Tylerwtzhang

I am working for @SparkLLM.

[Tylerwtzhang @Tylerwtzhang]

@0xChrild 嗨，可以了解在您具体的脚手架和任务类型吗？

[Mirrorize AI @mirrorizeai]

Proof that the future of incredible AI performance will live in a small model. @karpathy was right. I didn't believe it until I tried Spark.

[Tylerwtzhang @Tylerwtzhang]

😃

[Thiago Costa Lucena @uglesado]

@Tylerwtzhang @vaiduakhu @jaro3th @noctus91 Just tried a few and WOW. It seems like a qwen3.6-9b we never got. Its more efficient than 9b, wastes less token and get more things done. Its perfect por personal assistant like hermes. Currently getting 82tks median with a rx 9070xt and using it at q8.

[Tylerwtzhang @Tylerwtzhang]

@uglesado @vaiduakhu @jaro3th @noctus91 pls try spark-x2.6, it's really good at its size

[Tylerwtzhang @Tylerwtzhang]

sota on-device model in 2B class👉

[SparkLLM @SparkLLM]

Spark-X2.6-1.7B: less than half the parameters of our 4B, close on two benchmarks:
SWE Verified: 59.8 vs 62.8
τ²-bench: 85.3 vs 86.9
Both at high thinking.
Try it & Like on HF!
https://t.co/kjB7OxCIZU
@huggingface @Ollama @ModelScope2022

[Tylerwtzhang @Tylerwtzhang]

@SpawnLauv Spark-X2.6-4B supports 1M context, you don't need compaction when 16k

[SparkLLM @SparkLLM]

🎉 Spark-X2.6-4B reached #4 on Hugging Face Trending!
Snapshot: All · last 7 days, Oct 25, 2026.

Thanks for your support, hands-on reports & constructive feedback. If you find it useful, give it a ❤️ Like:
https://t.co/5GdI4SGeTo

[OpenBMB @OpenBMB]

When should the cloud execute for the edge agent, and when should it just advise?

We present a systematic study of edge-cloud collaboration in long-horizon agent execution. Using EC²-Bench, we evaluate four collaboration strategies across 128 executable tasks, jointly measuring task completion, resource cost, and privacy exposure.
Here are three key findings:

1️⃣ Small cloud budget, big gains.(cost-efficiency). With Qwen3.5-9B on-device and GLM-5.3 in the cloud, edge-only scores 39.12; the best collaboration reaches 60.46 — about 73% of cloud-only (82.70) — at only $8.73, roughly 15% of cloud-only's $56.42.
2️⃣ Performance converges, privacy doesn't. On Qwen3.7-Plus, best collaboration hits 76.84 vs 82.70 cloud-only; strategy gaps shrink to just 3.67 completion points, yet privacy exposure spans 66.23 percentage points — and the best-performing strategy leaks the most.
3️⃣ The mode flips with edge capability. Weak edges do best with an external router that lets the cloud execute (2B: Per-step Routing 39.97 vs self-delegation 15.86); stronger edges do best when the cloud only advises (9B Cloud Advisory 60.46).
Further experiments highlight two abilities that matter for effective self-directed collaboration: knowing when to ask for help, and actually acting on the cloud's advice.

📄 Paper: https://t.co/ESc7nA1Aeu
🌐 Project: https://t.co/J1A3ZjtgCj
💻 Code: https://t.co/xjAuYnCDT1

#EdgeCloudCollaboration #AIAgent #LLM #OnDeviceAI #AgentBenchmark #EC2Bench #OpenBMB #Tsinghua

[Tylerwtzhang @Tylerwtzhang]

🚀 Spark-X2.6-4B is now #5 on Hugging Face’s model trending list! @huggingface

Thanks for all the support! 🤗 Give it a try and let us know what you think.

👉 Try https://t.co/5Eu9C44XN1

#Spark #HuggingFace #OpenSourceAI

[Tylerwtzhang @Tylerwtzhang]

thanks. keep going…

[Dante Martin @DanteMMartin]

A 4B model is matching 9B models on several benchmarks. Spark-X2.6-4B: same arch as X2.5, gains all from post-training — coding, agents, 1M context, on-device. The small-model curve is steep. https://t.co/RndcMSt4uP

[Tylerwtzhang @Tylerwtzhang]

@waltonoemi it overthinks sometimes. we provide two thinking modes : high and low. pls try the low mode, maybe it's the one for you.

[Tylerwtzhang @Tylerwtzhang]

@paulgug yeah it performs better with thinking mode. but may have some overthinking problems. pls have a try.

[Tylerwtzhang @Tylerwtzhang]

@waltonoemi @SparkLLM @huggingface @ModelScope2022 @ollama @vllm_project @sgl_project could you pls try the low thinking mode ?

[Benz ON ⚙️ @Benz_onttv]

@SparkLLM @huggingface @ModelScope2022 @ollama @vllm_project @sgl_project 🚨 Update🚨
We may have the best <16GB VRAM model over here. Impressive results and impressive speed.
⚠️The benchmark is about RAW performance. Thinking OFF, and MTP OFF.

Spark had difficult time with Terminal-Bench 2.0 (samples)

But Overall TOP #1 quality/speed for sure.

[Tylerwtzhang @Tylerwtzhang]

@Ash_Eriga thanks bro!

[Tylerwtzhang @Tylerwtzhang]

Please tell us the performance on your own benchmarks

[David Hendrickson @TeksEdge]

@NayteThaGreat Yeah going to test it myself on my own benchmarks. So far no small model has been able to beat Qwen3.5-9B. I’m getting ~70 tps so far on this new model

[Tylerwtzhang @Tylerwtzhang]

@TeksEdge @NayteThaGreat Please tell us the performance on your own benchmarks.

[Tylerwtzhang @Tylerwtzhang]

@PaXError @ModelScope2022 @SparkLLM on the way!

[Tylerwtzhang @Tylerwtzhang]

nice work!👉

[Chouaib | Développeur d'Apps @Dz_Civ_Eng]

@SparkLLM @huggingface @ModelScope2022 @ollama @vllm_project @sgl_project Wow made with Spark-X2.6-4B in less than 1 minute using 12k token only