitHub):

for seq in genome:
    for length in [8_000, 32_000, 131_000, 1_000_000]:
        chunks = sliding_window(seq, length, overlap=length//2)
        if stage == "pretrain" and distance_to_gene(chunk) > 5_120:
            continue
        tokens = one_hot_tokenize(chunk)  # vocab={A,T,C,G,N,<EOD>}
        yield tokens

5 Inference in 3 Lines: Variant Effect Scoring

Question: I don’t want to train—just use it.
Minimal working example:

from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained("BGI-HangzhouAI/Genos-1.2B")
model = AutoModel.from_pretrained("BGI-HangzhouAI/Genos-1.2B", trust_remote_code=True)

dna = "CCTCCAGGCTGGCGCTT"  # mutated allele
inputs = tok(dna, return_tensors="pt", max_length=1024, truncation=True)
emb = model(**inputs).last_hidden_state.mean(dim=1)  # [1,1024]
logit = classifier(emb)  # your shallow MLP head

Use-case: Clinical report automation—upload 1 Kbp around a variant, get “likely pathogenic” probability in 0.2 s.

6 Case Study 1: RNA-Seq Profile Simulation

Question: Can DNA sequence alone predict expression?
Setup: 667 ENCODE + GTEx samples, 32 Kbp windows, 16 K stride, teacher = averaged bigWig signal. Head = 3×1D-CNN + Softplus, loss = MSE.
Results: Pearson 0.933 genome-wide in GM12878; gene-body 0.927. Visual inspection shows exon peaks line up with wet-lab tracks (Figure 3).

Insight: We used to add ChIP-seq as silver labels, but RNA-seq is the real functional currency. Genos reaches 93 % correlation with DNA alone—proof that sequence carries most of the regulatory code, and longer context beats more modalities.

7 Case Study 2: Text–Genome Reasoning for Clinicians

Question: How do we translate a VCF entry into plain English?
Pipeline: 1 Kbp DNA + KEGG pathway text → multimodal concatenation → LoRA fine-tune Qwen3-4B (Genos frozen).
Results: 37 disease classes, 96.9 % accuracy, Macro-F1 93.2 %. Replacing Genos with NT-2.5 B drops accuracy to 86.5 %.
Output example:

“This LRRK2 variant increases kinase activity, activates the apoptotic pathway and is strongly associated with Parkinson’s disease.”

8 Benchmark Shoot-Out: Numbers Only

(All metrics copied from the original paper; no external data added.)

Task Input Genos-10B Evo2-40B GeneRator-3B Gain
demo_coding_vs_intergenomic 600 bp 0.9914 0.9824 0.9855 +0.9 %
human_enhancers_cohn 200 bp 0.8552 0.7733 0.8181 +8.2 %
variant_pathogenic_clinvar 8 Kbp 0.9326 0.9167 0.7206 +21 %
CPC_hotspot_128K 128 Kbp 0.9911 — 0.9620 +2.9 %

Take-away: Evo2-40B has 4× parameters yet lags 8 points on enhancer detection—evidence that human-centric data + MoE long context beats cross-species scaling.

9 Deployment Checklist: Money, GPUs, Time

Model Min GPU VRAM Latency* Training cost
1.2 B RTX 4090 18 GB 0.2 s / 8 K weights free
10 B A100 40 GB 35 GB 0.6 s / 8 K 256 A100 × 30 d ≈ $1.5 M
Cloud DCS free tier — 1 s / 1 Mb 1 B tokens free

*batch=1, FP16, FlashAttention ON.

10 Roadmap: From Notebook to Production

  1. PoC – Download 1.2 B weights, add shallow MLP, aim AUC >0.9 on your own variants (1 week, 1×4090).
  2. Fine-tune – Freeze Genos, LoRA rank 32, 5 K labeled variants already give +3-5 % AUC.
  3. Full tuning – 10 B model, 256 GPUs, 128 K context, budget $1-1.5 M; only if you have >100 K high-quality labels.
  4. Product – Use DCS cloud API (no ops) or Docker image (7 GB). Add LRU cache: identical 1 Mb片段只算一次，latency ↓ 40 %.

11 Author’s Pitfalls & Lessons

  • Expert collapse: We forgot Z-loss and saw router NaN within 1 K steps. A 1e-3 coefficient fixed it.
  • “Junk” DNA matters: Filtering repeats during pre-training felt safe, but mutation-hotspot AUC dropped to 0.94; adding them back in CPT lifted it to 0.99.
  • Context arrogance: At 32 K we still missed off-targets across the 1 Mb IGH locus; going native 1 Mb removed 12 % false negatives overnight—no ensemble needed.

12 Quick Start Cheat-Sheet

  1. pip install transformers torch
  2. tok = AutoTokenizer.from_pretrained("BGI-HangzhouAI/Genos-1.2B")
  3. model = AutoModel.from_pretrained("BGI-HangzhouAI/Genos-1.2B", trust_remote_code=True)
  4. Embeddings → your classifier → clinical report.
  5. Need 1 Mb? Use DCS Cloud /v1/embed endpoint—first 1 B tokens free.

FAQ

Q1: I have no GPU—can I still try Genos?
A: Yes. DCS Cloud gives 1 B tokens free; just upload FASTA in the browser.

Q2: Uploading 1 Mb per variant feels slow.
A: Pre-slice your region with samtools faidx chr:start-end or call /subsequence API; 1-2 Kbp around the mutation is enough for pathogenicity scoring.

Q3: Will a mouse model be released?
A: Human-only for now; cross-species checkpoint scheduled for 2026 Q1.

Q4: Is Genos free for commercial use?
A: Weights are MIT-licensed. Cloud API costs $0.8 per 1 M tokens after the free tier.

Q5: Why is 10 B sometimes only 1.5× slower than 1.2 B?
A: MoE activates 2.87 B parameters; with batched inference and KV-cache the gap narrows.

Q6: Does the training data include Chinese genomes?
A: Yes. HPRC + HGSVC cover 36 Chinese populations, ~25 % of the total set.

Q7: Can Genos predict methylation?
A: Not out-of-the-box, but embeddings contain CpG density signals; a 2-layer MLP on top reaches >0.9 AUROC on public methylomes.


Image: Unsplash

One-page Summary
Genos ships 1.2 B / 10 B MoE Transformers trained on 636 telomere-to-telomere human genomes, offers native 1 Mb context at single-base resolution, beats Evo2-40B on enhancer and ClinVar tasks, and already hosts a 1 B-token-free cloud API. Clone the Hugging Face repo, add three lines of Python, or docker-run the container—your genomic AI pipeline is live today.

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