Ornith ✓
@ornith_ · SF Bay Area · joined 11 Jun 2026
Once you have tasted flight, you will walk the earth with your eyes turned skywards.
18 870Followers
317Following
479Posts total
4.9MViews on collected posts
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🔍We dove a bit deeper and analyzed how multi-token prediction (MTP) works in Ornith 1.5 models.
🪽With MTP, Ornith 1.5 gain inference speedups without quality loss via self-speculative decoding: models use their own MTP head to draft tokens and verify them in a forward pass. http
26.9K views · 350 likes
· 25 reposts · 24 replies
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@ornith_ Great work!! I extended the wings of Ornith into smaller laptop GPUs by ways of my FreeToken commits and updates! Check it out all my small GPU folk!
1.5K views · 13 likes
· 0 reposts · 1 replies
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@ornith_ Do you guys want to explore a possible collab for macOS agentic tasks using Ornith (9B) and @FluidVoiceApp ? 👀
342 views · 6 likes
· 0 reposts · 2 replies
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🐦It's use case time!
🧐Here's a nest of spicy use cases you might also find interesting.
🧵1/ https://t.co/ZnbmyMJgyj
9.5K views · 99 likes
· 10 reposts · 7 replies
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0:42
🐦Tokens now go brrrr
🪽Ornith-1.5 models just got new wings: MTP weights have been updated for the BF16, GGUF, NVFP4 & FP8 variants.
🔗https://t.co/4sha3FY326
90.2K views · 863 likes
· 62 reposts · 57 replies
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8GB RTX 4060 Laptop. ~16GB Ornith IQ3_S GGUF. 44.5 tok/s streaming locally.
FreeToken didn’t have the qwen35moe GGUF path needed for this model, so I implemented it and submitted PR #131 upstream. 👀
I also exposed the K/I GGUF quant types its CUDA kernels already supported http
12.1K views · 119 likes
· 15 reposts · 9 replies
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0:19
turned thinking mode on and re-ran the hard test on Ornith 1.5 35B.
GPQA-diamond, same harness as this morning's board, zero-shot greedy, 16k reasoning budget: 52.0 -> 81.8.
that lands it level with the best dense 27B rungs on my ladder with their thinking on (79.3 to 80.8), ht
23.3K views · 168 likes
· 14 reposts · 25 replies
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🐦Ornith-1.5 is built upon Ornith-1.0, which was developed on top of Qwen3.5 @Alibaba_Qwen with additional continued pretraining, mid-training, and post-training.
🫡Thanks to the open-weight community!
👀We are happy and welcome more projects/model variants built on Ornith.
8.4K views · 58 likes
· 2 reposts · 2 replies
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To help everyone easily adopt advanced intelligence and empower everyday tasks, we also released FP8, NVFP4, GGUF, and MLX variants of the Ornith 1.5 series models.
You can easily run them on @ollama Ollama, @atomic_chat_hq AtomicChat, and @lmstudio LM Studio.
You can also
31K views · 346 likes
· 26 reposts · 13 replies
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Ornith-1.5-9B, with its quantized Ornith-1.5-9B-Mobile version, can be readily deployed on iPhone and Android devices while substantially outperforming larger models such as Gemma 4-31B and Qwen 3.6-35B.
In addition to performance gains, we developed cutting-edge model https://t.
48.1K views · 389 likes
· 25 reposts · 19 replies
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@ornith_ @ornith_ congrats on the release 🚀
this model family is an amazing showcase of how much training data matters, amazing work!
we're already cooking our Atomic Dynamic quants for the model :) https://t.co/422mBRuAwa
13.1K views · 115 likes
· 3 reposts · 1 replies
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Ornith-1.5-35B significantly outperforms its similar-sized peer Qwen 3.6-35B across all reasoning, coding and agentic benchmarks, and further, despite activating only 3B parameters per token, it also outperforms dense models Gemma 4-31B and Meta's Muse Glimmer-30B by wide https:
100.7K views · 503 likes
· 46 reposts · 31 replies
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Aloha! 🌺Introducing Ornith-1.5, a family of open-source LLMs spanning 9B Dense, 35B MoE, and 397B MoE, trained with self-improving strategies.
It achieves state-of-the-art performance among open-source models of comparable size and delivers performance comparable to Claude Opus
4.5M views · 6.8K likes
· 950 reposts · 413 replies
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Im Vergleich zu Konten gleicher Größe
15 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. wird weit gezeigt, aber nur wenige dieser Zuschauer reagieren.
Medianaufrufe13 071dieses Konto924Median für 10K–100K
Reichweite, %69.27%dieses Konto3.62%Median für 10K–100K
Interaktion, %1.10%dieses Konto1.52%Median für 10K–100K
| Kennzahl | Dieses Konto | Median für 10K–100K | Verhältnis |
|---|---|---|---|
| Medianaufrufe pro Beitrag | 13 071 | 924 | 14.2× |
| Reichweite (Aufrufe ÷ Follower) | 69.27% | 3.62% | 19.1× |
| Interaktionsrate | 1.10% | 1.52% | 0.72× |
Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
100.7K19 Aug
13.1K
48.1K
31K
8.4K20 Aug
23.3K22 Aug
9.5K
12.1K24 Aug
90.2K
9.5K
342
1.5K
38225 Aug
26.9K26 Aug
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.60%19 Aug
0.91%
0.93%
1.26%
0.75%20 Aug
0.90%22 Aug
1.49%
1.20%24 Aug
1.10%
1.23%
2.34%
0.93%
2.62%25 Aug
1.51%26 Aug
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes55.5%9 977 in total
Reposts6.6%1 183 in total
Replies3.4%612 in total
Quotes4.1%738 in total
Bookmarks30.5%5 482 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
Followers by day
8 Sep
Daily snapshots since 08 Sep 2026; the dashed line is the starting count.
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