DEV Community: SparkLLM

A 4B model writes Python. Blender builds a bowl. What happens next?

SparkLLM — Sat, 07 Nov 2026 15:40:36 +0000

Ask for a feeding bowl. A small model writes Python. Blender runs it, and a bowl-shaped mesh appears.

Community builder hotocooo has full-fine-tuned Spark-X2.6-4B-Base for that loop: an English description becomes a Blender bpy script. The interesting part is what happens after the code runs—and the author has shared both the objects and the misses.

Four task groups, four different results

The public checkpoint is v3, a full-parameter fine-tune of all 4.1B weights. In the author's tests, the scripts executed and built geometry in 100/100 BlendNet, 60/60 anatomy, 60/60 spatial assembly, and 20/50 3DCode cases. These are separate task suites, not a combined accuracy score.

The author used Blender 5.2.1 LTS headless, greedy decoding and one attempt per displayed held-out example. The model card reports Apple M4 Max / 64 GB unified memory. Base/v1 generation used Transformers bf16/MPS, while later stages used llama.cpp with f16 GGUF, so we do not turn the base-to-v3 table into a controlled speed comparison. SparkLLM has not independently rerun these tests.

A bowl and a sandwich

The author asks for a single-compartment feeding bowl, then a sandwich with bread and filling. These are the generated scripts rendered in Blender, shown from two views. Both selected examples have reference fidelity 1.000. That metric checks size, proportions and part count; it is not a photorealism score.

Tables and shelves with specified dimensions

The spatial suite asks for dimensions, placement and part counts. The author reports 60/60 scripts building geometry; these table and shelf examples each have reference fidelity 1.000. They show basic assemblies, not finished production assets.

Doors from code

The two selected 3DCode successes have shape F-scores of 1.000 and 0.996. The whole suite is harder: only 20/50 scripts execute and build geometry. These successful examples do not represent the full set.

A starting point for sculpting

The anatomy suite reports 60/60 scripts building geometry. These cat and human examples are base meshes. Fine faces, muscles and finished sculpting are outside the demonstrated result.

The prompt can survive while the details disappear

The author also publishes weaker BlendNet outputs: reference fidelity 0.219 for the logo and 0.393 for the bottle. A script can run without producing what the user intended.

Harder objects expose the gap

These 3DCode examples have F-scores of 0.000 and 0.094. Empty or poor renders are part of the source evidence, not missing illustration assets. They belong beside the successes.

Anatomy still needs inspection

The author ranks these among the weaker anatomy examples, with reference fidelity 0.920 each. High structural scores still leave a human review step.

Spatial assemblies are not automatically perfect

The weaker grid examples have reference fidelity 0.756 and 0.862. Correctly executing code and satisfying all geometric requirements remain separate goals.

Why this is an interesting community build

The training pipeline checks generated scripts in Blender rather than trusting code that merely looks plausible. The author reports 11,653 of 12,059 raw BlendNet rows passing the Blender check, split into 11,353 training rows and 300 held-out rows; the displayed BlendNet evaluation uses the first 100 held-out rows. Rejection sampling generates multiple candidates and keeps winners that meet topology and fidelity conditions.

The results also show the limits of specialization. The model card calls open-world objects weak; v3 has 0% exact success on the listed extreme task metric and 0% hard-constraint success. General-text perplexity rises from 9.73 to 10.44 in the author's v3 comparison. This is a focused experiment in code-driven base meshes, not a universal 3D artist.

Try a small object of your own

Start with a simple bowl, stool or shelf using the author's prompt format and setup. This is a completion model, not a chat model; the card specifies Transformers 4.57.1 and Blender 5.x. The output is Python that Blender must execute. Use an isolated Blender environment without unsaved work.

What would you ask it to build—and which detail would you inspect first?

Credit and reproducibility: community model, original renders and reported results by hotocooo; base model by the SparkLLM team. Source snapshot: 061fec4c9def10e8b76a5973b50d364815fa6225. Original renders are preserved inside the new layouts; chart and layout by SparkLLM. Source license: Apache-2.0. This article contains one data chart and 16 paired example panels showing all 32 published evaluation renders; the separate anatomy training image is intentionally not presented as an evaluation result.

A browser tab, a local AI: Spark-X2.6-1.7B on WebGPU

SparkLLM — Fri, 06 Nov 2026 07:55:03 +0000

Your next local AI setup could start with a browser tab.

Community developer Mohd Mahmodi converted Spark-X2.6-1.7B into a 4-bit ONNX build that runs inference in the browser through WebGPU. The first visit downloads about 1.21 GB, which the browser caches for later visits.

The numbers below are the author's measurements, not a new SparkLLM benchmark run: Windows 11, desktop Chrome, NVIDIA RTX 3090 Ti, one request at a time; benchmark quality checks use thinking off. The 49 ms first-token result is for a 28-token prompt, not a long document.

A longer conversation does not have to mean slower decoding

The author's original speed sweep records 108, 104, 107, 109 and 107 tokens/s at prompt lengths of 28, 1k, 4k, 8k and 16k tokens. First-token latency does increase: from 49 ms at 28 tokens to 5.9 seconds at 16k. Decoding speed and waiting for the first token are different measurements.

The included loader keeps its KV cache on the GPU and reuses the cached prefix for follow-up chat turns. That is a useful implementation detail for developers building a local browser assistant.

What survives the conversion?

With thinking off and the same prompts and greedy decoding, the author reports:

Check	Official BF16 in Transformers	Official INT4 in Transformers	Browser build
GSM8K, all 1,319 problems	75.7%	72.7%	72.7%
BFCL v3 simple function calling, 200 cases	76.5%	52.5%	52.5%
Needle retrieval, 12 cases at 1k–16k tokens	12/12	12/12	12/12

The browser build matches the official INT4 scores on these checks, not the BF16 model. The function-calling gap remains, and these limited checks are not a claim of identical outputs or universal accuracy. The author also reports a 30-minute stability run with 877 generations and no errors.

Open the demo, then build your own

Try the live browser demo in desktop Chrome or Edge. Allow time for the first model download. The repository also includes a JavaScript loader, thinking controls, multi-turn chat and tool-call parsing.

Only Windows 11/Chrome/RTX 3090 Ti was tested by the author; phones, integrated GPUs and other platforms are not validated by this report. CPU/WASM is not supported by the app. Start with one tab: the author reports that two tabs in one Chrome instance can exhaust D3D12 descriptor heaps.

What would you put in a local browser assistant: private notes, a coding scratchpad, or a small document tool?

Credit: Mohd Mahmodi. Original images reproduced unchanged from the Apache-2.0 repository. Source README · Validation report · License. Source snapshot: 1a2cedace485d8bdae557729adaa8f34ea05eed1.

Looks finished. Does it work? Six Spark X2.6 4B demos put to the test

SparkLLM — Wed, 04 Nov 2026 09:25:50 +0000

The page looks great. Then you press the button.

Does the candy game update both the score and the remaining moves? Does Pause actually stop the timer? What happens when you finish a checklist, undo one task, and reset it?

A screenshot cannot answer those questions. A walkthrough can.

In this English video, follow Spark and the little purple flame through six interactive webpages. Tap matching candies, switch a profile card between light and dark, pause a timer, change a gradient, answer a quiz, and check off a daily list. Each small action gives you something concrete to inspect.

The interesting question is what happens after the page appears. Do the controls and the state agree? Can you get back to a clean start?

Your turn to press the button

Open the Spark X2.6 4B Playground, pick an idea in Try this, and load its prompt. Generate a webpage, open the preview, and try the important actions.

Which would you make first: a tiny game or a tool you would use every day?

Explore Spark X2.6 4B · Project

An AI flagged the hiring clause. Its creators published the failures too.

SparkLLM — Wed, 04 Nov 2026 08:41:52 +0000

"Before you join, promise not to have a child for two years."

That condition appears in a synthetic hiring example submitted to HER Hack-Astron #7. Recruitment Fairness Lens locates the wording, quotes it, and hands it to a human reviewer.

The engineering and legal-research team won this edition. They also published the system's mistakes—including a choir's “soprano” requirement misread as a sex restriction.

On 30 synthetic cases, agreement with one researcher's reference was 15/30. This is reference agreement, not legal accuracy or a measure of real-world impact. The interesting design choice is the review process: find a cue, keep the evidence, and let a person accept, correct or reject it.

Watch the complete English showcase (1080p MP4)

Seven more ways to make information easier to act on

A difficult insurance letter. An unverified rural report. A community notice with a date that is still provisional. These entries turn everyday information problems into concrete prototypes.

Rural Signal keeps the source and unverified status visible. TramiteClaro local explores plain-language instructions, local read-aloud and photo input. PlainLetter pairs deadlines with quotations. Notice Cards falls back to original text when a rewrite cannot be confirmed. Notice Reader adds larger text and source comparison. PlainBridge keeps unknown details explicit. NoticeClear shows both a successful draft and an omission: the missing statement that the community would provide garbage bags.

The demos and small development tests show specific workflows, not proven benefits for real users. Their failures are part of the story.

Explore the submissions

Event and contributor entries.

Spark on your phone: 9 mobile app cases with sources and setup limits

SparkLLM — Tue, 03 Nov 2026 10:37:55 +0000

Spark on your phone: apps, real examples and what the evidence shows

Spark-X2.5 and Spark-X2.6 are appearing in mobile apps across Android and iOS. We collected nine examples, with model versions, original sources, download entry points and practical limits, in the Spark mobile apps Wiki.

PocketPal: local models with optional web-search tools

PocketPal’s Android and iOS device rules include Spark-X2.5 4B and 1.7B GGUF models. Its published web-search evaluation reports 0.89 and 0.87 fresh-fact accuracy, respectively, using Q4_K_M files of about 2.6 GB and 1.1 GB. Both appear on the accuracy-versus-file-size frontier.

The study covers 80 fresh facts with 399 phrasing variants. These are workstation runs of the phone GGUF files with Brave search, reasoning disabled and a five-turn budget. The 90% confidence intervals overlap; the chart does not establish a statistically clear winner. Phone latency, battery use and heat were not measured. Download size is not runtime RAM.

Original evaluation and methodology

Android: TokenSpark and LlamaPal

The official TokenSpark Android entry is explicitly an X2.5 trial, even though the website’s main banner features X2.6. It lists Android 8.0 or later; install the app, then download a 1.7B or 4B model separately for local chat, writing and problem-solving.

TokenSpark Android entry

LlamaPal developer Vishist Varugeese announced Spark-X2.5 4B for the Android app on October 30, 2026 at 17:07:05 Beijing time. The original image below is the creator’s announcement: its resource and speed figures are creator claims without a reproducible device setup here. We have not independently benchmarked them; words/second and tokens/second are different units.

Original announcement

iPhone: a local Canvas example

Local LLM’s creator Tony demonstrated Spark-X2.6 4B on an iPhone with a small Canvas interaction. The post was published October 26, 2026 at 01:56:12 Beijing time. Its App Store history also records Spark X2.6 support. We link the original demonstration without adopting its unverified speedup claim.

Watch the creator’s original demo

More entry points to explore

The Wiki also covers AIでチャット, LocalEngine, Leo00703’s Android Local-LLM, Weixuan and Apex Creators’ LocalAI. AIでチャット’s current official listing includes both Spark generations and optional DSpark draft support; experimental acceleration requires matching models and may not speed up every configuration. The other entries are labelled by their evidence: store release notes, project support lists or app-distribution statements.

We have reviewed public evidence, not independently installed and benchmarked all nine apps. Local chat after model download is distinct from optional network tools. Features supplied by other vision or audio models should not be attributed to Spark’s text models.

Open the full Wiki, original images and setup links. If you try an app, share its version, your device, model and quantization, and the task you tested.

Spark-X2.6-4B in two live web assistants: share what you are building

SparkLLM — Mon, 02 Nov 2026 12:31:36 +0000

Full case on Hugging Face: https://huggingface.co/XHToken/Spark-X2.6-4B/discussions/29

Spark-X2.6-4B is running in two live web assistants. These short recordings show English questions, actual model replies, and the thinking-level controls available to visitors.

Try the apps, then share a demo of your own Spark-X2.6 application in the comments. Longer case write-ups are welcome too—post them on Hugging Face and drop the link here.

Astron Harness Tutorial: Learning Assistant

Open the learning assistant · Source repository

This bilingual tutorial assistant helps visitors explore AStudio, Loomy and AstronClaw. It uses relevant tutorial material as context for the conversation.

Question: “Plan mode vs Goal mode in AStudio? Two short sentences, no Markdown.”

The recording shows the assistant explaining when to review a plan before execution and when to use Goal mode for continued progress. The interface offers None, Low and High thinking levels and remembers the visitor's selection.

Full-length recording

Awesome Astron Workflow: AI Chat

Open AI Chat

This assistant answers questions about Astron projects and capabilities using the site's reference material.

Question: “How can SkillHub help a team reuse AI skills? Give 3 benefits in under 60 words.”

The recorded reply discusses skill distribution, secure sharing and discoverability. Both the question and the answer remain as they appeared in the application.

Full-length recording

Integration details

Both applications call spark-x2.6-4b through iFLYTEK Astron MaaS, with credentials kept on the server. The browser sends the conversation and selected thinking level to the application's backend. These demos use Low.

The server maps the controls to the model request:

Selection	Request behavior
None	Disable extra thinking
Low	Enable thinking with low reasoning effort
High	Enable thinking with high reasoning effort

The tutorial assistant supports streaming replies and stopping an answer. The clips show the website Q&A experience: typing a question, waiting for the model and reading the response.

About the recordings

These are real website recordings, and the responses have not been rewritten or simulated. The AI Chat GIF shortens pure waiting time and labels that edit. The full-length MP4 links retain the complete captures. These clips illustrate application integration; they are not latency benchmarks.

Share your Spark-X2.6 case

What are you building with Spark-X2.6? Share a working demo or screenshot in the comments. For a longer Hugging Face write-up, useful details include your use case, model variant, deployment stack, prompt, thinking level, integration approach and what you learned.

Spark-X2.6 model collection

769 tok/s on a Single RTX 4090: Spark-X2.6-1.7B DSpark

SparkLLM — Sun, 01 Nov 2026 13:39:52 +0000

769 tokens per second on a single NVIDIA RTX 4090. Spark-X2.6-1.7B with DSpark reaches that peak in the published GSM8K benchmark using a Q4_K_M target with llama.cpp, up from 424 tok/s without DSpark.

For BF16, the highest reported speedup is 2.43x, also on GSM8K and RTX 4090: 220 → 536 tok/s. The throughput peak and speedup peak use different target precisions.

❤️ Give the model a Like and download the weights: Hugging Face · ModelScope.

A 345M-parameter BF16 draft model proposes up to seven tokens per pass. Spark-X2.6-1.7B verifies those candidates in parallel, reducing sequential target decoding steps. The draft uses five sliding-window attention layers and is provided in BF16 GGUF format. Pair it with the Spark-X2.6-1.7B target model, using BF16 or quantized target weights.

Across AIME25, GSM8K, MATH500, HumanEval, MBPP and Arena-Hard-v2, the model card reports:

Target / device / runtime	Average speedup	Average throughput, without → with DSpark
BF16 · RTX 4090 · llama.cpp	2.10x	220 → 463 tok/s
BF16 · M5 Pro · MLX	1.88x	68 → 128 tok/s
Q4_K_M · RTX 4090 · llama.cpp	1.53x	424 → 650 tok/s
INT4 · M5 Pro · MLX	1.20x	138 → 165 tok/s

The acceptance-length evaluation uses seven draft tokens and a maximum output length of 4,096 tokens. Average acceptance lengths range from 3.5763 to 4.3158 across the four reported sampling/thinking configurations. Acceptance length includes an additional target token, giving a maximum of eight.

These are the published model-card results. Performance depends on the device, target precision and workload; the poster and model card show the full tables.

To run it locally, follow the model card setup for XHToken/llama.cpp. The documented commands target Linux, Bash and NVIDIA GPUs. Integration into upstream llama.cpp is in progress; the card reports MLX performance separately. Spark-X2.6 uses the Apache 2.0 license.

Like the model. Download the magic. Give it a Like on Hugging Face and ModelScope, download the draft and target weights, and share your device, runtime, precision and measured results with the community.

Download the full-resolution English poster — 7920 × 15400 PNG

Spark-X2.6-4B DSpark Is Here: More Than 2x Average BF16 Inference Speed

SparkLLM — Sat, 31 Oct 2026 06:58:28 +0000

DSpark for Spark-X2.6-4B is now available. Faster inference. More magic. 🪄

The published benchmarks show more than double the average BF16 inference throughput: 2.14x on NVIDIA RTX 4090 with llama.cpp and 2.50x on Apple M5 Pro with MLX, across the six reported datasets.

Download on Hugging Face · Download on ModelScope

A small draft model, fewer sequential decoding steps

DSpark is a 624M-parameter BF16 draft model for speculative decoding with Spark-X2.6-4B. The draft proposes up to seven tokens per pass, and the target model verifies them in parallel. This reduces the number of sequential target decoding steps.

This release provides the draft in BF16 GGUF format, paired with Spark-X2.6-4B-GGUF. The target can use BF16 or quantized GGUF weights. The draft complements the target model and is not a standalone chat model.

What the benchmarks show

Target / device / runtime	Average speedup	Average throughput, without → with DSpark
BF16 · RTX 4090 · llama.cpp	2.14x	95 → 203 tok/s
BF16 · M5 Pro · MLX	2.50x	31 → 77 tok/s
Q4_K_M · RTX 4090 · llama.cpp	1.98x	226 → 447 tok/s
INT4 · M5 Pro · MLX	1.62x	71 → 115 tok/s

The peak reported speedup is 2.94x on MATH500 with BF16 on M5 Pro. The peak reported throughput is 532 tok/s on GSM8K with Q4_K_M on RTX 4090. These are different test configurations.

The six datasets are AIME25, GSM8K, MATH500, HumanEval, MBPP and Arena-Hard-v2. Results depend on hardware, precision and workload. These figures come from the published model card; they are not a new independent benchmark.

The acceptance-length evaluation uses seven draft tokens and a maximum output length of 4,096 tokens. Its average acceptance lengths range from 4.0236 to 4.5522 across the four reported configurations. Acceptance length includes one additional target token, so the maximum is eight.

Try it locally

Follow the model card's setup instructions for XHToken/llama.cpp. The documented commands target Linux, Bash and NVIDIA GPUs. Integration into upstream llama.cpp is in progress. The model card reports MLX benchmarks separately.

The Spark-X2.6 model series is released under Apache 2.0.

Like the model. Download the magic.

❤️ Give the model a Like, download the weights and try DSpark on your own workload:

Share your device, runtime, target precision and results with the community. We would love to see what you build.

Full-resolution poster

🎃 3.5M+ Downloads: Create Your Own Magic with Spark Open Models 🪄

SparkLLM — Fri, 30 Oct 2026 11:49:09 +0000

🎃 3.5 million downloads. One magical community. ✨

Spark X2.5 and Spark X2.6 have passed 3.5 million combined downloads across tracked official model channels and community derivatives. Thank you for every download, experiment, quantization, fine-tune and contribution. ❤️🛠️

This Halloween, pick up a little open-model magic and make something of your own. 🧙‍♀️🪄

✨ The milestone, in numbers

Our October 30, 2026 snapshot records:

🎉 Approximately 3,626,852 combined downloads and pulls.
🤗 Approximately 1,540,498 through tracked official model channels.
🛠️ 2,086,354 downloads of community derivatives.
🌟 204 community derivative models: 114 for X2.5 and 90 for X2.6.

These are cumulative model distribution counts, not unique people or installations. They include Ollama pulls and approximate counters. Platforms use different counting methods, so the aggregate is a reference figure. Community models are counted once per repository. 📊

🪄 Three ideas for a Halloween build

These are ideas to try, rather than claims about an existing demo:

👻 A spooky story bot: give it a setting, a character and a twist; turn the result into a tiny interactive adventure.

🎃 A pumpkin-game NPC: prototype a character that responds to player choices, then test how it handles your game's rules.

🍬 A candy-themed coding helper: build a small local playground for explaining snippets or exploring ideas, and review its outputs as you would any model-generated code.

Start with a task you can actually test. Tell the community which model, runtime and settings you used, and share what worked as well as what needs improvement. 🔬✨

🤗 Open your spellbook

Explore the Spark X2.6 1.7B and 4B models and choose the available format that fits your runtime:

If you enjoy the models, leave a ❤️ Like, share your demo, or tell another builder about them. Your experiments help this community grow.

Create your own magic with Spark open-weight models. 🪄✨

Happy Halloween, builders! 🎃👻🍬

Download the full-resolution milestone poster

4B, 94.6/100: Spark-X2.6 shines in a community NanoBench agent run

SparkLLM — Fri, 30 Oct 2026 03:26:40 +0000

A 4B model is worth a closer look when a community agent run scores 94.6/100. Hugging Face community member userr99 shared a NanoBench result for our Spark-X2.6-4B-GGUF, using Hermes Agent as the harness. Thank you for putting our model to the test.

What the community reported

Field	Reported result
Model	XHToken/Spark-X2.6-4B-GGUF
NanoBench score	94.6 / 100
Harness	Hermes Agent
Tasks solved	9 / 12
Scored time	1h 8m
Run completed	October 29, 2026

Read the original community report · NanoBench site

Why this is exciting for a compact model

The shared screenshot shows Spark at 94.6, just 0.4 points below the listed 35B-A3B variant at 95.0. That is an encouraging score for a 4B model intended for on-device use. The community author also compares it with larger models; the screenshot should be read as the record of this particular submission.

Our practical strengths go beyond one score:

A compact 4B model for local agents. Spark-X2.6-4B targets on-device deployment and workflows involving tools, planning, and code.
GGUF distribution and documented local deployment paths. The model card covers llama.cpp, Ollama, and LM Studio, making it easier to choose a familiar local runtime. Follow the current setup instructions for the supported build and template.
Apache-2.0 licensing. The model is available under a permissive open-source license.
Configurable thinking budget. The model offers none, low, and high thinking levels so applications can tune reasoning effort.

These are reasons to try Spark on your own tasks, especially when you want a compact model you can run locally.

How to read this result

This is a community-reported run, not an independently reproduced evaluation by our team. NanoBench states in the screenshot that scores are client-reported and that its service checks result structure and score arithmetic, but cannot independently prove the local verifier ran. Spark solved 9/12 tasks; the 35B-A3B entry shown solved 11/12, and the listed harnesses differ. A close score here does not establish equivalent performance across workloads. The report does not identify the tested quantization or hardware, so we are not claiming a memory, speed, or cost advantage from this run.

Try it — and support open models with a Like

Download the GGUF model and read the deployment guide.
Test it with your own agent setup, then share your runtime, quantization, hardware, and task results in the community.
If you like this direction, please tap ❤️ Like on the original Spark-X2.6-4B model page. Your support helps more developers discover our work and encourages us to keep building in the open.

Like this post and share it with someone building local agents. Thank you for supporting SparkLLM!

Spark-X2.6-1.7B Is Here: Stronger Coding and Agents in a Compact Model

SparkLLM — Tue, 27 Oct 2026 15:06:51 +0000

Spark-X2.6-1.7B is here: a compact model for on-device AI, following Spark-X2.6-4B. If you find it useful, please give the model a Like on Hugging Face and share your feedback.

What's new

The model keeps the architecture of Spark-X2.5-1.7B and improves its capabilities through post-training. The official model card describes gains in coding, agent tasks, long-context reasoning, multilingual tasks and reasoning, with three configurable thinking budgets: none, low and high. The model is licensed under Apache 2.0.

Selected results

The official benchmark table reports these results for X2.5-1.7B → X2.6-1.7B:

Benchmark	X2.5-1.7B	X2.6-1.7B
SWE Verified	28.3	59.8
SWE Multilingual	23.3	57.3
τ²-bench	65.3	85.3

These are model-team evaluations. The table uses thinking mode, with X2.6 evaluated at high. Asterisks in the full chart identify externally reported scores; SWebBench and PAgentBench are in-house benchmarks. See the retained notes for sampling settings and evaluation details.

Local inference

The second figure separates prefill and decode throughput at 4K, 32K and 128K context lengths. Its Mac M5 Pro 16-core group uses MLX INT4 weights and BF16 KV cache. Its RTX 4090 group uses llama.cpp Q4_K_M weights and FP16 KV cache, with an additional Spark variant using the team's optimized llama.cpp implementation. The software versions and quantization details are in the figure. Results depend on the tested configuration.

Choose your model and runtime

Hugging Face lists the post-trained model, Base, GGUF, INT8, INT4 and FP8 repositories. The supplied ModelScope screenshot shows the post-trained, Base, INT8, INT4 and FP8 options. Choose the format supported by your runtime.

Run with Ollama:

ollama run SparkLLM/Spark-X2.6-1.7B

Four launch images

In order: benchmarks, inference throughput, Hugging Face model list, ModelScope model list. The platform screenshots are launch snapshots; counters and relative timestamps can change.

Try it on a real task, tell us your hardware and runtime, and share what you build. Thank you for supporting open models!

Thank You for the Spark-X2.6-4B Community Feedback — Let’s Build for Good

SparkLLM — Mon, 26 Oct 2026 15:32:41 +0000

Thank you to everyone trying Spark-X2.6-4B, sharing feedback, and building with it. These three community snapshots capture what we appreciate most: people putting the model to work and sharing what they learn.

What the community is sharing

On Hugging Face, risermaker praised the GGUF model and said it felt comparable to much larger models in their experience.

In the supplied r/LocalLLaMA screenshot, u/MeinDruckerSpinnt described using the model in pi CLI for a Blender Python task, understanding a Rust codebase, and finding and following a global agents.md file. They also shared positive impressions of speed and tool calling. These are individual reports from particular setups, rather than results from a controlled comparison.

The third screenshot shows community project discussions, including Aegis-Her, Rural Signal, TramiteClaro local, Agentic Fashion, and a community-notice reading prototype. Thank you to everyone turning ideas into applications.

Try it, leave a Like, and build for good

If you find the model useful, please give Spark-X2.6-4B a Like on Hugging Face. Your feedback helps us understand what works and what needs improvement. When sharing a test, include your runtime, quantization, hardware, settings, and a reproducible example.

You can also join HER Hack-Astron #7: Open Source AI for Good. Build a working public-interest application using Spark-X2.6-4B, show it in a video or illustrated demo, publish the complete entry in the model's Community Discussions, then submit the Discussion link in the event issue.

The deadline is October 31, 2026, 23:59 UTC (November 1, 07:59 Beijing time). Individuals of any gender are welcome; teams must meet the requirement of at least 50% women contributors. AI-assisted development must be disclosed. See the event issue for the full participation and judging rules.

Thank you for the recognition, thoughtful testing, and practical ideas. We look forward to seeing what you build next.